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The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation. Plain Language Summary: Many properties of the atmosphere are affected by the temperature of the ocean surface. Knowing how strong these effects are would help us to better predict global warming. The response to a given surface warming depends on where the warming occurs. To account for this, researchers sometimes simulate the response to individual patches of warming and then assume the response to an arbitrary warming pattern can be summed together from these patch responses. This is sometimes called the atmospheric Green's function method, and it works well at recreating the atmospheric response to historical temperature changes. We are organizing a Green's Function Model Intercomparison Project (GFMIP), in which participants will apply the method consistently for many climate models. This paper presents the GFMIP protocol. In the course of developing this protocol, we found that the atmospheric response to warming is not proportional in all cases: the response to surface warming is not the opposite of the response to surface cooling; warming twice as much doesn't cause twice as much of a response; and making a patch of warming twice as large doesn't cause twice as large a response. GFMIP will help us figure out how to account for this nonlinearity. Key Points: The Green's Function Model Intercomparison Project (GFMIP) explores the atmospheric response to surface temperature patch perturbationsThis paper presents the GFMIP protocol, which was generated using insights from past studies and new sensitivity testsGreen's functions reconstruct the response to historical temperatures, but nonlinearities can affect responses to other warming patterns [ABSTRACT FROM AUTHOR]

Understanding historical soil moisture (SM) variations and their relationship with temperature in high‐latitude humid regions is essential for predicting hot droughts under widespread warming. This paper presents the first‐ever annual‐resolution summer surface SM reconstruction (1736–2006 CE) in Sweden, located in northern Europe (NE). The reconstruction utilizes the paleoclimate proxy, tree‐ring δ18O, which exhibits a strong correlation with reanalysis SM data during 1948–2007 CE (r = −0.67, p < 0.001). In contrast to an unprecedented trend suggested by previous studies, our reconstruction exhibits a wetting trend in NE since the 1960s falling within the historical spectrum of trends. Both reanalysis SM data and our reconstruction show significant negative correlations with temperature at interannual timescales. However, Coupled Model Intercomparison Project Phase 6 climate models failed to capture the significant SM‐temperature relationship during the historical (1850–1947 CE) and observation periods (1948–2006 CE). This proxy‐model comparison represents a critical step toward enhancing our understanding of SM dynamics and the reliability of SM projections in high‐latitude humid regions. Plain Language Summary: Soil moisture (SM) refers to water in any form contained in soils, and it plays an important role in sustaining plant growth and influencing climate dynamics. SM deficits can result in ecological/agricultural droughts. However, our understanding of historical SM changes is limited due to the scarcity of long‐term records. Consequently, modeling historical SM changes proves challenging and prone to inaccuracies. In this study, we utilized a tree‐ring record to reconstruct centuries‐long SM changes in Sweden, located in northern Europe. Our analysis revealed a significant negative correlation between SM and temperature in both our reconstruction and SM data sets. However, state‐of‐art models failed to capture the significant relationship observed at our study site. These findings highlight the need to improve climate models to increase the accuracy of climate forecasts and better understand SM dynamics in high‐latitude humid areas. Key Points: This study presents the first 271‐year historical soil moisture (SM) reconstruction in northern Europe (NE) using the tree‐ring δ18O proxySignificant correlations between historical SM and temperature in NE were observed at interannual timescalesOur results implied that Coupled Model Intercomparison Project Phase 6 models might underestimate the SM‐temperature relationship [ABSTRACT FROM AUTHOR]

It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This power of the approach is illustrated by three examples from a decades‐long climate simulation experiment. The first example demonstrates that the hybrid model can produce sudden stratospheric warming, a dynamical process of nature not resolved by the low resolution AGCM component of the hybrid model. The second and third example show that introducing 6‐hr cumulative precipitation and sea surface temperature (SST) as ML‐based prognostic variables improves the precipitation climatology and leads to a realistic ENSO signal in the SST and atmospheric surface pressure. Plain Language Summary: This paper introduces and tests schemes for efficiently enabling significant expansion of the utility and scope of a recently introduced hybrid modeling technique that combines machine learning with an atmospheric global circulation model (AGCM). Simulation experiments are carried out with an implementation of the approach on a low resolution simplified AGCM. An examination of the simulated atmospheric circulation suggests that the hybrid model can capture dynamical process not captured by the AGCM. Moreover, the addition of precipitation and sea surface temperature (SST) as machine learning predicted physical quantities to the model improves the precipitation climatology and leads to a realistic El Niño‐La Niña signal in the SST and atmospheric surface pressure. Key Points: A hybrid system combining an atmospheric global circulation model (AGCM) with a machine‐learning component can capture processes not captured by the AGCMMachine learning provides a flexible framework to introduce additional prognostic variables into the hybrid modelThe prototype hybrid model tested in the paper is stable and has a realistic climate in decades‐long simulation experiments [ABSTRACT FROM AUTHOR]

When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub‐cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid‐scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry‐to‐wet 10‐days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas. Plain Language Summary: In nature, cold pools are formed by cold air masses descending from the low to mid‐troposphere in thunderstorms. When these drafts reach the surface, they spread out horizontally. A manifestation of cold pools is the relatively high speed at the gust front, which can lift environmental air producing new convective cells. Moreover, depending on ambient conditions, the cold pools may help organize the new convective cells, increasing their aggregation and forming the so‐called mesoscale convective systems (MCSs). MCSs, which cover hundreds to thousands of km2, significantly impact the global scale circulation, energy budget, hydrological cycle, and population safety. Forecasting MCSs is challenging for global circulation models (GCM) due to the broad spectrum of scales of the involved atmospheric phenomenon. The computational limitations, at present and for some time to come, do not allow running in real‐time GCMs, which explicitly solves all relevant scales of motion. This paper describes a methodology to account for essential interplays between cold pools edges and moist convection to be applied in the GCMs of weather and climate forecasting. We show that the method improves the model simulation of the main types of MCSs over the Amazon Basin and other continental areas. Key Points: A model scheme for including effect of cold pools edges in triggering new convective cells and storm propagation is presentedThe scheme is coupled with a convection parameterization and applied in the modeling of moist convective systemsThe method improves the organization, longevity, propagation, and severity of the simulated mesoscale convective systems [ABSTRACT FROM AUTHOR]

The ratio of O to N2 number densities (O/N2) at different altitudes is an important parameter in describing thermospheric neutral composition changes and their effects on the ionosphere during geomagnetic storms. However, storm‐induced vertical variations in O/N2 and its dependence on the O and N2 perturbations are still not fully understood. Here, the Thermosphere/Ionosphere Electrodynamics General Circulation Model simulations were used to investigate the responses of thermospheric composition at different pressure levels to the super geomagnetic storm occurred on November 20 and 21 in 2003. Our analysis shows that the behaviors of O/N2 perturbations on different pressure levels are similar above ∼180 km altitude. In the middle and low thermosphere of below ∼300 km, the storm‐time O/N2 decrease is mainly caused by a large reduction of O number density. However, N2 enhancement plays a vital role in O/N2 decreases in the upper thermosphere. The O/N2 enhancement is mainly attributed to the N2 decreases at all pressure levels. The changes of O and N2 number densities at a constant pressure level can be explained by the perturbations of their mass mixing ratio (mmr) and total mass density (ρ). The regions of the O/N2 decrease are characterized by the O mmr decrease and N2 mmr enhancement, whereas the regions of the O/N2 increase are characterized by the O mmr increase and N2 mmr decrease. The ρ value that shows the decrease globally at most pressure levels during the storm either enhance or reduce the O and N2 perturbations. Plain Language Summary: The column O/column N2 density ratio (∑O/N2) was usually used to describe thermospheric neutral composition responses to geomagnetic storms and the storm effects on ionospheric plasma density. However, thermospheric circulation changed considerably during the storm, resulting in discrepancies in composition at different altitudes. Additionally, the daytime electron density changes during geomagnetic storms are more related to those of local O/N2 at a given altitude, not the ∑O/N2. Therefore, it is important to fully understand the storm‐induced vertical variations in O, N2 and O/N2 perturbations. In this paper, the vertical variations in O/N2 and its dependence on the O and N2 perturbations during the 20–21 November 2003 storm are investigated by the numerical simulations. Our results shows that the behaviors of O and N2 perturbations depend much on the altitude, but those of O/N2 on different pressure levels are similar, especially above ∼180 km. This study helps us better understand the physical process of storm‐time ∑O/N2 variations based on the observations. Key Points: In middle and low thermosphere of below ∼300 km, storm‐time decreases of the ratio of O/N2 volume density are mainly caused by O reductionIn the upper thermosphere, N2 enhancement plays a vital role in the decreases of the ratio of O/N2 volume density during the stormAt all pressure levels, storm‐time increases of the ratio of O/N2 volume density depend more on the N2 decreases [ABSTRACT FROM AUTHOR]

We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021, https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in GCM development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models. Plain Language Summary: This paper discusses how machine learning can be used to make climate models more accurate. Specifically, we import an existing machine learning model that predicts how small eddies (in the order of 10–100 km) in the ocean affect larger currents. We test this machine learning model in a different ocean circulation model than the one it was originally designed for, and found that it worked well. However, we also found some limitations: the model works differently at different depths in the ocean, and it does not work as well near the coasts of the ocean. We also found that the model takes a long time to run on normal computers. Overall, we concluded that the model is promising, but more work is needed to make it work well in realistic situations. Key Points: A stochastic‐deep learning model is implemented in an ocean circulation model, MOM6We evaluate the online performance of the stochastic‐deep learning model as a subgrid parameterizationWe identify certain limitations of the machine learned parameterization which otherwise has the potential to improve specific metrics [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

Precipitation is highly variable on spatial scales smaller than a typical general circulation model (GCM) grid box. Neglecting this subgrid‐scale variability can impact the simulation of the underlying land surface and its coupling with the atmosphere. In this study we present a scheme to stochastically disaggregate precipitation from an atmospheric grid to an underlying mosaic of surface tiles, using an assumed probability distribution of subgrid precipitation derived from observations. Unlike previous GCM‐based schemes, our approach includes a treatment of memory to allow persistence of subgrid features. In single column model experiments, we demonstrate the ability of the scheme to reproduce observed precipitation statistics at the spatial scale of the surface tiles. The root mean square error of hourly spatial standard deviation of precipitation is reduced from 1.90 to 0.96 mm hr−1 when disaggregation is applied. The scheme increases the spatial standard deviations of surface heat fluxes, soil moisture and temperature, and we show that incorporating memory amplifies these increases by a factor of 2–4. We also document increases in mean precipitation runoff and the Bowen ratio. Plain Language Summary: Global climate and weather models (GCMs), used for global warming projections and near‐term weather prediction, represent the atmosphere on a grid, while the underlying land surface is often composed of smaller elements. In most models, all surface elements under a given atmospheric grid point receive the same precipitation rate. However, real‐world precipitation rates vary significantly over the scale of a GCM grid cell. This paper presents an approach to represent subgrid precipitation variability in a GCM. Grid mean precipitation is randomly distributed across surface elements, such that surface precipitation reproduces observed statistics. The approach is found to increase grid cell‐averaged surface runoff as well as the spatial variances of surface temperature, soil moisture, and surface sensible heat and moisture fluxes. Key Points: A parameterization is developed to stochastically distribute precipitation across subgrid surface tiles within a general circulation model (GCM)In single column experiments, the scheme reproduces observed precipitation statistics at the scale of land surface tilesThe scheme increases surface runoff, Bowen ratio, and the spatial variance of surface properties at the ARM Southern Great Plains site [ABSTRACT FROM AUTHOR]

Tidal signatures in sporadic E (Es) layer have been confirmed by observations and simulations. However, the effect of gravity waves (GWs) on the Es layer formation process has not yet been fully understood. In this paper, the modulation of Es layers by GWs is examined through numerical simulations, in which a physics‐based model of Es layer is forced by neutral winds from the High Altitude Mechanistic General Circulation Model that can resolve GWs with horizontal wavelengths longer than 156 km (λh > 156 km). Comparison of the simulation results with and without the GWs (1,350 km > λh > 156 km) forcing reveals that the inclusion of GWs leads to short‐period (1.2–3 hr) density perturbations in Es layers, which are also seen in ground‐based ionosonde observations. At a given time, the metallic ion density at altitudes between 120 and 150 km can either increase (by up to ∼+600%) or reduce (by up to −90%) in response to GW forcing. The relative density perturbations are smaller (by up to 60%) between 90 and 120 km altitude. It is also found that the GW effect on the metallic ion density relates to the longitude, which is mostly explained by the geographical distribution of GWs activity in the mesosphere and lower thermosphere region. The longitudinal variation of the background geomagnetic field plays only a secondary role. Key Points: The modulation of Es layers by gravity waves (GWs) (1,350 km > λh > 156 km) is simulatedGWs cause short‐period (1.2–3 hr) Fe+ density perturbations, leading to production of fine structure in mid‐latitude Es layersLongitudinal dependence in Fe+ density perturbations is mainly explained by the longitudinal distribution of GWs activity in the mesosphere and lower thermosphere region [ABSTRACT FROM AUTHOR]

In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. Different optimization tools have been successfully applied to address this task of model calibration. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes which cannot be fully resolved, it might be preferable to consider associated model parameters as random parameters. In this paper, a random parameter is drawn from a wide probability distribution for every singe model simulation. We developed an optimization approach that allows us to declare certain parameters random while optimizing those that are assumed to take fixed values. We designed a corresponding variant of the well known Covariance Matrix Adaption Evolution Strategy (CMA‐ES). The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES, our new method converges to a solution that better suits the credible range of the corresponding random parameter with less computational effort. Plain Language Summary: In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes it might be preferable to consider associated model parameters as random parameters that are drawn from a wide probability distribution for every single model simulation. To explore this further, we modified a popular variant of the Covariance Matrix Adaption Evolution Strategy (CMA‐ES) in order to tune the other parameters. The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES our new method converges to a solution that better suits the credible range of the corresponding random parameter. Key Points: Model calibration is an important task in the view of parametric uncertaintiesWe allow specific uncertain model parameters to be random while optimizing other model parameters, providing an efficient tool for the taskWe apply it to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐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 responseBoth 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 [ABSTRACT FROM AUTHOR]

This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics‐based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7–8 forecast days, and for even longer times for the temperature and humidity near the earth's surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the CHyPP approach for climate research is demonstrated by a 10‐year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM. Plain Language Summary: This paper presents a computationally efficient novel approach to construct a hybrid model of the atmosphere by combining a physics‐based model of the global atmospheric circulation with a machine learning component. The primary purpose of the hybrid model is to produce quantitative weather forecasts on the same grid as the physics‐based model. It is found that the hybrid model produces more accurate forecasts than the host physics‐based model for the first 7–8 forecast days for most forecast variables, and for even longer times for the temperature and humidity near the Earth's surface. Furthermore, the hybrid model is found to simulate the climate with substantially smaller systematic errors and more realistic temporal variability than the host model. Key Points: A hybrid model incorporating machine learning produces more accurate forecasts and more realistic climate than the host physics‐based modelThe hybrid model states are more realistically balanced and have substantially lower biases than the host modelThe hybrid model produces more realistic atmospheric variability than the host model at time scales shorter than about a week [ABSTRACT FROM AUTHOR]

We develop a doubly periodic version of the Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) to provide an efficient configuration for this global convection permitting model (GCPM), akin to a single column model often found in conventional general circulation models. The design details are explained, in addition to the extensive case library associated with the doubly periodic SCREAM (DP‐SCREAM) configuration. We demonstrate that doubly periodic cloud resolving models are useful tools to explore the horizontal resolution sensitivity of GCPMs, in addition to replicating biases seen in the global models. Using DP‐SCREAM, we show that SCREAM exhibits behaviors of a scale aware model as it is able to naturally partition between sub‐grid scale (SGS) and resolved vertical transport across the gray zone of turbulence. We show that SCREAM is reasonably scale insensitive when run at resolutions from 1 to 5 km, but can exhibit sensitivity, particularly for the shallow convective regime, when run at resolutions approaching that of large eddy simulations. We conclude that SGS parameterization improvements are likely needed to reduce this scale sensitivity. Plain Language Summary: Advances in computational resources have allowed climate simulations to be performed with very high resolution, which provides higher quality results. However, these simulations require a lot of time and computer resources to perform, which makes these models difficult to use for the common scientist. In this paper we develop a high‐resolution configuration which focuses on a specific point on the globe, enabling it to run fast and to use minimal computational resources. This allows users and developers to gauge how the model may perform before doing a computationally intensive global simulation. We show that this faster configuration is a useful tool to replicate problems that are found in the global model and is a valuable way to assess sensitivities of the model, particularly pertaining to choices made in its resolution. Key Points: Doubly periodic configurations represent an efficient framework to supplement global cloud resolving modelsShallow convection is sensitive to horizontal resolution in Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) but stratiform clouds are more robustSCREAM exhibits the behaviors of a scale aware model and can represent a range of cloud regimes [ABSTRACT FROM AUTHOR]

The thermospheric neutral density response to the 7–9 September 2017 storms is investigated based on the Swarm satellite observations and the thermosphere‐ionosphere‐electrodynamic general circulation model (TIEGCM) simulation. The Swarm data depicted a prominent interhemispheric asymmetry (IHA) in the afternoon sector during the second storm, a feature that was yet explained. Driven by realistic high‐latitude electric potential and electron precipitation patterns, the TIEGCM is able to reproduce the observed storm‐time neutral density response. The TIEGCM simulation reveals that the differences in the traveling atmospheric disturbances (TADs) is largely responsible for the observed IHA in the neutral mass density response at low and middle latitudes, whereas the difference in mean molecular mass between the two hemispheres may contribute to the IHA in neutral density at higher latitudes. The IHAs in TADs and mean molecular mass are attributed to the IHA in Joule heating dissipation on the night and dawn sides. Plain Language Summary: During geomagnetic storms, thermospheric neutral density can increase drastically due to intense heat inputs. Enhanced neutral density increases the air drag on satellites which could lower their orbits and lead to faster atmosphere reentering of satellites. In addition, enhanced neutral mass density may cause larger uncertainties in orbit prediction which increases the risk of collisions with other space objects. Therefore, it is essential to improve the understanding of storm‐time neutral density variations to improve satellite operations. Recent satellite observations have revealed that thermospheric neutral mass density response can be interhemispherically asymmetric even under equinox conditions when the background density in the two hemispheres is comparable. This paper presents a case study of such an event based on data analysis and numerical simulations to investigate the physical mechanisms behind this intriguing phenomenon. The event took place on 7–9 September 2017 and the most prominent interhemispheric asymmetry of neutral density occurred in the afternoon sector. Our study found that the interhemispheric asymmetry during this event results from the differences in the generation, propagation and interaction of globally propagating atmospheric waves, namely traveling atmospheric disturbances, as well as the difference in mean molecular mass between the northern and southern hemispheres. Key Points: Thermosphere‐ionosphere‐electrodynamic general circulation model captures the observed interhemispheric asymmetry (IHA) in the neutral density response to the 7–9 September 2017 geomagnetic stormsThe IHA in the neutral density response results from the IHA in traveling atmospheric disturbances and mean molecular massThe observed IHA in the neutral density response is not caused by direct Joule heating dissipated at the same local time [ABSTRACT FROM AUTHOR]

The mixing of tracers by mesoscale eddies, parameterized in many ocean general circulation models (OGCMs) as a diffusive‐advective process, contributes significantly to the distribution of tracers in the ocean. In the ocean interior, diffusive contribution occurs mostly along the direction parallel to local neutral density surfaces. However, near the surface of the ocean, small‐scale turbulence and the presence of the boundary itself break this constraint and the mesoscale transport occurs mostly along a plane parallel to the ocean surface (horizontal). Although this process is easily represented in OGCMs with geopotential vertical coordinates, the representation is more challenging in OGCMs that use a general vertical coordinate, where surfaces can be tilted with respect to the horizontal. We propose a method for representing the diffusive horizontal mesoscale fluxes within the surface boundary layer of general vertical coordinate OGCMs. The method relies on regridding/remapping techniques to represent tracers in a geopotential grid. Horizontal fluxes are calculated on this grid and then remapped back to the native grid, where fluxes are applied. The algorithm is implemented in an ocean model and tested in idealized and realistic settings. Horizontal diffusion can account for up to 10% of the total northward heat transport in the Southern Ocean and Western boundary current regions of the Northern Hemisphere. It also reduces the vertical stratification of the upper ocean, which results in an overall deepening of the surface boundary layer depth. Finally, enabling horizontal diffusion leads to meaningful reductions in the near‐surface global bias of potential temperature and salinity. Plain Language Summary: Mesoscale ocean eddies, which are analogous to the weather systems in the atmosphere, are crucial to the distribution of heat, salt, carbon, and nutrients throughout the global ocean. Most of the ocean models used in climate simulations do not have enough horizontal resolution to resolve these eddies and, therefore, their effects must be parameterized. In the ocean interior, where no appreciable heat or mass is exchanged across density surfaces, the mixing of tracers due to mesoscale eddies occurs along surfaces of constant density. However, as ocean boundaries are approached, mixing occurs on a plane parallel to the boundary. For example, near the surface of the ocean, which is where the scheme presented here is designed to work, this plane is mostly horizontal. There is a class of ocean models that use a vertical coordinate system whose layer thicknesses can vary horizontally, thus complicating the implementation of horizontal diffusive parameterizations. This paper presents and evaluates a method that allows horizontal fluxes to be calculated and applied within the surface layer of this class of ocean models. Key Points: General vertical coordinate ocean models lack the option to shift diffusive fluxes from neutral surfaces to a purely horizontal directionRegridding/remapping techniques are used to represent tracers in z*‐coordinates, where horizontal fluxes are easily appliedThe method reduces global tracer biases in forced simulations, regardless of the coordinate system employed [ABSTRACT FROM AUTHOR]

Radiative transfer models are widely applied in climate models to simulate vertical temperature perturbations caused by external radiative forcings. A large part of radiative transfer models is the infrared gaseous spectral transmittance scheme, which quantifies the longwave atmospheric absorption. A rapid infrared gaseous spectral transmittance scheme, called the Optimized alternate Mapping Correlated K‐Distribution model (OMCKD), is introduced in this paper. To improve the accuracy of our scheme without increasing pseudo‐monochromatic calculations, we introduce the optimal iteration method to automatically tune the equivalent absorption coefficients in the cumulative probability subspace. In addition, a new expression weighted by black‐body radiation is introduced to calculate the equivalent absorption coefficient. The OMCKD simulates heating rate and radiation flux with errors of less than 0.12 K d−1 and 0.35 W m−2, respectively, below stratopause for standard atmospheric profiles. The OMCKD is also evaluated and compared with the rapid radiative transfer model for general circulation models (RRTMG) in realistic atmospheric profiles. We found that OMCKD can accurately produce heating rates and generally captures radiative forcings associated with large perturbations to the concentrations of main greenhouse gases. Furthermore, the number of pseudo‐monochromatic calculations in OMCKD is 11.4% less than that in RRTMG, which indicates less computational cost. Plain Language Summary: Radiative models are practical tools for studying the greenhouse effect. The thermal infrared radiation absorbed by greenhouse gases is quantified by a specially designed program called the radiation scheme. The scheme divides the thermal infrared band into hundreds of intervals and calculates each interval's gaseous radiation absorption ability separately. Generally, increasing the number of intervals improves simulation accuracy, but the computational cost is proportional to the number of intervals. We have developed a rapid infrared gaseous spectral transmittance scheme called the Optimized alternate Mapping Correlated K‐Distribution model (OMCKD). Our scheme introduces an optimal method to adjust the radiation absorption ability of each interval with the line‐by‐line integration scheme (accurate but extremely time‐consuming) as the standard. We succeeded in improving the accuracy of our scheme without increasing the number of intervals. The OMCKD simulates heating rate and radiation flux with errors of less than 0.12 K d−1 and 0.35 W m−2, respectively, below stratopause for standard atmospheric conditions. The OMCKD generally captures the changes in the net fluxes and heating rates associated with perturbations to the concentrations of five main greenhouse gases. The stability of OMCKD implies its potential to improve the accuracy of climate predictions under higher CO2 concentration scenarios. Key Points: A rapid infrared gaseous spectral transmittance scheme based on the correlated K‐distribution method and the optimal method is developedThe optimal iteration method can significantly improve the accuracy of heating rates simulated with the correlated K‐distribution modelOur infrared gaseous spectral transmittance scheme maintains high accuracy across various carbon dioxide concentration scenarios [ABSTRACT FROM AUTHOR]

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress. Plain Language Summary: This paper describes the new global 3.25 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model and its behavior in a 40‐day northern‐hemisphere wintertime simulation. In exchange for huge computational expense, this high‐resolution model avoids many but not all biases common in lower‐resolution models. It also captures several types of extreme weather that would simply not be resolved in lower‐resolution models. Several opportunities for further development are identified. Key Points: Describes the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)SCREAM performs well in a 40‐day boreal winter simulation at 3.25 km ΔxResolving deep convection solves many long‐standing climate model biases [ABSTRACT FROM AUTHOR]

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 realistic mesoscale variability, and another in which the mesoscale SST variability is suppressed. The results of the present study suggest that the modeling approach yields the desired SST differences between the two ensembles at the mesoscales. They also show, however, that the approach can lead to undesirable SST differences at the large scales, if the prescribed pair of oceanic heat flux fields have large scale differences. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by nonlinear interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat flux fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Plain Language Summary: This study evaluates a modeling approach to investigate the potential effects of mesoscale (scales smaller than 100 km) ocean sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a thermodynamical model of the ocean. Two ensembles of model simulations are prepared: one in which the ocean exhibits realistic mesoscale SST variability, and another in which that variability is suppressed. The results suggest that the modeling approach yields the desired mesoscale SST differences. They also show, however, that the approach can lead to undesirable large scale SST differences, if there are large scale differences between the prescribed oceanic heat transport fields of the two ensembles. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by two‐way interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat transport fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Key Points: The large scale response in an uncoupled atmospheric simulation is the response of the model rather than that of the atmosphereA coupled modeling approach is necessary to investigate the large scale atmospheric response to mesoscale sea surface temperature variabilityA pair of oceanic heat fluxes that do not have large scale differences is critical to produce a realistic large scale atmospheric response [ABSTRACT FROM AUTHOR]

To assess the effect of ocean‐atmosphere coupling in the climate response to forced sea ice loss, the Polar Amplification Model Intercomparison Project protocol includes centennial coupled atmosphere‐ocean general circulation model simulations with imposed sea ice loss. The protocol, which specifies sea ice concentration and thickness distribution targets, does not prescribe a method for achieving them. Although different methods for imposing sea ice loss (or growth) in models have been documented, testing of the method‐dependence of the resulting climate responses has been limited. Achieving the targeted sea ice state has proven to be challenging using the so‐called ghost‐flux nudging method, which induces ice melt from below, as this method does not constrain the partitioning between thickness and concentration. We propose, describe and test a simple method that combines the advantages of direct sea ice nudging and ghost‐flux nudging. The hybrid nudging method better captures the partitioning between thickness and concentration while conserving total water content. We document how this novel sea ice constraining method reaches specific targets, enhances surface turbulent heat flux responses to sea ice loss, and induces tropospheric warming for both polar regions. Plain Language Summary: The Arctic is warming faster than the global average due to several processes that, once combined, lead to so‐called Arctic Amplification. Part of this anomalous polar warming comes from an intense reduction in ice cover allowing heat into the ocean, warming the Arctic ocean near the surface, hence melting more ice. A joint effort by several climate modeling groups called the Polar Amplification Model Intercomparison Project (PAMIP) aims at better understanding Arctic Amplification through a coordinated set of climate simulations. Among this ensemble of simulations is a set of centennial simulations performed with fully coupled state‐of‐the‐art climate models. In these experiments, Arctic (and Antarctic) sea ice are forced to reach specific states in order to better isolate Arctic Amplification and sea ice loss from the rest of anthropogenic global warming. In this paper, we propose a simple technique to nudge sea ice models to specific states such as prescribed by PAMIP. This new method combines advantages from existing techniques to improve the control over the extent and the thickness of the ice. We document how our novel method produces atmospheric warming and circulation responses that are more consistent with previously published results than the existing techniques. Key Points: Already established sea ice nudging method leads to issues in the partitioning between sea ice concentration (SIC) and sea ice thickness (SIT)New hybrid nudging method significantly improves capture of both SIC and SIT targets for Polar Amplification Model Intercomparison Project, in particular for Arctic sea iceHybrid scheme increases Arctic turbulent heat flux and warming [ABSTRACT FROM AUTHOR]

Abstract: The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking. [ABSTRACT FROM AUTHOR]

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed‐layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three‐dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed‐layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid‐latitude westerly jet and of the Inter‐tropical Convergence Zone (ITCZ) in response to sea‐ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed‐layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization. Plain Language Summary: Coupled model hierarchies that progressively add individual ocean processes can provide insights into the workings of the climate system. Because of the missing processes, surface flux corrections must be added to produce a target climate. In this paper, we manage to overcome this problem and develop a globally coupled ocean model hierarchy that can turn on and off the processes of mixed‐layer entrainment and Ekman flow. We use the hierarchy to study the impact of Arctic sea‐ice loss on the climate system. We find that the effect of mixed‐layer entrainment on ocean heat uptake influences the atmospheric circulation by shifting the latitudinal positions of the mid‐latitude westerly jet and the Inter‐tropical Convergence Zone. Key Points: We develop a flux‐corrected globally coupled ocean‐model hierarchy that explicitly includes mixed‐layer entrainment and Ekman flowFlux corrections need to contain components due to interannual variability to reproduce vertical gradient of ocean temperature and salinityThe hierarchy applied to Arctic sea‐ice loss reveals that the Inter‐tropical Convergence Zone is sensitive to the entrainment and Ekman flow parameterization [ABSTRACT FROM AUTHOR]

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks. Plain Language Summary: This paper describes the latest iteration of the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) climate model, which will be used for understanding historical climate change and to make projections for the future. We compare the model output to a wide range of observations over the recent era (1979–2014) and show that there has been a significant increase in how well the model performs compared to the previous version from 2014, particularly in the Southern Ocean, though some persistent biases remain. The model has a temperature response to the increase of carbon dioxide that is slightly higher than previous versions but is well within the range expected from observational and past climate constraints. Key Points: GISS‐E2.1 is an updated climate model version for use within the CMIP6 projectAtmospheric composition is calculated consistently in all model versionsResults demonstrate a significant improvement in skill in a climate model without changes to atmospheric resolution [ABSTRACT FROM AUTHOR]

The global ocean overturning circulation, critically dependent on the global density stratification, plays a central role in regulating climate evolution. While it is well known that the global stratification profile exhibits a strong dependence to Southern Ocean dynamics and in particular to wind and buoyancy forcing, we demonstrate here that the stratification is also acutely sensitive to the mesoscale eddy energy dissipation timescale. Within the context of a global ocean circulation model with an energy constrained mesoscale eddy parameterization, it is shown that modest variations in the eddy energy dissipation timescale lead to significant variations in key metrics relating to ocean circulation, namely the Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and global ocean heat content, over long timescales. The results highlight a need to constrain uncertainties associated with eddy energy dissipation for climate model projections over centennial timescales and also for paleoclimate simulations over millennial timescales. Plain Language Summary: The ocean is populated by "eddies", analogous to weather systems in the atmosphere, but occurring on much smaller scales of typically 10–100 km. Recent advances in our understanding of the circulation of the Southern Ocean, which connects all of the major ocean basins to the north, have revealed a crucial role for the energy balance of Southern Ocean eddies. In this paper, we show that the timescale over which energy is removed from Southern Ocean eddies has a dramatic impact on the following: (a) the strength of the Antarctic Circumpolar Current, the largest current in the global ocean; (b) the strength of the Atlantic meridional overturning circulation, responsible in part for the relatively mild climatic conditions over northwestern Europe; and (c) global ocean heat content, a key parameter in the global climate system. These results have significant implications for both past and future climates, and highlight the importance of combining observational, theoretical and modeling efforts to better understand and constrain the energy balance of ocean eddies. Key Points: Key metrics of global ocean circulation (Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and Ocean Heat Content anomaly) acutely sensitive to an eddy energy dissipation timescaleModest variations in the dissipation timescale has a comparable effect to significant variations in the Southern Ocean wind forcingConstraints on the dissipation timescale critical to longtime integrations of ocean climate models such as paleoclimate scenarios [ABSTRACT FROM AUTHOR]

This paper describes the main characteristics of CNRM‐CM6‐1, the fully coupled atmosphere‐ocean general circulation model of sixth generation jointly developed by Centre National de Recherches Météorologiques (CNRM) and Cerfacs for the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The paper provides a description of each component of CNRM‐CM6‐1, including the coupling method and the new online output software. We emphasize where model's components have been updated with respect to the former model version, CNRM‐CM5.1. In particular, we highlight major improvements in the representation of atmospheric and land processes. A particular attention has also been devoted to mass and energy conservation in the simulated climate system to limit long‐term drifts. The climate simulated by CNRM‐CM6‐1 is then evaluated using CMIP6 historical and Diagnostic, Evaluation and Characterization of Klima (DECK) experiments in comparison with CMIP5 CNRM‐CM5.1 equivalent experiments. Overall, the mean surface biases are of similar magnitude but with different spatial patterns. Deep ocean biases are generally reduced, whereas sea ice is too thin in the Arctic. Although the simulated climate variability remains roughly consistent with CNRM‐CM5.1, its sensitivity to rising CO2 has increased: the equilibrium climate sensitivity is 4.9 K, which is now close to the upper bound of the range estimated from CMIP5 models. Key Points: Description of CNRM‐CM6‐1 model components, their coupling, and tuning procedures are describedHistorical simulations and DECK experiments are assessedPreindustrial simulation is stable and mean climate and variability in historical runs is realistic [ABSTRACT FROM AUTHOR]

Data‐driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational‐Multiscale (VMS) Artificial Neural Network (ANN) discretization which makes no a priori assumptions on the model form, and is only restricted in its accuracy by the precision of the ANN. Using a simplified problem, we demonstrate that good predictions of the required closure terms can be obtained with relatively compact ANN architectures. We then turn our attention to the stability of the VMS‐ANN discretization in the context of a single implicit time step. It is demonstrated that the ANN parameterization introduces nonphysical solutions to the governing equations that can significantly affect or prevent convergence. We show that enriching the training data with nonphysical states from intra‐time step iterations is an effective remedy. This indicates that the lack of representative ANN‐induced errors in our original, exact training inputs underpin the observed instabilities. In turn, this suggests that data set enrichment might aid in resolving instabilities that develop over several time steps. Plain Language Summary: Computer models of weather and climate have coarse resolutions, which prevent them from accurately predicting the effect of small atmospheric motions, for instance in low‐lying clouds, on the global climate. Recent studies indicate that improvements might be had by using models for the small motions that are informed purely by data, for example Artificial Neural Network (ANN) models. However, capitalizing on this potential is challenging in practice, since ANNs can introduce instabilities to the numerical model for larger‐scale motions. In this study, we zero in on these instabilities by introducing a model framework in which prediction accuracy is only limited by an ANN's ability to predict the net influence of small atmospheric motions. We contextualize this framework by contrasting it to more commonly used approaches. Using a simple test case based on the motions underpinning the development of low clouds, we illustrate that standard training procedures do not prepare ANN models for their interaction with the rest of the numerical model, and that this can contribute to numerical instability. We demonstrate an effective remedy for a specific type of instability, and suggest how this technique might also be used to treat other types of instabilities prompted by ANN models. Key Points: Artificial Neural Networks (ANNs) can parameterize exact closure terms of a Variational Multiscale method without a priori model form assumptionsThe ANN parameterization outperforms state‐of‐the‐art schemes in offline testsInstabilities in the numerical model comprising the ANN parameterization are amendable through data set enrichment [ABSTRACT FROM AUTHOR]

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 chain Monte Carlo (MCMC) methods typically require O(105) model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(102) model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties. Plain Language Summary: Calibrating climate models with available data and quantifying their uncertainties are essential to make climate predictions accurate and actionable. A primary source of uncertainties in climate models comes from representation of small‐scale processes such as moist convection. Parameters in convection schemes and other parameterizations are usually calibrated by hand, using only a small fraction of data that are available. As a result, the calibration process may miss information about the small‐scale processes in question. This paper presents a proof‐of‐concept, in an idealized setting, of how parameters in climate models can be calibrated using a substantial fraction of the available data, and how uncertainties in the parameters can be quantified. We employ a new algorithm, called calibrate‐emulate‐sample (CES), which makes such calibration and uncertainty quantification feasible for computationally expensive climate models. CES reduces the hundreds of thousands of model runs usually required to quantify uncertainties in computer models to hundreds, thereby achieving about a factor 1,000 speedup. It leads to more robust calibration and uncertainty quantification in the presence of noise arising from chaotic variability of the climate system. We show how uncertainties in climate model parameters can be translated into quantified uncertainties of climate predictions through ensemble integrations. Key Points: We use time averaged climate statistics to calibrate convective parameters and quantify their uncertaintiesWe demonstrate use of the calibrate‐emulate‐sample algorithm to provide efficient calibration and uncertainty quantificationParametric uncertainty in climate predictions is quantified by sampling from the learnt convective parameter distribution [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: For forecasts less than about 10 days, Numerical Weather Prediction (NWP) and General Circulation Models (GCMs) have been highly successful, yet for longer ranges, their skill is disappointing. This has motivated the development of alternatives that are based on either the strong spatial correlations–teleconnection patterns such El Niño events–or on the long memories whereby the atmospheric state at any moment is strongly influenced by its own past. In particular, a model only using the long memory already rivals GCM monthly and seasonal temperature forecasts: The Stochastic Seasonal to Interannual Prediction System (StocSIPS). In this paper, we answer the question of whether StocSIPS skill can be further improved by using teleconnections. We do this by developing the space‐time m‐StocSIPS model that is optimally forecast by StocSIPS. In m‐StocSIPS, spatial co‐predictors do not improve the skill: There is no causal relation between different locations useful for long‐range predictions. Although m‐StocSIPS has strong spatial correlations and reproduces teleconnection patterns including El Niño events, they cannot be used to improve the long‐memory StocSIPS forecasts. The teleconnections "were already used" to build the history at every location, which is enough to produce the optimal forecast. Key Points: Granger causality shows that spatial correlations in temperature cannot be used to improve on memory‐based predictions of infinitely long seriesScaling‐based long‐range stochastic forecasting is a past value problem not an initial value problemReal‐world statistics and teleconnection patterns can be reproduced with stochastic simulations with a total lack of causal relationships [ABSTRACT FROM AUTHOR]

Knowledge of the daytime variability of cloud fraction is pivotal for the accurate determination of the atmosphere's energy balance. Yet, polar orbiting satellites are unable to fully capture daytime variability and individual geostationary satellites only provide measurements for portions of the Earth. In addition, diurnal cloud fraction variations analyzed with General Circulation Models provide results that cannot be reconciled with observations in many situations. Here, we show that the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite presents a unique opportunity to examine changes in daytime global cloud fraction resolved at one‐hour frequency. For the first time, we characterize the global daytime variability of clouds with a single sensor, presenting evidence that the fraction of liquid clouds reaches its maximum over continental areas and its minimum over ocean around noon. We compare our results with previous global and regional studies and examine implications for the Earth's energy budget. Plain Language Summary: Cloud coverage changes during the day over every location of the Earth. While those movements might seem random for a particular day, patterns emerge when cloud fraction is evaluated with sufficient data in local times. The Earth Polychromatic Imaging Camera (EPIC) aboard the DSCOVR satellite observes the Earth from approximately 1.5 million kilometers away, capturing the whole sunlit side of the planet at once. Therefore, each one of EPIC's images contains information about daytime cloud fraction at different local times, with early morning and late afternoon in the left and right edges respectively. As EPIC takes between 10‐22 images per day, we can track the daytime evolution of cloud fraction with a single sensor. In this paper, we aggregate seasonally four years of data to show that there are more liquid clouds over land and less over ocean around noon. On the contrary, ice clouds evolve independently of the underlying surface type, with larger cloud fraction in the morning and afternoon and a minimum at noon. Key Points: Global daytime cycles are analyzed with a single sensor for the first time using DSCOVR/EPICLiquid cloud fraction is maximum over land and minimum over ocean around noonIce cloud fraction is higher in the morning and afternoon than around noon independently of the surface type [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations in the North Atlantic since 1870 reveal a region of enhanced warming off the northeastern coast of North America, and a region of cooling to the south of Greenland. It has been hypothesized that these adjacent SST trends are a result of long‐term changes in the buoyancy‐driven ocean circulation—a slowdown of the Atlantic Meridional Overturning Circulation. The impacts of these historical SST trends on the atmosphere are estimated using idealized atmospheric general circulation model experiments in which the global atmosphere is exposed to modern climatological forcing minus the aforementioned regional SST trends. The local response includes a negative North Atlantic Oscillation tendency and southward shift of the wind forcing for the subtropical gyre. Due to planetary wave propagation, the regional SST trends also induce a northward shift of the intertropical convergence zone over the Indian Ocean. Implications for climate feedbacks and projections are discussed. Plain Language Summary: The ocean surface is warming over most of the planet, with a couple of notable exceptions. One is over the North Atlantic Ocean, just south of Greenland, where the temperature of the ocean surface has actually been cooling by about 1°C since 1870. Nearby, extending off the northeastern coastline of North America is a region where the ocean surface has been warming much faster than the global ocean on average—by about 1.5°C since 1870. This side‐by‐side pair of accelerated warming and cooling trends is likely due to a slowdown of the ocean's overturning circulation, which carries large amounts of heat energy from the tropics toward the poles. This paper reveals the effects of those unusual ocean temperature trends in the North Atlantic on the atmospheric circulation using a computer model of the global atmosphere. The model simulations indicate that these ocean temperature trends are causing shifts in the jet stream, changing the way the winds propel the upper ocean currents, and even have impacts quite far away by moving the tropical rain belt northward in the Indian Ocean. Key Points: The impact of historical sea surface temperature trends in the North Atlantic since 1870 is simulated with a global atmospheric modelAdjacent warming and cooling trends in the North Atlantic Ocean induce a negative NAO‐like response and southward‐shifted gyre forcingPropagation of planetary waves away from the North Atlantic induces a northward‐shifted intertropical convergence zone in the Indian Ocean [ABSTRACT FROM AUTHOR]

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, the algorithm dissipates tracer extrema locally, and no new extrema are created. A demonstration using a linear equation of state in an idealized configuration shows that the algorithm is perfectly neutral. When using the nonlinear TEOS‐10 equation of state with a constant reference pressure, the algorithm compares nearly exactly to a case discretized onto isopycnal surfaces and using along‐layer diffusion. The algorithm's cost is comparable to that of tracer advection and can be readily implemented into ocean general circulation models. Plain Language Summary: One prominent effect of ocean turbulence is the mixing of quantities such as temperature, salinity, and other tracers carried by the seawater. For turbulence associated with geostrophic motions, this mixing occurs along neutral directions, which are directions where buoyancy does not change. In computer models that do not explicitly represent such geostrophic eddy motions, this type of turbulent mixing is parameterized by diffusion oriented along the neutral directions. A common means to represent neutral diffusion is via rotating the diffusion fluxes to align with neutral directions (which are typically not aligned with the model grid lines). Unfortunately, this rotation can result in spurious numerical artifacts such as the creation of new minimum or maximum values of tracer concentration (referred to as extrema). In this paper, we propose a new algorithm for calculating and applying neutral diffusion in sublayers of a numerical model. We show that this new method does not create extrema, and it provides a physically accurate representation of turbulent diffusive mixing along neutral directions. Key Points: A new discretization of a neutral diffusion operator is detailed, with the new method not based on a rotated tensorThe new algorithm overcomes many numerical deficiencies of previous approachesIdealized test cases show minimal spurious dianeutral diffusion for linear and nonlinear equations of state [ABSTRACT FROM AUTHOR]

Reconstruction of the atmospheric circulation from the numerical output of general circulation model (GCM) and reanalysis products often suffers from spurious values resulting in imbalances in the mass, energy, and water transport. A correction hence is needed prior to the analysis of energy transport. Most of the previous correction methods only employ a mass budget adjustment since it is the primary source of error. The present study proposes a new method of mass flux correction by constraining the mass, energy, and water budgets that can be applied to meridional energy transport and overturning circulation. The new method seeks a vertical dependent solution, unlike the conventional barotropic correction procedures. The procedure has been tested for one aquaplanet GCM and a reanalysis product. The method successfully corrects the spurious imbalances in the mean meridional energy transport and circulation. Plain Language Summary: Meridional energy transport is an essential climate diagnostic. However, the calculation of energy transport from climate model output and reanalyses often encounters spurious values due to mass, energy, and water budget imbalances. Although climate models are based on fundamental conservation laws, the numerical models often slightly deviate due to various interpolations, approximations, and low resolution. Traditionally, such imbalances are corrected by adjusting the mass flux to satisfy mass conservation, which corrects the spurious values in energy transport to a certain extent. The present paper proposes a new mass flux correction method that satisfies not only the mass conservation but also the energy and water conservation. Unlike the conventional vertically uniform correction, the present method provides a vertically dependent correction. Moreover, the new mass flux correction can also be applied to correct the mean meridional circulation. The result demonstrates that the new method is a remarkable improvement from the conventional methods. Key Points: The present study proposes a new flux correction method for vertically integrated zonal mean energy transport in the atmosphereThe proposed method satisfies the conservation of mass, energy, and water vapor, which was not a characteristic of most previous methodsUnlike the previous methods, the new approach provides a vertical dependent correction [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) Goddard Earth Observing System global circulation model (GCM) is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a nonhydrostatic dynamical core and includes a scale‐aware, deep convection parameterization (DPCP). The 40‐day simulations at six resolutions (100 km to 3 km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out: one with no DPCP and one with its scale awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale‐aware DPCP even at 3‐km scale, as the run relying solely on explicit grid‐scale production of rainfall performs more poorly at this resolution. Plain Language Summary: Global circulation models (GCMs) are the means we have to predict the weather and assess the future climate on daily, seasonal, and even century‐long timescales. GCMs are computational tools that employ approximate forms of the fundamental equations governing the time evolution of the atmosphere. Given the broad spectrum of scales of the atmospheric phenomenon, computational limitations require parameterizations for processes not explicitly resolved at the model computational grid. The next generation of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS) GCM is being developed as a unified, seamless model to be applied across a broad spectrum of spatial and temporal scales of the atmospheric motions. This paper provides an evaluation of relevant aspects of the GEOS forecasts across a pertinent range of horizontal resolution with an unvarying model formulation. We focus specifically on the parameterization of deep convection and evaluate its influence on model results down to the a few kilometers scale. We arrive at the unexpected conclusion that a kilometer‐scale resolution where convection is mostly explicitly resolved perform worse. Key Points: The Goddard Earth Observing System global circulation model is evaluated across a wide spectrum of horizontal resolutionAcross scales, the convective precipitation smoothly transitions from mostly produced by subgrid‐ to grid‐scale model formulationsContrary to current views, poorer results occur when scale‐aware deep convection parameterization is avoided at grid spacings of a few kilometers [ABSTRACT FROM AUTHOR]

This study presents the version of the LMDZ global atmospheric model used as the atmospheric component of the Institut Pierre Simon Laplace coupled model (IPSL‐CM6A‐LR) to contribute to the 6th phase of the international Coupled Model Intercomparison Project (CMIP6). This LMDZ6A version includes original convective parameterizations that define the LMDZ "New Physics": a mass flux parameterization of the organized structures of the convective boundary layer, the "thermal plume model," and a parameterization of the cold pools created by reevaporation of convective rainfall. The vertical velocity associated with thermal plumes and gust fronts of cold pools are used to control the triggering and intensity of deep convection. Because of several shortcomings, the early version 5B of this New Physics was worse than the previous "Standard Physics" version 5A regarding several classical climate metrics. To overcome these deficiencies, version 6A includes new developments: a stochastic triggering of deep convection, a modification of the thermal plume model that allows the representation of stratocumulus and cumulus clouds in a unified framework, an improved parameterization of very stable boundary layers, and the modification of the gravity waves scheme targeting the quasi‐biennal oscillation in the stratosphere. These improvements to the physical content and a more well‐defined tuning strategy led to major improvements in the LMDZ6A version model climatology. Beyond the presentation of this particular model version and documentation of its climatology, the present paper underlines possible methodological pathways toward model improvement that can be shared across modeling groups. Plain Language Summary: The improvement of global numerical models is essential for the anticipation of future climate changes. We present significant advances in the physical content of a particular atmospheric model which contributes to the simulations of the Coupled Model Intercomparison project CMIP that feed reports from the IPCC. We document in particular the improvements of the representation through "parameterizations" of convective and cloudy processes. The article emphasizes the importance of strengthening the formalization of the methodology of development and tuning of models, so that new physical ideas can be translated into effective improvement of the climate representation. Key Points: The development strategy of the LMDZ6A global atmospheric circulation model is presentedImprovements with respect to previous versions are documented in the context of the Coupled Model Intercomparison Project, CMIPThe improvements are based on significant changes of the physics content as well as on a better controlled tuning strategy [ABSTRACT FROM AUTHOR]

Climate models rely on parameterizations of a variety of processes in the atmospheric physics, but a common concern is that the temporal resolution is too coarse to consistently resolve the behavior that individual parameterizations are designed to capture. This study examines timescales numerically derived from the Morrison‐Gettelman (MG2) microphysics as implemented within the Energy Exascale Earth System Model, version 1 (E3SMv1). Numerically relevant timescales in MG2 are derived by computing the eigenspectrum of its Jacobian. These timescales are found to often be smaller than the default 5 min time step used for MG2. The fast timescales are then heuristically connected to individual microphysics processes. By substepping a few particular rain processes within MG2, the time discretization error for those processes was considerably reduced with minimal additional expense to the overall microphysics. While this improvement has a substantial effect on the target processes and on the vertical distribution of stratiform‐derived rain within E3SMv1, the overall model climate is found to not be sensitive to MG2 time step. We hypothesize that this is because the surface climate does not depend strongly on certain process rates, especially MG2's rain evaporation rate. Plain Language Summary: The atmospheric components of climate models contain a number of physics parameterizations, subcomponents that are designed to capture particular aspects of the atmospheric physics. Cloud microphysics models are parameterizations designed to represent very small scale cloud processes, including phase changes and the formation of precipitation. The accuracy of these parameterizations depends on the model time step: A shorter time step typically requires more computational resources, but also improves the model's accuracy. This paper examines a particular microphysics model, MG2, used with a time step of 5 min in the E3SMv1 climate model. By linearizing MG2, we can find characteristic timescales associated with this model, which are often much shorter than 5 min. This suggests that the usual time step is too large to fully capture the physics that MG2 represents. We also experiment with using a shorter time step for parts of the rain physics and find that in many cases the rain mass is strongly affected. However, reducing the MG2 time step does not have much effect on the overall climate of E3SMv1. Key Points: MG2 contains several processes that are poorly resolved at a time step of 5 minSubstepping MG2's rain‐related processes changes their rates considerablyHowever, reducing MG2's time step within E3SM has little to no impact on the climate [ABSTRACT FROM AUTHOR]

Subauroral red (SAR) arcs are commonly observed ionospheric red line emissions. They are usually attributed to subauroral electron heating by inner magnetospheric heat flux (IMHF). However, the role of IMHF in changing the ionosphere‐thermosphere (IT) still remains elusive. We conduct controlled numerical experiments with the Thermosphere‐Ionosphere Electrodynamic General Circulation Model (TIEGCM). Coulomb collisional heat flux derived with the Comprehensive Inner Magnetosphere Ionosphere (CIMI) model and empirical subauroral polarization streams (SAPS) are implemented in TIEGCM. The heat flux causes electron temperature enhancement, electron density depletion, and consequently SAR arcs formed in the dusk‐to‐midnight subauroral ionosphere region. SAPS cause more substantial plasma and neutral heating and plasma density variations in a broader region. The maximum enhancement of subauroral red line emission rate is comparable to that caused by the heat flux. However, the visibility of SAR arcs also depends on the relative enhancement to the background brightness. Plain Language Summary: The Earth's topside atmosphere is subject to energy inputs from the magnetosphere and solar wind. In addition to the Joule heating generated by high latitude plasma convection and energy flux carried by precipitating magnetospheric particles, magnetospheric energy can be also deposited in the ionosphere‐thermosphere via heat flux, that is, energy flows carried by low‐energy thermal electrons. When hot ions in the ring current collide with the cold plasma in the plasmasphere, heat conduction occurs and the resultant heat flux is transported along geomagnetic field lines to the footprint ionosphere. The additional heating raises the electron temperature in the subauroral ionosphere and modifies the ionosphere‐thermosphere states. This study uses first‐principles inner magnetosphere model and ionosphere‐thermosphere model to illustrate the thermodynamic coupling effects between the topside ionosphere and the magnetosphere, and compare the relative significance between the heat flux and plasma convection due to electrodynamic coupling. The numerical experiments show that the heat flux primarily increases electron temperature while subauroral plasma flow heats up both plasma and neutrals. Despite different physical mechanisms, the heat flux and subauroral plasma convection make comparable contributions to red line emission rates in the subauroral region. Key Points: Inner magnetospheric heat flux increases subauroral ionospheric electron temperature and depletes the density to form subauroral red arcsCompared to subauroral polarization streams, the heat flux heating effects are only confined to electrons in the subauroral regionThe heat flux produces negligible impacts on ions and neutrals compared to subauroral polarization streams [ABSTRACT FROM AUTHOR]

The equatorial Kelvin waves, remotely excited by basin‐scale climate modes, and subsequent coastal trapped waves significantly influence the intraseasonal variations, their low‐frequency modulations, and the frequency of extreme sea level events along the western coast of India. This study demonstrates that the frequency of extreme events are linked to the phase of the Indian Ocean Dipole mode. The temporal changes in the occurrence frequency of extremes are simulated in an eddy‐resolving ocean model consistently with observations. However, a non‐eddying model significantly underestimate the occurrence frequency of extreme sea level events, suggesting the importance of coastal trapped wave propagations regulated by the horizontal scale with the Rossby radius of deformation. This result implies that many state‐of‐the‐art climate models with a one‐degree ocean horizontal resolution may underestimate future coastal sea level variability and the frequency of extreme events under global warming and potential modulations of major internal climate modes. Plain Language Summary: Sea level variations in the northern Indian Ocean are influenced by ocean waves near the coast, typically in a horizontal scale less than 100 km. It remains unclear whether there is a relationship between extreme events associated with coastal waves and climate variability. Also, if such a relationship exists, it is uncertain how well it is represented in climate simulations, which often have relatively coarse horizontal resolution. To highlight the role of relatively small scale coastal waves, this study compared sea level variations along the western coast of India using two ocean models with coarse and fine horizontal resolutions. We found that the high‐resolution model adequately simulates the generation and propagation of coastal waves, and thus successfully simulate sea level variations along western India modulated by large scale climate variability with a 20–150‐day time scale. This result suggests that many recent climate simulations may have underestimated the frequency of extreme sea level events in coastal regions. Key Points: The eddy‐resolving model represents the intrasesonal sea level variability along the coast of India explained by coastal trapped wavesThe occurrence frequency of extreme intraseasonal sea level anomalies is significantly underestimated in the non‐eddying modelChanges in the probability distributions of sea level associated with the Indian Ocean Dipole are simulated in the eddy‐resolving model [ABSTRACT FROM AUTHOR]

Wind‐generated near‐inertial internal waves (NIWs) are characterized by dominant long‐range equatorward radiation due to the gradient of the planetary vorticity, known as the β‐refraction effect. In this study, we analyze the effects of horizontal model resolution on the long‐range equatorward radiation of NIWs. In a high‐resolution Community Earth System Model (CESM‐HR) with a 0.1° oceanic resolution, about 25% (15%) of NIW energy flux injected downward the surface boundary layer base poleward of 30°N (30°S) radiates into the lower‐latitude region. This ratio decreases to about 15% (8%) in a low‐resolution CESM (CESM‐LR) with a 1° oceanic resolution. The higher long‐range equatorward radiation efficiency in the CESM‐HR than the CESM‐LR is directly attributed to the faster equatorward group velocity of the NIWs of the first three vertical modes, which reflects the better representation of equatorward propagation and beta‐refraction of smaller scale NIWs in the CESM‐HR. The enhancement of equatorward wavenumber induced by the β‐refraction is inhibited in the CESM‐LR, which underrepresent the long‐range equatorward radiation of NIWs. These results underscore the necessity of high‐resolution ocean models in accurately simulating the spatial variabilities of NIWs and their induced turbulent diapycnal mixing in the global ocean. Plain Language Summary: Generated by variable winds like synoptic storms, hurricanes, and typhoons, near‐inertial internal waves (NIWs) are ubiquitous in the upper ocean with frequency close to the inertial frequency. Although NIWs have been extensively studied using numerical modeling, there remains a question on how fine the model horizontal resolution needs to be for their reliable simulations. In this study, we investigate the effect of model horizontal resolution on the long‐range equatorward radiation of NIWs induced by the gradient of planetary vorticity. It is found that the long‐range equatorward radiation of NIWs is significantly weaker in a climate model with a 1° oceanic resolution than in one with a 0.1° oceanic resolution. This finding emphasizes the importance of fine model resolution in accurately simulating the geographical distribution of NIWs and their induced turbulent mixing. Key Points: Long‐range equatorward radiation efficiency of near‐inertial waves (NIWs) is enhanced in high‐resolution models than in low‐resolution modelsEquatorward group velocity of the NIWs of the first three modes is essential in determining the long‐range radiation efficiencyThe beta refraction of NIWs is inhibited in the low‐resolution model [ABSTRACT FROM AUTHOR]

Different meteor radars at low latitudes observed abnormally strong westward mesospheric winds around the March Equinox of 2023, that is, during the first phase of the Mesospheric Semiannual Oscillation. This event was the strongest of at least the last decade (2014–2023). The westward winds reached −80 m/s at 82 km of altitude in late March, and decreased with increasing altitude and latitude. A considerable increase in the diurnal tide amplitude was also observed. The Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension constrained to meteorological reanalysis up to ∼50 km does not capture the observed low‐latitude behavior. Additionally, these strong mesospheric winds developed during the westerly phase of the Quasi‐Biennial Oscillation, in accordance with the filtering mechanism of gravity waves in the stratosphere proposed in previous works. Finally, analysis of SABER temperatures strongly suggests that the breaking of the migrating diurnal tide may be the main driver of these strong winds. Plain Language Summary: Around the March Equinox of 2023, abnormally strong westward winds were observed in the low latitude region at altitudes between 80 and 100 km. This event was the strongest in at least the last decade. The westward winds reached a maximum amplitude of 80 m/s at 82 km of altitude during late March, and decreased with increasing altitude and latitude. A considerable increase in the amplitude of the diurnal tide was also observed. Simulations based on a whole atmosphere global circulation model constrained to meteorological reanalysis up to ∼50 km do not capture the observed behavior. Results based on specular meteor radar and satellite measurements suggest that the strong westward winds were driven by two main factors: the filtering mechanism of eastward‐propagating gravity waves in the stratosphere and the breaking of the diurnal tide at about 85 km of altitude. Key Points: Strong mesospheric westward winds during March equinox 2023 are observed globally at low latitudesThe westward winds reached a peak of −80 m/s at 82 km, the largest in the last ten years, accompanied by an enhancement of the diurnal tideThe breaking of the DW1 plays a role in generating these strong winds, in addition to the filtering of gravity waves in the stratosphere [ABSTRACT FROM AUTHOR]

Many general circulation models (GCMs) assume some heterogeneity of water amounts in their grid boxes and use probability density functions to parameterize cloud fractions CF and amounts of condensed water qc. Most GCM cloud schemes calculate the CF as the volume of the grid box that contains clouds (CFvol), whereas radiative fluxes primarily depend on the CF by surface (CFsurf), that is, the surface of the grid box covered by clouds when looking from above. This discrepancy matters as previous findings suggest that CFsurf is typically greater than CFvol by about 30%. In this paper we modify the single column model version of the LMDz GCM cloud scheme by introducing the vertical subgrid‐scale heterogeneity of water content. This allows to distinctly compute the two fractions, CFvol and CFsurf, as well as the amount of condensed water qc. This study is one of the first to take into account such vertical subgrid‐scale heterogeneity in a GCM cloud scheme. Three large eddy simulation cases of cumuliform boundary layer clouds are used to test and calibrate two different parameterizations. These new developments increase cloud cover by about 10% for the oceanic cases RICO and Barbados Oceanographic Meteorological Experiment and by up to 50% for the continental case ARM. The change in condensed water reduces the liquid water path by 10–20% and therefore the cloud opacity by 5–50%. These results show the potential of the new scheme to reduce the too few, too bright bias by increasing low‐level CF and decreasing cloud reflectance. Key Points: Most cloud schemes assume vertically homogeneous cloud fractions in GCM grid boxesThis approximation may induce an underestimation of cloud cover and overestimation of reflectanceWe parameterize 3‐D subgrid‐scale heterogeneities for low‐level clouds and reduce these biases [ABSTRACT FROM AUTHOR]

Abstract: NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near‐real‐time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)‐based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided. [ABSTRACT FROM AUTHOR]

Ecological niche modelling and first records from Namibia and Zimbabwe validate the amphi-equatorial distribution of Byrsinus pseudosyriacus (Hemiptera: Heteroptera: Cydnidae) The genus I Byrsinus i Fieber, 1860 belongs to the tribe Geotomini in the subfamily Cydninae of the family Cydnidae (sensu Pluot-Sigwalt & Lis, 2008; and Lis et al., 2017). Moreover, the distance of the new localities in Namibia and Zimbabwe to the Equator is almost the same as the Equator's distance to the species northernmost site. At present, 26 species are classified within Byrsinus (Dusoulier & Lupoli, 2015; Lis, 1999, 2001, 2003) including a single Oriental species, four representatives in the Australian Region, 17 species in the Palearctic and four Afrotropical species, namely I B i . [Extracted from the article]

Observing System Simulation Experiments (OSSEs) provide an effective way to evaluate the impact of assimilating data from a specific observing system on hindcasting, nowcasting, and forecasting of environmental systems. The NSF NCAR's Data Assimilation (DA) Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) tool, to be hosted at the NASA Community Coordinated Modeling Center, serves as a valuable and accessible community resource for quantitatively evaluating the impact of observations from both current and future ionosphere‐thermosphere (IT) observing systems. This study demonstrates the utility of DART/TIEGCM as an IT OSSE tool, using synthetic observations simulated using a currently planned NASA Geospace Dynamics Constellation (GDC) observing system design. Five sets of OSSEs are carried out to compare the effects of assimilating various combinations of prospective GDC observations (e.g., neutral temperature, neutral wind, neutral composition, atomic oxygen ion density, and ion and electron temperature) during a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. The maximum error reduction in neutral temperature and atomic ion oxygen density is 24.6% and 43.3% compared to the control experiment. These OSSEs indicate the benefits of coupled IT DA approaches implemented in DART/TIEGCM to maximize the impact of multi‐parameter IT observations, such as those expected from the GDC mission. Although more work is required to draw any definitive conclusion on the GDC data impact, the study provides an illustrative example of how the DART/TIEGCM community tool can be used to evaluate observational impacts of planned or existing missions for geospace research and applications. Plain Language Summary: Data assimilation (DA) is a method that optimally combines observation information with a model to improve hindcasting, nowcasting, and forecasting of environmental conditions. An Observing System Simulation Experiment (OSSE) is a type of DA experiment that can help evaluate the impact of assimilating data from a specific observation system. The NSF NCAR's DA Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) tool serves as a community resource for DA of the ionosphere‐thermosphere (IT) observing system. This study demonstrates the utility of DART/TIEGCM using synthetic observations from a notional observing system provided by the planned NASA Geospace Dynamics Constellation mission under geophysical conditions that mimic a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. This preliminary study provides an illustrative example of how DART/TIEGCM can be used to evaluate the impact of an observing system on geospace research and applications. Key Points: Data Assimilation (DA) Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) is a community DA tool that facilitates ionosphere‐thermosphere Observing System Simulation ExperimentsDART/TIEGCM's utility as a viable Observing System Simulation Experiment tool is demonstrated with the currently planned Geospace Dynamics Constellation (GDC) observing systemCoupled ionosphere‐thermosphere DA approaches can help maximize the impact of multi‐parameter observations, such as from GDC [ABSTRACT FROM AUTHOR]

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]

Current eddy‐permitting and eddy‐resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large‐scale ocean models that involves upwinding through a weighted essentially non‐oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, "vector invariant" formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO "smoothness indicators" is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid‐resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (a) two‐dimensional turbulence and (b) three‐dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased "effective" resolution compared to other approaches commonly used in ocean models. Plain Language Summary: High‐resolution climate models that resolve the cyclones and anticyclones in the ocean, often called "eddies," must prevent an artificial build‐up of whirl‐like movements, or "enstrophy," at the model's grid‐scale. Even though methods that prevent artificial accumulation of enstrophy are included only to ensure numerical stability, they unfortunately also negatively impact the accuracy of model predictions even at scales larger than the grid‐scale. Here, we devise a novel numerical method to overcome this deficiency. Our method has the best of both worlds: it removes just enough enstrophy so that the flow is as close to reality as possible and it achieves this without accumulating enstrophy at grid‐scale. Key Points: We describe a new momentum advection scheme based on upwind‐biased, weighted essentially non‐oscillatory reconstructionsThe new scheme automatically adapts to horizontal resolution without generating grid‐scale noise typical of low order oscillatory schemesWe demonstrate a higher "effective" resolution compared to other widely used approaches [ABSTRACT FROM AUTHOR]

The Venusian atmosphere hosts a 10 km deep convective layer that has been studied by various spacecrafts. However, the impact of the strong vertical mixing on the chemistry of this region is still unknown. This study presents the first realistic coupling between resolved small‐scale turbulence and a chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. Vertical eddy diffusion due to resolved convection motions was estimated, ranging from 102 to 104 m2/s for the 48–55 km convective layer, several orders of magnitude above the typically used value. In the 48–55 km convective layer, the impact of the small‐scale turbulence on the cloud layer boundaries was between 200 m and 1 km. The impact of turbulence on cloud chemistry is consistent with Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations. The observability at the cloud‐top of small‐scale turbulence by VenSpec‐U spectrometer would be challenging. Plain Language Summary: Venus hosts a global sulfuric acid cloud layer between 45 and 70 km. A convective layer is present between roughly 50 and 60 km, with its variability in latitude and local time assessed by observation, with a thicker layer at high latitude and at night. One question that remains unclear is how this turbulence mixes momentum, heat, and chemical species. Especially, the impact of the strong vertical mixing on the chemistry of this region is still unknown. To investigate this topic, we use a convection‐resolving model coupled for the first time with a realistic chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. 1D and global circulation models use the so‐called vertical eddy diffusion approach to represent turbulent motion, quantified in our model and underestimated in chemistry models. The small‐scale turbulence in the cloud layer causes a variation in the altitude of the top and bottom boundaries of the cloud. Our model shows that the impact of turbulence on cloud chemistry corresponds well to what has been observed by satellites. In the future, the EnVision mission will be able to observe chemical species at the small turbulence scales. Key Points: Estimation for the first time of the spatial and temporal variability of chemical species due to vertical mixingQuantification of the vertical eddy diffusion coefficient, order of magnitude above typical used valuesCloud‐top altitudes change by 0.2–1 km due to vertical convective mixing and gravity waves [ABSTRACT FROM AUTHOR]

Aim: The rapidly changing Arctic is ideal for investigating uncertainties in climate projections. Despite the challenges of collecting data in this region, an unprecedented large‐scale survey of shorebirds has been conducted over the last 30 years. Our study aimed to (1) develop probabilistic estimates for the change in suitable habitat for 10 Arctic shorebird species in Canada by 2075 and (2) assess the contribution of modelling decisions to the uncertainty in these estimates. Location: Arctic Canada. Methods: To evaluate uncertainty, we considered six classes of modelling decisions, yielding 216 unique projections for each species. We tested three decisions that are less commonly explored − the pool of candidate variables, a method for selecting variables, and the maximum distance of tree line dispersal, as well as the modelling algorithm, carbon emissions scenario, and global circulation model. We used a bootstrapping approach, creating a probability distribution for the proportional change in suitable habitat for each species. Results: Our findings indicated a substantial risk for 8/10 species to lose over half of their suitable breeding habitat, but this projection is much less certain than has been described previously. While much uncertainty is unexplained, we were surprised that the largest source of uncertainty among our modelling decisions was from our choice of methods for variable selection, that the other modelling decisions were relatively small sources of uncertainty, overshadowing other modelling decisions. Main Conclusions: While most scenarios predict a northward shift and significant habitat loss for Arctic‐breeding shorebirds, the Arctic Archipelago of Canada will remain an important refuge because in many other Arctic regions, there is no land farther north for these species to shift into. A comprehensive understanding of uncertainty is important for deciding if future projections can or should be used when planning climate‐resilient protected area networks. [ABSTRACT FROM AUTHOR]

The non‐local model of mixing based on internal wave breaking, IDEMIX, is implemented as an enhancement of a turbulent kinetic energy closure model in three non‐eddy resolving general circulation ocean models that differ in the discretization and choice of computational grids. In IDEMIX internal wave energy is generated by an energy flux resulting from near‐inertial waves induced by wind forcing at the surface, and at the bottom, by an energy flux that parameterizes the transfer of energy between baroclinic and barotropic tides. In all model simulations with IDEMIX, the mixing work is increased compared to the reference solutions without IDEMIX, reaching values in better agreement with finestructure observations. Furthermore, the horizontal structure of the mixing work is more realistic as a consequence of the heterogeneous forcing functions. All models with IDEMIX simulate deeper thermocline depths related to stronger shallow overturning cells in the Indo‐Pacific. In the North Atlantic, deeper mixed layers in simulations with IDEMIX are associated with an increased Atlantic overturning circulation and an increase of northward heat transports toward more realistic values. The response of the deep Indo‐Pacific overturning circulation and the weak bottom cell of the Atlantic to the inclusion of IDEMIX is incoherent between the models, suggesting that additional unidentified processes and numerical mixing may confound the analysis. Applying different tidal forcing functions leads to simulation differences that are small compared to differences between the different models or between simulations with IDEMIX and without IDEMIX. Plain Language Summary: Waves in the ocean interior play a fundamental role for ocean dynamics since they can carry energy over long distances and, once they break, lead to turbulent mixing. This turbulent mixing can cause dense water masses to rise from the deep ocean with a direct impact on large‐scale currents. The wave dynamics occur on spatial scales that cannot be resolved in global ocean or climate models. To account for these processes, we apply the new parameterization IDEMIX that describes internal wave generation, propagation, and mixing. Using three different ocean models with and without IDEMIX ensures that we can identify model‐specific effects of the parameterization and discriminate them from those independent of the model. We find that the simulated mixing patterns agree better with observations once IDEMIX is applied. Large‐scale currents and the vertical temperature distribution are substantially affected by the internal wave parameterization. Whether this leads to an improved agreement with observed currents and water mass properties depends on the specific model and on numerical effects. In most cases, simulations with IDEMIX are not very sensitive to details of how the internal wave model is driven by tidal energy input. Key Points: the IDEMIX closure for a consistent representation of internal wave‐induced mixing is evaluated in three state‐of‐the‐art ocean modelsonly in simulations with IDEMIX can the models reproduce the magnitude and spatial variability of the observed mixing workmost changes with IDEMIX can be attributed to stronger mixing, but some effects are confounded by other processes and numerical mixing [ABSTRACT FROM AUTHOR]

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Plain Language Summary: To anticipate future climate change, it is crucial to detect and understand the impacts of human activities. However, distinguishing the effects of anthropogenic forcing from natural climate variations in observational data is challenging. Natural climate variability, known as internal variability, arises from the chaotic nature of atmospheric and oceanic circulation, and from the interactions among the ocean, atmosphere, and land. Here, a novel approach is introduced to distinguish the changes caused by human activities from internal variability. It is applied to the surface air temperature evolution from 1901 to 2020. This method uses an artificial neural network designed to separate the internal from the human‐induced variability. An unprecedented number of climate model simulations are used, enabling precise estimation of human‐forced variability in these climate models. The spatio‐temporal variations are distinguished by applying a well‐known methodology previously used to remove noise from images. The method's performance is evaluated, revealing errors regarding the internal variability that are typically one‐fourth of the actual variations. Regions with important internal variability or with low agreement among models exhibit the largest errors. Overall, the skills are comparable to other existing approaches, but improvements are anticipated. Key Points: We present a new method to separate the forced and internal variability of the surface air temperatureWe utilize a U‐Net trained with global climate models outputs and implement a noise to noise methodology to eliminate internal variabilityThe results are assessed through the utilization of very large ensemble simulations of two distinct climate models [ABSTRACT FROM AUTHOR]

A single column model with parameterized large‐scale (LS) dynamics is used to better understand the response of steady‐state tropical precipitation to relative sea surface temperature under various representations of radiation, convection, and circulation. The large‐scale dynamics are parametrized via the weak temperature gradient (WTG), damped gravity wave (DGW), and spectral weak temperature gradient (Spectral WTG) method in NCAR's Single Column Atmosphere Model (SCAM6). Radiative cooling is either specified or interactive, and the convective parameterization is run using two different values of a parameter that controls the degree of convective inhibition. Results are interpreted in the context of the Global Atmospheric System Studies ‐Weak Temperature Gradient (GASS‐WTG) Intercomparison project. Using the same parameter settings and simulation configuration as in the GASS‐WTG Intercomparison project, SCAM6 under the WTG and DGW methods produces erratic results, suggestive of numerical instability. However, when key parameters are changed to weaken the large‐scale circulation's damping of tropospheric temperature variations, SCAM6 performs comparably to single column models in the GASS‐WTG Intercomparison project. The Spectral WTG method is less sensitive to changes in convection and radiation than are the other two methods, performing qualitatively similarly across all configurations considered. Under all three methods, circulation strength, represented in 1D by grid‐scale vertical velocity, is decreased when barriers to convection are reduced. This effect is most extreme under specified radiative cooling, and is shown to come from increased static stability in the column's reference radiative‐convective equilibrium profile. This argument can be extended to interactive radiation cases as well, though perhaps less conclusively. Plain Language Summary: Single column models, as the name suggests, only model the vertical dimension of the atmosphere. They are simpler than full‐scale 3D global circulation models, but nonetheless play an important role in model development and in better understanding physical phenomena. We use NCAR's Single Column Atmosphere Model (SCAM6) to better understand tropical rainfall. In a single column model, the atmospheric wind coming from other locations (the large‐scale circulation) must be either specified or approximated using a parameterization. We implement three different parameterizations of the large‐scale circulation into SCAM6 and assess how SCAM6 responds to various changes while using these parameterizations. The Global Atmospheric System Studies‐Weak Temperature Gradient (GASS‐WTG) Intercomparison project assessed the performance of various other single column models using two parameterizations of the large‐scale circulation. We fold SCAM6 into this Intercomparison, and we find that SCAM6 performs substantially differently to its peers under the settings used in the GASS‐WTG Intercomparison project, but more comparably to its peers when key circulation parameters are relaxed. Another notable finding is that, across all three parameterizations of large‐scale dynamics, circulation strength decreases when we reduce SCAM6's barrier to convection. Key Points: We use three different methods to parameterize the large‐scale (LS) dynamics in NCAR's single column atmospheric model (SCAM6)As in the Global Atmospheric System Studies Intercomparison, we test SCAM6's response to various boundary conditions and model parametersUnder all three methods, circulation strength is decreased when barriers to convection are reduced [ABSTRACT FROM AUTHOR]