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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]

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An international joint research project, entitled Interhemispheric Coupling Study by Observations and Modelling (ICSOM), is ongoing. In the late 2000s, an interesting form of interhemispheric coupling (IHC) was discovered: when warming occurs in the winter polar stratosphere, the upper mesosphere in the summer hemisphere also becomes warmer with a time lag of days. This IHC phenomenon is considered to be a coupling through processes in the middle atmosphere (i.e., stratosphere, mesosphere, and lower thermosphere). Several plausible mechanisms have been proposed so far, but they are still controversial. This is mainly because of the difficulty in observing and simulating gravity waves (GWs) at small scales, despite the important role they are known to play in middle atmosphere dynamics. In this project, by networking sparsely but globally distributed radars, mesospheric GWs have been simultaneously observed in seven boreal winters since 2015/16. We have succeeded in capturing five stratospheric sudden warming events and two polar vortex intensification events. This project also includes the development of a new data assimilation system to generate long‐term reanalysis data for the whole middle atmosphere, and simulations by a state‐of‐the‐art GW‐permitting general circulation model using the reanalysis data as initial values. By analyzing data from these observations, data assimilation, and model simulation, comprehensive studies to investigate the mechanism of IHC are planned. This paper provides an overview of ICSOM, but even initial results suggest that not only GWs but also large‐scale waves are important for the mechanism of the IHC. Plain Language Summary: In the late 2000s, an interesting form of the coupling between the Northern and Southern Hemispheres was discovered: when the winter polar stratosphere warms, the upper summer mesosphere also warms several days later. An international research project called Interhemispheric Coupling Study by Observations and Modelling (ICSOM) is ongoing to examine the mechanism of this interhemispheric coupling (IHC). This IHC phenomenon is thought to be the connection in the middle atmosphere (i.e., stratosphere, mesosphere, and lower thermosphere). Several promising mechanisms have been proposed, but they remain controversial. This is because gravity waves (GWs) having small scales, which are difficult to observe and simulate, are thought to play a crucial role in the coupling. So, we have performed observations of GWs by networking radars over seven Northern Hemisphere winters, and succeeded in capturing five stratospheric warming events and two opposite events. We also developed a new data assimilation system for the entire middle atmosphere and used the global data produced by the system to simulate GWs with a high‐resolution global model. By combining these research tools, we plan to elucidate the mechanism of IHC comprehensively. This paper presents an overview of ICSOM. Initial results show that not only GWs but also large‐scale waves are important for the IHC mechanism. Key Points: An international project is ongoing to elucidate the mechanism of interhemispheric coupling (IHC) in the middle atmosphereGravity waves (GWs), which are thought to play a key role in IHC, were observed by a radar network and simulated by high‐resolution global modelInitial results suggest that not only GWs but also large‐scale waves are important for the IHC mechanism [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]

In this paper, we constructed the Exospheric Temperature Models (ETM) on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs). The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. It was found that all these three models can provide good specification of thermospheric density including day‐night, seasonal, and latitudinal variations. However, the ETM based on CHAMP and GRACE data gives a better performance in modeling the Equatorial Thermospheric Anomaly and the Midnight Density Maximum features than the MSIS‐ETM and TIEGCM‐ETM. Specifically, independent SWARM‐C data comparison showed that the Relative Deviations and corresponding Root‐Mean‐Square‐Errors of our Texo models are less than 8.9% and 22.8%, much better than the MSIS‐00 model. Plain Language Summary: Exospheric temperature, the atmospheric temperature at an altitude above 300 km, is a critical parameter used in most thermospheric density models. In this study, we use different mathematical functions to construct the exospheric temperature models (ETM) named ETM, MSIS‐ETM, and Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM)‐ETM, respectively based on the CHAMP and GRACE density observations. The mathematical functions can be derived either using the data‐driven method or using the model‐driven method. Our purpose is to evaluate the performance of these constructed models in reflecting the thermosphere, and we found that all these models can provide good specification of the thermosphere such as the typical day‐night, seasonal, and latitudinal variation characteristics. Overall, the ETM constructed by using the data‐driven method gives a better performance than the MSIS‐ETM and TIEGCM‐ETM, and it can effectively simulate the physical features that are observed in the thermosphere. Our results indicate that compared with the model‐driven method utilizing the sufficient data‐driven PCA method to bend the exospheric temperature model is also an effective way. Key Points: Exospheric Temperature Models (ETM) are constructed on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs)The three ETM models can provide good specification of thermospheric density after they are embedded in the MSISThe ETM with EOFs directly from the observations gives a better performance than those with EOFs obtained from the MSIS and Thermosphere Ionosphere Electrodynamics General Circulation Model models [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]

A negative ionospheric storm occurred over the North American sector on 4 November 2021. By the integration of hemispheric power (HP), Ionospheric Connection Explorer (ICON), Global‐scale Observations of the Limb and Disk and global navigation satellite system total electron content (TEC) observations, a complete observation chain is obtained for the first time. At 07:11 UT, prominent energy was input into high latitudes. From 11:51 to 15:49 UT, strong southwestward neutral wind was observed by ICON over the south of North America, followed by significant ∑O/N2 depletion (60%) and temperature enhancement (∼300 K at ∼150 km). TEC depletion started at 12:00 UT in the northeastern North America and expanded to most of the United States. The TEC reduction showed the latitudinally tilted structure similar to that of ∑O/N2 depletion and temperature enhancement, which was shaped by the southwestward neutral wind. The thermosphere‐ionosphere‐electrodynamics general circulation model (TIEGCM) reproduced the observations qualitatively and was utilized to investigate the details of the negative storm generation and evolution processes. Strong equatorward wind and ∑O/N2 depletion reached the North America at midnight, much earlier than the TEC depletion, which began at sunrise. At 90°W, the time delay between TEC and ∑O/N2 depletions increased from ∼3.3 hr at 30°N to ∼11 hr at 60°N. Several factors contributed to the time delay including the time difference between midnight and sunrise, ∑O/N2 transport direction, westward wind and the variation of solar elevation angle with latitude. Plain Language Summary: During geomagnetic storms, the increase/decrease of ionospheric plasma density during geomagnetic storms relative to geomagnetic calm period is called positive/negative ionospheric storms. Due to the lack of observations of thermospheric properties, there are very few studies on negative storms over middle latitudes using direct neutral wind and temperature observations. This paper investigates the negative storm over the North American sector on 4 November 2021. A complete observation chain of the negative storm generating processes is presented by using hemispherical power index, neutral wind, neutral temperature and composition, and total electron content (TEC) measurements. Strong southward and westward neutral wind were observed by Ionospheric Connection Explorer satellite over middle and low latitudes. Global‐scale Observations of the Limb and Disk satellite also recorded prominent thermospheric composition changes and temperature enhancement. Subsequently, TEC depletion occurred was consistent with that of neutral temperature and composition variations. A numerical model is employed to uncover the details of the processes. Thermospheric composition changes occurred between 40° and 60°N firstly at 06:00 UT in the North America sector while TEC depletion appeared at 12:00 UT. There were four factors contributed to the time lag between TEC and composition changes including the time difference between midnight and sunrise, composition transport, westward wind and solar elevation angle. Key Points: During a negative storm, strong southwestward neutral winds and prominent ∑O/N2 depletion were observed by Ionospheric Connection Explorer and Global‐scale Observations of the Limb and Disk, respectivelyA complete observation chain of the negative ionospheric storm generation at middle latitudes is first established for the first timeThe time delay between TEC and ∑O/N2 depletions increased from 3.3 hr at 30°N to ∼11 hr at 60°N [ABSTRACT FROM AUTHOR]

In this study, we used observations from multiple instruments (all‐sky imager [ASI], digisonde, and Beidou geostationary satellite global positioning system receivers) to show the occurrence and evolution of two plasma density enhancement structures (PDESs) during the night of 8 February and 4 November 2018. The PDESs were elongated from northwest to southeast. Their zonal scales were about 200–400 km and longitudinal ranges were about 800–1,400 km. Within the PDES, the plasma density can be enhanced by about two times over the background density. The PDESs appeared before midnight and gradually dissipated after midnight. Plasma density depletion structures appeared within the PDES at the later stage of PDES. Each PDES event lasted for over 4 hours. The morphology and evolution of the PDESs presented in this paper are different from those reported in previous studies. In addition, based on the thermosphere‐ionosphere‐electrodynamics general circulation model simulations and digisonde observations, we proposed a possible cause for the PDESs in the events, in which the occurrence and evolution of PDES are related to the variations of thermospheric meridional winds in the equatorial region. Plain Language Summary: Ionospheric plasma irregularities or disturbances are frequently observed in the equatorial region and have been the focus of ionospheric research. These irregularities include plasma bubbles, traveling ionospheric disturbances, and plasma density enhancements. They have a great impact on the trans‐ionospheric radio wave signals. Most of the previous studies show that the plasma density enhancements occur over large scales and the occurrence of plasma density enhancements is usually accompanied by plasma density depletions in the adjacent areas, and hence, the formation of the density enhancement is suggested to be related to the plasma density depletion. In this study, two unique plasma density enhancement structures (PDESs) were observed using multiple instruments. The observed plasma density enhancements were elongated from northwest to southeast, which is different from previous studies. This paper presents the full evolution of the plasma density enhancement from its appearance to disappearance. In addition, we proposed a possible formation process for the equatorial plasma density enhancement in which the occurrence and evolution of PDES are related to the variations of thermospheric meridional winds in the equatorial region. Our results provide new insight into the dynamics and variability of the coupled thermosphere and ionosphere system in the equatorial region. Key Points: Evolution of equatorial ionospheric plasma density enhancements from occurrence to disappearance was illustrated with multiple data setsThe plasma density enhancement structures (PDESs) were elongated from northwest to southeast, with their sizes changing with time, and they produced Spread F in ionogramsVariations in neutral winds may contribute to the occurrence of the observed PDES [ABSTRACT FROM AUTHOR]

HDO and the D/H ratio are essential to understand Mars past and present climate, in particular with regard to the evolution through ages of the Martian water cycle. We present here new modeling developments of the HDO cycle with the Laboratoire de Météorologie Dynamique Mars Global Climate Model (GCM). The present study aims at exploring the behavior of the D/H ratio cycle and its sensitivity to the modeling of water ice clouds and the formulation of the fractionation by condensation. Our GCM simulations are compared with observations provided by the Atmospheric Chemistry Suite (ACS) on board the ESA/Roscosmos Trace Gas Orbiter (TGO), and reveal that the model quite well reproduces the temperature and water vapor fields, which offers a good basis for representing the D/H ratio cycle. The comparison also emphasizes the importance of modeling the state of supersaturation, resulting from the microphysical processes of water ice clouds, to correctly account for the water vapor and the D/H ratio of the middle‐to‐upper atmosphere. This work comes jointly with a detailed comparison of the measured D/H profiles by TGO/ACS and the model outputs, conducted in the companion paper of Rossi et al. (2022, https://doi.org/10.1029/2022JE007201) (this issue). Plain Language Summary: The D/H ratio observed in a planetary atmosphere is traditionally used as a proxy to estimate the initial water reservoir of the planet. We present here an improved global circulation model including HDO, the main isotope of water on Mars. The updated model takes into account the details of the formation of clouds and their radiative effect. It also includes the effect of photochemistry on HDO and deuterated species. We find that supersaturation is key to the representation of the D/H cycle by making the hygropause more porous, allowing more HDO in the upper atmosphere. It also reduces the efficiency of the isotopic fractionation occurring at condensation. We present here comparisons between observations by the Atmospheric Chemistry Suite spectrometer onboard the orbiter TGO. While the model is globally in agreement with the observations, the inability of the model to reproduce the observed vertical distribution of dust, especially during the Global Dust Storm, causes discrepancies in the representation of water vapor. The model reveals the importance of representing the state of supersaturation to correctly account for the water vapor amount reaching the top of the atmosphere and estimate the D/H ratio at escape. Key Points: The HDO cycle has been implemented in the last version of the Laboratoire de Météorologie Dynamique Mars Global Climate Model including microphysics and radiative effect of water ice cloudsKinetics effect is now included in HDO fractionation during condensation and proves to be significantSupersaturation, the presence of which is regulated by cloud processes, alters significantly the relative abundances of HDO and H2O [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]

When solar wind and interplanetary magnetic field (IMF) disturb, thermospheric winds change accordingly. Among the momentum forces driving high‐latitude thermospheric winds, ion drag is supposed to greatly affect wind variations through ion‐neutral coupling when abrupt and strong changes in ion drifts occur. However, due to the great inertia of thermospheric winds it needs a certain period of time for the wind changes to be prominent both in speed and direction. How long the neutral winds take to change from one steady state to another through the ion‐neutral coupling process is currently still a controversial issue. In this paper, we examine the high latitudinal ion‐neutral coupling time scale based on the Thermosphere Ionosphere Electrodynamics General Circulation Model simulations, which can determine whether wind variations are dominantly driven by ion drag by analyzing the relative contribution of each momentum force. It is found that the spatial variation of ion‐neutral coupling time scale is primarily determined by local electron density, but also varies with neutral density and ion‐neutral collision frequency. Simulations during periods of medium solar activity at ∼250 km altitude show that the ion drag‐dominated region is generally located at the dayside convection inverse boundary and the coupling time scale (e‐folding time) is ∼1 hr when IMF By is the dominant component of the IMF and changes direction. Meanwhile, the southward component of IMF Bz enlarges the ion drag‐dominated region. When IMF Bz is southward with a large magnitude, ion drag‐dominated region is primarily located in the nightside auroral oval with ∼2 hr coupling time scale. Key Points: The spatial variation of ion‐neutral coupling time scale depends primarily on local electron densityThe ion drag dominant region is located at the dayside convection cell boundary (nightside auroral oval) when interplanetary magnetic field (IMF) By (negative Bz) disturbsWhen wind variation is greatly affected by IMF By, the concurrent negative shift of IMF Bz obviously enlarges the ion drag dominant region [ABSTRACT FROM AUTHOR]

This paper introduces the new Venus global ionosphere‐thermosphere model (V‐GITM) which incorporates the terrestrial GITM framework with Venus‐specific parameters, ion‐neutral chemistry, and radiative processes in order to simulate some of the observable features regarding the temperatures, composition, and dynamical structure of the Venus atmosphere from 70 to 170 km. Atmospheric processes are included based upon formulations used in previous Venus GCMs, several augmentations exist, such as improved horizontal and vertical momentum equations and tracking exothermic chemistry. Explicitly solving the momentum equations allows for the exploration of its dynamical effects on the day‐night structure. In addition, V‐GITM's use of exothermic chemistry instead of a strong heating efficiency accounts for the heating due to the solar EUV while producing comparable temperatures to empirical models. V‐GITM neutral temperatures and neutral‐ion densities are compared to upper atmosphere measurements obtained from Pioneer Venus and Venus Express. V‐GITM demonstrates asymmetric horizontal wind velocities through the cloud tops to the middle thermosphere and explains the mechanisms for sustaining the wind structure. In addition, V‐GITM produces reasonable dayside ion densities and shows that the neutral winds can carry the ions to the nightside via an experiment advecting O2+ ${\mathrm{O}}_{2}^{+}$. Plain Language Summary: We present a new, state‐of‐the‐art Venus global circulation model, the Venus global ionosphere thermosphere model. The new model will be useful for answering unknown questions about Venus' atmosphere, the physics of CO2 rich planets, and Venus missions utilizing the aerobraking maneuver. We performed many model simulations and compared the results to some of the existing Venus data sets to assess the accuracy of the model. The results showed that the dayside extreme ultraviolet heating can be captured using the dominant ion‐neutral chemistry rather than relying on simplified legacy methods. Furthermore, the cloud layer below the thermosphere has a unique wind pattern and its impact on the thermospheric temperatures and winds were explored. This work also revealed that the thin nightside ionosphere forms as a result of the transport of a single ion species. Key Points: A new, non‐hydrostatic Venus ionosphere‐thermosphere model is introduced with new physics not previously included in Venus GCMsSimulations during solar minimum conditions are used for data‐model comparisons of the temperatures, plasma, and neutral densitiesThe influence of the retrograde superrotating zonal flow is explored in relation to how it affects the neutral temperature and velocities [ABSTRACT FROM AUTHOR]

This paper examines spectra of horizontal kinetic energy (HKE) in the surface and sub‐surface ocean, with an emphasis on internal gravity wave (IGW) motions, in global high‐resolution ocean simulations. Horizontal wavenumber‐frequency spectra of surface HKE are computed over seven oceanic regions from two global simulations of the HYbrid Coordinate Ocean Model (HYCOM) and three global simulations of the Massachusetts Institute of Technology general circulation model (MITgcm). In regions with high IGW activity, high surface HKE variance in the horizontal wavenumber‐frequency spectra is aligned along IGW linear dispersion curves. For both HYCOM and MITgcm, and in almost all regions, finer horizontal resolution yields more energetic supertidal IGW continuum spectra. The ratio of high‐horizontal‐wavenumber variance in semi‐diurnal and supertidal motions relative to lower‐frequency motions, a quantity of great interest for swath altimetry, depends on the model employed and the horizontal resolution within the model, implying that quantitative predictions of the partition between low‐ and high‐frequency motions taken from particular simulations should be treated with care. The frequency‐vertical wavenumber spectra, frequency spectra, and vertical wavenumber spectra from the models are compared to spectra computed from McLane profilers at nine locations. In general, MITgcm spectra match the McLane profiler spectra more closely at high frequencies (|ω| > 4.5 cpd). In both models, vertical wavenumber spectra roll off more steeply than observations at high vertical wavenumbers (m > 10−2 cpm). The vertical wavenumber spectra in such models is an important target for improvement, due to turbulence production and dissipation that takes place at high vertical wavenumbers. Plain Language Summary: Recently, a small but growing number of global ocean models have begun to employ simultaneous tidal and atmospheric forcing. At the same time, increasing supercomputer power has allowed for simulations of oceanic motions with increasing accuracy, increasing feature (spatial) resolution, and more frequent time slices. Global ocean models with fine grid spacing, and simultaneous tidal and atmospheric forcing, host a vigorous spectrum of high‐frequency waves that control mixing over most of the ocean water column, and are important for many operational oceanography challenges. As an example of the latter, high‐resolution global internal wave models have been used to study the relative partition of high‐frequency versus low‐frequency motions at the small horizontal scales that will be measured by the new Surface Water Ocean Topography mission. The partition described above depends on the model employed and the grid spacing employed within that model, meaning that conclusions about the partition are dependent on the model used to estimate it. Comparisons between the models and vertically profiling instruments indicate that resolving fine scale motions in the vertical direction, where ocean mixing takes place, is not yet handled well by the models. Modeling of fine‐vertical scale motions is therefore an important future research direction. Key Points: Vertical wavenumber spectra of internal gravity wave kinetic energy in two high‐resolution global models are compared to observed spectraModels under‐estimate motions at high vertical wavenumbers (small vertical scales), flagging this as a target for model improvementThe ratio of high‐ versus low‐frequency surface kinetic energy at small horizontal scales is dependent on the model and grid spacings employed [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]

Despite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food‐webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp is impacted by phytoplankton size, cell composition, and non‐algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing bbp algorithms by applying them to bbp data from the BGC‐Argo array in surface waters (<10 m). We find a bbp threshold where estimated Cphyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from bbp‐based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error‐estimations show that bbp‐based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions. Plain Language Summary: Phytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering. In this paper we examine how well backscattering can be used to estimate phytoplankton. To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans. We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by ∼20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data. Key Points: Phytoplankton carbon bbp‐based algorithms can differ up to an order of magnitude at low bbp valuesAn algorithm fitted to a global model output shows biases ranging between 15% and 40% in most regionsMost uncertainties are due to the relative contribution of phytoplankton to total bbp [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]

Using the 5 km coupled general circulation model ICON, the surface internal wave energy source, crucial for the oceanic circulation, is quantified as the wind‐induced wave energy flux that radiates from the mixed layer bottom (MLB) into the ocean interior. Our result lowers the previous estimates of the wind power input to surface near‐inertial motions from up to more than 1 TW down to about 0.23–0.27 TW, depending on season. We point out that the estimate of the wind input to ocean depends not only on the wind stress used—as suggested by previous studies—but also on the ocean model used. While the surface currents in a slab ocean model or a non‐eddying ocean circulation model are strongly determined by the wind forcing, the surface currents in the 5 km ICON model can be more strongly determined by internal instability process (eddy) than by wind stress forcing from less‐extreme weather disturbances. The resulting more or less random alignment of surface current and wind stress can presumably lead to a lower wind input to surface near‐inertial motions. Of the surface wave energy source, about 30% is fluxed down into the interior ocean. This percentage roughly doubles those from previous studies, due to the stronger wave energy flux related to stronger inertial waves generated by the tropical cyclones simulated by the 5 km ICON model. Overall, the low wind input at near‐inertial frequencies produces a wind‐induced wave energy source at the MLB that is well below 0.1 TW. Plain Language Summary: For maintaining the oceanic overturning circulation, energy is needed to mix the dense water up and light water down. The main energy source for mixing arises from breaking of internal waves. A considerable portion of this source comes from waves excited by winds at the sea surface. This paper quantifies this wave energy source based on a frontier simulation of a coupled atmosphere‐ocean general circulation model at a horizontal resolution of 5 km. This model is capable to simulate tropical cyclones (hurricanes and typhoons) and less‐extreme small‐scale and short‐living weather disturbances and oceanic mesoscale eddies, which were not represented by the models used in most of the previous studies. Taking these new features into account, we find that the wind‐induced wave energy source is less than 0.1 TW. Key Points: Relatively low wind power input to near‐inertial motions in a 5 km global coupled simulationEnergy flux radiating from the mixed layer bottom as interior wave energy sourceStrong internal waves excited by tropical cyclones simulated by a 5 km global coupled general circulation model [ABSTRACT FROM AUTHOR]

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

This paper examines the changes of El Niño–Southern Oscillation (ENSO) associated winter rainfall relationship in Southeast Asia (SEA) in the historical period (1981–2010) and future period (2071–2100) based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models simulations. Twenty general circulation models (GCMs) are chosen from the Scenario Model Intercomparison Project (ScenarioMIP) highest emission scenario—Shared Socioeconomic Pathway (SSP) 5‐8.5. This study focuses only on the GCMs that have been able to replicate the consistent associated patterns observed, referred to as "teleconnection‐consistent models" (TCMs). During historical period, ENSO has significant negative association with SEA winter rainfall, particularly over Indochina mainland, Maritime Continent (MC) and northern Borneo. The significant negative‐associated teleconnection affected area has increased under future climate, particularly over southern part of South China Sea (SCS). The weakening of south westerlies at Western North Pacific (WNP) centred near 20°N reduces the moisture transport from Indochina into south of China, increase the negative correlated affected area in the SEA regions above equator. The northeasterlies/easterlies that prevailed from the WNP anticyclone (AC) brings significant moisture flux into southern Borneo, causing increment of significant positive‐associated moisture flux convergence to that region. The weakening of upward motion in TCM results in less warm and moist air rising over SEA MC, reducing the rainfall availability. The 1° eastward expansion of WC downward branch eastern edge leads to suppression of convective activities, increasing the dry affected area. The results suggest that the ENSO has a larger influence on the negative‐associated SEA rainfall and atmospheric circulation in the December–January–February (DJF) late‐21st century when compared to the 1980–2010. [ABSTRACT FROM AUTHOR]

We conduct a global assessment of the spatial heterogeneity of cloud phase within the temperature range where liquid and ice can coexist. Single‐shot Cloud‐Aerosol Lidar with Orthogonal Polarization lidar retrievals are used to examine cloud phase at scales as fine as 333 m, and horizontal heterogeneity is quantified according to the frequency of switches between liquid and ice along the satellite's path. In the global mean, heterogeneity is greatest between −15 and −4°C with a peak at −5°C, when small patches of ice are prevalent within liquid‐dominated clouds. Heterogeneity "hot spots" are typically found over the extratropical continents, whereas phase is relatively homogeneous over the Southern Ocean and the eastern subtropical ocean basins, where supercooled liquid clouds dominate. Even at a fixed temperature, heterogeneity undergoes a pronounced annual cycle that, in most places, consists of a minimum during autumn or winter and a maximum during spring or summer. Based on this spatial and temporal variability, it is hypothesized that heterogeneity is affected by the availability of ice nucleating particles. These results can be used to improve the representation of subgrid‐scale heterogeneity in general circulation models, which has the potential to reduce longstanding model biases in cloud phase partitioning and radiative fluxes. Plain Language Summary: At temperatures where ice and liquid can coexist within clouds, climate models tend to produce too much ice and too little liquid compared to satellite observations. This bias is likely caused by the assumption that liquid and ice are uniformly mixed, which results in the rapid conversion of liquid to ice for thermodynamic reasons. To reduce this bias, models need to account for the spatial heterogeneity ("patchiness") of liquid and ice that exists in the real atmosphere. The goal of this paper is to quantify this spatial heterogeneity using satellite‐based lidar observations of cloud phase. We find small pockets of ice in liquid‐dominated clouds to be more common than small pockets of liquid in ice‐dominated clouds. The greatest heterogeneity is found over the midlatitude continents, whereas phase is relatively uniform over the Southern Ocean and other maritime regions with extensive low cloud cover. In the mid and high latitudes, cloud phase tends to be more heterogeneous during spring and summer and more homogeneous during autumn and winter. These results can be used in the future to improve model representations of the thermodynamic processes responsible for biases in cloud phase. Key Points: Cloud phase heterogeneity is greatest at −5°C, when small ice patches form in majority‐liquid cloudsCloud phase is relatively homogeneous over the Southern Ocean and heterogeneous over the northern continentsFor a fixed temperature, extratropical phase heterogeneity is generally greatest during local spring and summer [ABSTRACT FROM AUTHOR]

The latitudinal location of the Equatorial Ionization Anomaly (EIA) crest has seasonal variation, and there are disagreements on the interpretation of such seasonal characteristic in previous studies. Some studies suggested that this seasonal characteristic is determined by the seasonal characteristic of the equatorial electric field. Others suggested that this seasonal characteristic is determined by the seasonal changes of the thermospheric wind. The current paper uses Total Electron Content (TEC) data and the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) to analyze the seasonal variation of the northern EIA crest in the eastern Asian sector under low solar activity. Our results show that the monthly averaged latitudinal location of the northern EIA crest has a good linear relationship (r = 0.74) with the monthly averaged Equatorial Electrojet (EEJ) intensity, which is a commonly used proxy of the eastward electric field. However, TIEGCM simulations with and without F‐region wind indicate that such a relationship might be attributed to wind effects. Additionally, the linear relationship between the EEJ intensity and the northern‐southern EIA crest distance is not significant (r = 0.47) in the eastern Asian sector. Our results suggest that a good correspondence between the eastward electric field and the latitudinal location of the EIA crest is not assured annually, as the seasonally varying F‐region wind significantly influences EIA evolution. Key Points: Variations of the EIA crest latitude in the east Asian sector are analyzed with TEC data and TIEGCM simulationEEJ intensity and northern EIA crest latitude have a good linear relationship but might be attributed to F‐region meridional wind effectWind effect on the EIA evolution cannot be neglected on an annual timescale [ABSTRACT FROM AUTHOR]

This paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) events yield precipitation deficits and ensuing droughts, often damaging regional forests, in many parts of the world. The relative roles of ENSO, other natural climate changes, and anthropogenic factors on the forest clearing of Easter Island over the last millennium are still debated. Here, we analyze Easter Island precipitation changes using in situ, satellite‐derived and reanalysis products spanning the last 4–7 decades, and 46 monthly 156‐year‐long (1850–2014) simulations derived from 25 CMIP5 and 21 CMIP6 (Coupled Model Intercomparison Project phases 5 and 6) General Circulation Models. Our analysis shows that La Niña events, the cold phases of ENSO, cause precipitation deficits of −0.2 to −0.3 standard deviation (relative to long‐term mean) in all analyzed data types. ENSO‐like events are further examined over the last millennium (850–1981). A new multiproxy reconstruction of the NINO3.4 index based on proxy records from the Past Global Changes 2k database and Random Forest method is produced. Our reconstruction reveals unusual high recurrences of La Niña‐like situations during the fifteenth to seventeenth centuries, which likely induced significant precipitation deficits on the island. These situations are compared to published vegetation reconstructions based on pollen analyses derived from sedimentary cores collected in three island sites. We conclude the environmental consequences of cumulative precipitation deficits over long‐lasting La Niña‐like situations reconstructed here over the fifteenth to seventeenth centuries were likely favoring drought and forest flammability. La Niña events should be better accounted for among the causes of forest clearing on Easter Island. Plain Language Summary: Easter Island is a small remote island located in the south‐eastern Pacific Ocean. It is home of several scientific enigmas: the origin of early settlements, the construction of the giant moai statues, and causes of the forest clearing. Was the forest clearing abrupt, gradual, homogenous in time and space, human‐induced (in line with the ecocide hypothesis), or related to natural climate variability? The question we address in this paper is related to the natural climate variability hypothesis, focusing on the El Niño Southern Oscillation (ENSO) phenomenon. We analyze ENSO effects on Easter Island precipitation from three instrumental (1950–2021) and 42‐simulation data sets (1850–2014). Our analysis shows the cold phases of ENSO, also known as La Niña, cause significant precipitation deficits over the island in all analyzed data types. Then, we provide a new up‐to‐date reconstruction of ENSO over 850–1981. We found a large number of La Niña‐like situations during the fifteenth‐seventeenth centuries. These situations are compared to vegetation reconstructions derived from sedimentary cores collected in three island sites. We conclude that the environmental consequences of cumulative precipitation deficits during long‐lasting La Niña‐like situations could be an additional cause of the forest clearing of Easter Island. Key Points: Analysis of independent data sets shows precipitation deficits over Eastern Island during La Niña events over the period 1850–2021A new reconstruction of El Niño Southern Oscillation episodes reveals a large number of La Niña‐like situations during the fifteenth to seventeenth centuriesHigh repetitions of La Niña events, favoring drought, likely played a role in the forest clearing of the island, on top of other stressors [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]

We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the assimilation coherently combines various data inputs leading to continuous transitions between different datasets. The multiresolution modeling capability is achieved by allocating multiple layers of basis functions with different resolutions. Higher‐resolution fitting results capture more mesoscale (10–100 s km) structures such as auroral arcs, than the low‐resolution ones and the empirical model. To better reconcile different datasets, two preprocessing steps, temporal interpolation of satellite data and spatial down‐sampling of low‐fidelity data, are implemented. The inherent smoothing effect of the fitting, which causes an unrealistic spreading of the aurora, is mitigated by a post processing step: the K Nearest Neighbor (KNN) algorithm. KNN identifies the probability of a region with significant aurora and thereby eliminates those regions with low values. Thereby, this methodology can be used to maintain realistic and mesoscale auroral structures without boundary issues. We then run the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) driven by the high‐ and low‐resolution auroral assimilations and compare total electron contents (TECs). TIEGCM driven by data assimilation produces enhanced TECs by a factor of ∼2 than the one driven by the empirical aurora, and high‐resolution results show mesoscale structures. Our study shows the value of incorporating realistic auroral inputs via assimilation to drive ionosphere‐thermosphere models for better understanding the consequences of mesoscale phenomena. Plain Language Summary: The energy deposition from the magnetosphere determines the storm‐time Ionosphere‐Thermosphere (I‐T) variations. Among different energy inputs, aurora and electric fields are usually prescribed as external drivers for I‐T models. Historically, these drivers are given by empirical models, which miss mesoscale structures. This poses a long‐lasting challenge to understand and predict mesoscale I‐T variations. This paper adopts a Lattice Kriging model to generate data‐driven auroral maps by combining space‐borne and ground‐based datasets. The approach maintains realistic and mesoscale structures while removing the boundary issues between different datasets compared to a simple padding. The methodology is inherently multiresolution, by laying out different levels of basis functions, to naturally decompose the auroral activities into different scales. The I‐T model simulation driven by the assimilation result well captures the location of enhanced total electron contents during a moderate storm due to auroral precipitation. Our work demonstrates the advantage of using data assimilation to understand mesoscale magnetospheric deposition and its impacts. Nevertheless, this assimilation model still falls into the category of data diagnostics, in which the analysis largely depends on the amount and spatial distribution of the available data. It is not a prediction model and cannot be used to infer the temporal evolution of aurora. Key Points: Multiresolution Gaussian process model (Lattice Kriging) is used to assimilate Special Sensor Ultraviolet Spectrographic Imagers and Time History of Events and Macroscale Interactions during Substorms for auroral energy flux and mean energyAuroral assimilation combines various datasets coherently, alleviates boundary issue, and captures real‐time mesoscale structure as observedThermosphere Ionosphere Electrodynamics General Circulation Model driven by the assimilated auroral maps reproduces strong and structured total electron contents, comparing more favorably with the global navigation satellite system observations [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]

The thermal and dynamical structure of Titan's middle atmosphere (the stratosphere and mesosphere) has been observed to evolve over seasonal timescales. Measurements from the Composite Infrared Spectrometer on the Cassini spacecraft indicated the presence of a westerly jet with the strongest winds exceeding 200 m s−1 near the autumn and spring poles. The strength of the winds varied substantially with latitude and altitude, and weakened throughout most of winter. The strong winds also served to trap short‐lived trace molecules near the winter pole, leading to a chemically enriched environment. Here, to better understand the evolution of the middle atmosphere jet, we quantify the heat and zonal momentum budgets in Titan's stratosphere and mesosphere over the course of a Titan year using a three‐dimensional general circulation model. We confirm that the dominant heating balance is between the net radiative and adiabatic heating rates, and also show that the convergence of sensible heat by the atmospheric flow is important at low stratospheric altitudes above the winter pole. We show that the polar jet is maintained by the convergence of zonal momentum by the mean meridional flow, while the low‐latitude winds are maintained by an up‐gradient transport of momentum by eddies that occur on time scales of less than one Titan day. The heat and momentum budgets we determine here will be useful in constraining the factors controlling the evolution of Titan's middle atmosphere over the coming decades. Plain Language Summary: Titan is the largest moon of Saturn, and possesses a thick, nitrogen dominated atmosphere with surface pressure that is greater than Earth's. The middle atmosphere of Titan includes the stratosphere and mesosphere and extends from about 60 km through about 600 km above its surface. Winds in Titan's middle atmosphere can be up to 200 m s−1 (∼450 miles per hour) and play an important role in moving trace molecules throughout the atmosphere. In this paper, we perform the first numerical study to determine what causes the temperature and winds in Titan's middle atmosphere to change over the course of Titan's 29.5 Earth‐year trip around the sun. We find that the temperatures in Titan's middle atmosphere are driven primarily by solar heating and infrared cooling, and by compressional heating of descending air; however, the transport of heat by the winds is also important. The winds are primarily accelerated by the large‐scale transport of momentum toward the high winter latitudes, but are balanced by the transport of momentum toward the equator by atmospheric waves. Our results will be important to predicting the changes that will be seen in Titan's atmosphere as it progresses through northern autumn in 2025. Key Points: Heating in the middle atmosphere is balanced between radiative and adiabatic effects, but convergence of heat is important near the polesThe acceleration of the polar jet is balanced between the transport of momentum by the mean flow and deceleration by transient eddiesThe transition of the polar jet from one hemisphere to the other is a complex process that involves changing structure over all latitudes [ABSTRACT FROM AUTHOR]

Previous studies have shown that solar flares can significantly affect Earth's ionosphere and induce ion upflow with a magnitude of ∼110 m/s in the topside ionosphere (∼570 km) at Millstone Hill (42.61°N, 71.48°W). We use simulations from the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM) and observations from Incoherent Scatter Radar (ISR) at Millstone Hill to reveal the mechanism of ionospheric ion upflow near the X9.3 flare peak (07:16 LT) on 6 September 2017. The ISR observed ionospheric upflow was captured by the TIEGCM in both magnitude and morphology. The term analysis of the F‐region ion continuity equation during the solar flare shows that the ambipolar diffusion enhancement is the main driver for the upflow in the topside ionosphere, while ion drifts caused by electric fields and neutral winds play a secondary role. Further decomposition of the ambipolar diffusive velocity illustrates that flare‐induced changes in the vertical plasma density gradient is responsible for ion upflow. The changes in the vertical plasma density gradient are mainly due to solar extreme ultraviolet (EUV, 15.5–79.8 nm) induced electron density and temperature enhancements at the F2‐region ionosphere with a minor and indirectly contribution from X‐ray (0–15.5 nm) and ultraviolet (UV, 79.8–102.7 nm). Plain Language Summary: Solar flares with a sudden enhancement in solar X‐ray and extreme ultraviolet (EUV) radiation heat the upper atmosphere and increase electron density dramatically, leading to an expansion of Earth's upper atmosphere. Previous observations have shown an upward ion motion of ∼110 m/s at middle latitudes during solar flares; however, the cause of it remains largely speculative. In this paper, we provide a quantitative analysis of the ionospheric ion upflow during the 6 September 2017 X9.3 flare via model‐data comparison. During solar flares, X‐ray and EUV radiation are intensified at different degrees, thus producing different magnitudes of enhancement in ionospheric plasma density and temperature at different altitudes. Enhanced electron density and temperature at ionospheric F‐region due to solar EUV photoionization build up an additional upward pressure gradient force, pushing the plasmas upward. Key Points: Flare‐induced ionospheric ion upflow at midlatitudes was simulated by a 3‐D ionosphere‐thermosphere coupled modelThe midlatitude ion upflow was mainly due to changes in ambipolar diffusion during flaresThe solar extreme ultraviolet (EUV) enhanced electron density and temperature were the primary driver of ambipolar diffusion velocity changes [ABSTRACT FROM AUTHOR]

Numerical simulations using the National Center for Atmospheric Research (NCAR) thermosphere‐ionosphere‐electrodynamics general circulation model (TIE‐GCM) are performed to elucidate the effects of the interplanetary magnetic field (IMF) on the middle thermosphere composition during a "geomagnetically quiet" period from the day of year (DOY) 110–111 in 2019 (when the Auroral electrojet (AE) index never exceeded 300 nT and the Kp never exceeded 2). In particular, this paper aims to examine how the Global‐scale Observations of the Limb and Disk (GOLD) mission observed daytime thermospheric O and N2 column density ratio (∑O/N2) depletion at mid‐latitudes originated under such a "geomagnetically quiet" condition. A comparison of electric potential, Joule heating rate per unit mass, ion velocity, neutral temperature and winds in the middle thermosphere (∼160 km) between real IMF and without the IMF east‐west component (By) indicates that a By dominant condition can enhance their strengths under this "geomagnetically quiet" condition. Consequently, ∑O/N2 depletion with a stronger magnitude (30% compared with ∼8% without By) and larger disturbed area was introduced in the post‐midnight sector at high‐latitudes due to strong and localized upwelling associated with the enhanced Joule heating rate per unit mass. The ∑O/N2 depletion was transported equatorward and corotated from local post‐midnight to early morning, and was observed by GOLD at middle latitudes during daytime. Plain Language Summary: This study utilizes numerical simulations with a climatological tide in the lower boundary to investigate how thermospheric composition disturbance is produced and transported to mid‐latitudes during a geomagnetically quiet period (DOY110‐112, 2019, maximum Kp = 1.7, AE < 300 nT). Through comparing parameters from simulations under real IMF and By = 0, it is found that the IMF By, which was dominant over this period, can significantly enhance the ionospheric potential, Joule heating rate per unit mass, ion drift and neutral winds compared with Bz only condition. As a result, the geomagnetic activity with AE < 300 nT can trigger stronger depletion (30%) of the column density ratio of thermospheric O and N2 (∑O/N2) in the polar region. Then the accelerated meridional wind transports the ∑O/N2 depletion equatorward to mid‐latitudes. The depletion is also transported from local post‐midnight to local early morning. This study shows the crucial role of dominant By during "quiet time". Additionally, it suggests that the response of ∑O/N2 to geomagnetic activity during the "quiet period" is similar to the one during geomagnetic storms. Key Points: IMF with dominant By enhances ion convection pattern, Joule heating rate and neutral windsStrong thermospheric composition disturbance can be generated and propagate into mid‐latitudes with dominant By during "quiet" timeResponse of thermospheric composition to geomagnetic activity with AE < 300 nT is similar to its response to geomagnetic storms [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]

The Earth's magnetosheath and cusps emit soft X‐rays due to the charge exchange between highly charged solar wind ions and exospheric hydrogen atoms. The Lunar Environment Heliospheric X‐ray Imager and Solar wind Magnetosphere Ionosphere Link Explorer missions are scheduled to image the Earth's dayside magnetosphere system in soft X‐rays to investigate global‐scale magnetopause reconnection modes under varying solar wind conditions. The exospheric neutral hydrogen density distribution, especially the value of this density at the subsolar magnetopause is of particular interest for understanding X‐ray emissions near this boundary. This paper estimates the exospheric density during solar minimum using the X‐ray Multimirror Mission (XMM) astrophysics observatory. We selected an event on 12 November 2008 from the XMM data archive, which detects soft X‐rays of magnetosheath origin while solar wind and interplanetary magnetic field conditions are relatively constant. During the event the location of the magnetopause was measured in situ by the THEMIS mission, thus the location of the solar wind ions responsible for the magnetosheath emission is well constrained by observation. We estimated the exospheric density using the Open Geospace Global Circulation Model (OpenGGCM) and a spherically symmetric exosphere model. The ratio of the magnetosheath plasma flux between the OpenGGCM model and the THEMIS, was nearly 1, which means the magnetohydrodynamic model reasonably reproduces the magnetosheath plasma conditions. The OpenGGCM magnetosheath parameters were used to deconvolve soft X‐rays of exospheric origin from the XMM signal. The lower‐limit of the exospheric density of this solar minimum event is 36.8 ± 11.7 cm−3 at 10 RE subsolar location. Key Points: From 11 years of X‐ray Multimirror Mission‐Newton X‐ray observations, we identified 193 intervals that can be used to estimate the exospheric neutral densityWe derived a solar minimum exospheric neutral density from the 12 November 2008 event using a global MagnetoHydroDynamics model and a simplified exosphere modelThe density estimate is 36.8 cm−3 near the subsolar magnetopause and is likely a lower‐limit value [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]

General circulation models (GCMs) are developed to simulate the past climate and generate future climate predictions. In the context of global warming, their important roles in identifying possible solutions to water resources planning/management are recognized by the world. However, in actual and regional implementation, due to many factors like initial and boundary conditions, parameters and model structures and so forth, there are great variabilities and uncertainties across the future climate projections of GCMs outputs, which has attracted criticism from water resources planners. Thus, the GCMs usually must be evaluated for assessing their performances in simulating the historical observations. Currently, there are many different conclusions and opinions as to whether the optimal individual model is more advantageous or whether the combined consideration of MME works better. The purpose of this paper is to compare the advantages and disadvantages between the selection of the optimal single GCM and multi‐model ensemble (MME) based on one of the more objective selection methods called TOPSIS. The results show that the performances of GCMs in simulating precipitation and temperature in different climate subregions over China are not identical. For CMIP6 GCMs simulations, the optimal precipitation GCM is CMCC‐CM2‐SR5 for EC, MIROC6 for SC, SWC and NWC, CESM2‐WACCM for NC and NEC, and FGOALS‐g3 for QTP. As for temperature, only NESM3 and BCC‐ESM1 are the optimal GCM for QTP and NWC, respectively; in other subregions, MME is better than single GCM. In general, a simple arithmetic averaging approach employed to generate the MME model is not superior to the optimal GCM, although the error metric with the observed data is reduced, at the cost of a severe compression of the interannual variability. [ABSTRACT FROM AUTHOR]

In Low Earth Orbit (LEO), atmospheric drag is the largest contributor to trajectory prediction error. The current thermospheric density model used in operations, the High Accuracy Satellite Drag Model (HASDM), applies corrections to an empirical density model every 3 hr using observations of 75+ calibration satellites. This work aims to improve global thermospheric density estimation by utilizing a physics-based space environment model and precise GPS-based orbit estimates of LEO CubeSats. The data assimilation approach presented here estimates drivers of the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) every 1.5 hr using CubeSat GPS information. In this work, Spire Global CubeSat data are used to demonstrate the method using only 10 satellites; the true strength of the method is its potential to exploit data already collected on large LEO constellations (hundreds of CubeSats). Precise Orbit Determination (POD) information from 10 CubeSats over 12 days is used to sense a global density field when Kp historical data show a minor and moderate geomagnetic storm in succession. This paper provides a direct comparison of estimated density, derived by our new method, to HASDM and Swarm mission derived density. A propagation analysis is also executed by comparing the CubeSat POD data to orbits propagated using our estimated density versus HASDM density. The analyses show that the estimated density is within 35% of HASDM during stormtime conditions, and that the propagation using the estimated density yields an improvement of 26% over NRLMSISE-00 compared to HASDM, while outperforming HASDM during the second storm peak. [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]