Search

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]

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model. [ABSTRACT FROM AUTHOR]

Understanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind‐climate, and other wind‐dependent geophysical climatic variables. Understanding the skill of atmosphere‐only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large‐scale spatial patterns and temporal variability of historical surface winds. We show that inter‐model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large‐scale spatial patterns of historical near‐surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter‐annual and inter‐seasonal variability particularly within tropical‐cyclone‐affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon‐cycle dynamics has insignificant effect on simulated surface winds (at decadal time‐scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter‐model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models. [ABSTRACT FROM AUTHOR]

This paper presents a multimodel assessment of fidelity in representation of the diurnal cycle of precipitation over the Maritime Continent (MC). Daily mean precipitation rate and amplitude and phase of the diurnal cycle are utilized to validate General Circulation Model (GCM) performance with respect to multiyear annual mean and seasonal cycle. The analysis shows that models' 6‐hourly temporal resolution and 2.5° spatial resolution is sufficient to depict key characteristic of diurnal precipitation. Results show that most models are sensitive to the existence of the MC and show different characteristics of subdaily precipitation. However, 19 out of 20 models underestimate the daily mean precipitation over the eastern Indian Ocean and most of them underestimate both daily mean precipitation and amplitude of the diurnal cycle, especially over islands within the region. Observations show that within the MC the diurnal cycle is higher over land than over ocean. Many models decrease this land‐sea contrast. Most models recognize difference in phase of the diurnal cycle between MC islands and surrounding ocean, but some of them show opposite phasing of the diurnal cycle. Results show that modern models perform better than past generation, sometimes comparably to regional models' run in much higher resolution. Although models perform well with respect to the seasonal cycle of the daily mean precipitation, they fail to realistically represent the seasonal evolution of the diurnal cycle amplitude. Additionally, models show no coherence in land‐ocean contrast derived from a multiyear average of daily mean precipitation and amplitude of the diurnal cycle. Key Points: GCMs are sensitive to the existence of the Maritime Continent in the Indo‐Pacific warm poolMost models underestimate daily mean precipitation and amplitude of the diurnal cycle, especially over landMost GCMs underestimate land‐sea contrast in precipitation characteristics [ABSTRACT FROM AUTHOR]

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

Abstract: A decadal climate prediction system named as IAP‐DecPreS was constructed in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, based on a fully coupled model FGOALS‐s2 and a newly developed initialization scheme, referred to as EnOI‐IAU. In this paper, we introduce the design of the EnOI‐IAU scheme, assess the accuracies of initialization integrations using the EnOI‐IAU and preliminarily evaluate hindcast skill of the IAP‐DecPreS. The EnOI‐IAU scheme integrates two conventional assimilation approaches, ensemble optimal interpolation (EnOI) and incremental analysis update (IAU). The EnOI and IAU were applied to calculate analysis increments and incorporate them into the model, respectively. Three continuous initialization (INIT) runs were conducted for the period of 1950–2015, in which observational sea surface temperature (SST) from the HadISST1.1 and subsurface ocean temperature profiles from the EN4.1.1 data set were assimilated. Then nine‐member 10 year long hindcast runs initiated from the INIT runs were conducted for each year in the period of 1960–2005. The accuracies of the INIT runs are evaluated from the following three aspects: upper 700 m ocean temperature, temporal evolution of SST anomalies, and dominant interdecadal variability modes, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). Finally, preliminary evaluation of the ensemble mean of the hindcast runs suggests that the IAP‐DecPreS has skill in the prediction of the PDO‐related SST anomalies in the midlatitude North Pacific and AMO‐related SST anomalies in the tropical North Atlantic. [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

1. Plant–soil interactions play a central role in the biogeochemical carbon (C), nitrogen (N) and hydrological cycles. In the context of global environmental change, they are important both in modulating the impact of climate change and in regulating the feedback of greenhouse gas emissions (CO2, CH4 and N2O) to the climate system. 2. Dynamic global vegetation models (DGVMs) represent the most advanced tools available to predict the impacts of global change on terrestrial ecosystem functions and to examine their feedbacks to climate change. The accurate representation of plant–soil interactions in these models is crucial to improving predictions of the effects of climate change on a global scale. 3. In this paper, we describe the general structure of DGVMs that use plant functional types (PFTs) classifications as a means to integrate plant–soil interactions and illustrate how models have been developed to improve the simulation of: (a) soil carbon dynamics, (b) nitrogen cycling, (c) drought impacts and (d) vegetation dynamics. For each of these, we discuss some recent advances and identify knowledge gaps. 4. We identify three ongoing challenges, requiring collaboration between the global modelling community and process ecologists. First, the need for a critical evaluation of the representation of plant–soil processes in global models; second, the need to supply and integrate knowledge into global models; third, the testing of global model simulations against large-scale multifactor experiments and data from observatory gradients. 5. Synthesis. This paper reviews how plant–soil interactions are represented in DGVMs that use PFTs and illustrates some model developments. We also identify areas of ecological understanding and experimentation needed to reduce uncertainty in future carbon coupled climate change predictions. [ABSTRACT FROM AUTHOR]

Planetary‐scale mountain waves have been observed at the cloud top of Venus and throughout the cloud deck. As they propagate from the surface to the cloud layers, multiple observations and numerical simulations have shown that they grow in size and do not break. However, the fate of mountain waves in the transition region and thermosphere, above the super‐rotating atmosphere, has only been addressed with two‐dimensional models. We conduct for the first time a simulation of mountain waves with a state‐of‐the‐art Venus climate model that includes the thermosphere. We find that mountain waves can propagate up to at least 150 km altitude, well above the transition region. They affect the circulation of the transition region, by reducing winds speeds, and the subsolar‐to‐antisolar circulation. Plain Language Summary: The planet Venus rotates very slowly, with a rotation period of 243 days, while its cloud cover rotates westwards much faster, with a period of 4 days at 70 km altitude, a phenomenon known as superrotation, which is not fully explained yet. Higher up, in the thermosphere, the air flows from the dayside to the nightside, rather than westwards. The thermosphere is difficult to explore by remote sounding due to the low air density, and thus its connection to the surface and the superrotating layer is poorly understood. In 2015, the Venus‐orbiting spacecraft Akatsuki detected a massive standing wave generated at the surface and reaching the superrotating layer, despite the fast zonal winds. Here, we show that this wave also reaches the thermosphere. Key Points: The first simulation of mountain waves with a 3D ground‐to‐thermosphere General Circulation Model is performedMountain waves reach the thermosphere of Venus and change the SS‐AS winds by up to 80 m/s, locally in the afternoonThe vertical wind field above the cloud deck is dominated by the effect of mountain waves [ABSTRACT FROM AUTHOR]

Watershed-scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 ( CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)-8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC-8. Two example use cases for the dataset also are presented. [ABSTRACT FROM AUTHOR]

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed ( U2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U2. Consequently, the reference evapotranspiration, modeled by the Penman-Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper explores the evolution of idealized tropical cyclones in the Community Atmosphere Model CAM 5 with the spectral element (SE) dynamical core at grid spacings of 111, 55 and 28 km. Over 10 simulation days the storms become increasingly intense and compact with increasing resolution. The experiments reveal unrealistically strong tropical cyclone intensities of minimum surface pressures ranging from 845 to 865 hPa and absolute maximum wind speeds greater than 100 m s−1 at the highest resolution, especially when small physics time steps are used. This unphysical behavior is related to the manner in which the physics time step is applied in CAM 5. The analysis indicates that the behavior of the physics parameterizations, namely the partitioning between convective and large-scale precipitation, at small time steps contributes to this intensity. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

One way in which global climate model (GCM) output can be utilized to infer local impacts is through the use of synoptic climatology: creating a set of atmospheric patterns that capture the variability in the climate system, and then analyzing trends and variability in the frequency of these patterns moving into the future. In this paper, we demonstrate a new synoptic climatological technique for classifying atmospheric patterns that can be used in conjunction with GCM output data (in this case, the Community Climate System Model 3). We apply this method to 850-hPa temperature patterns over the contiguous United States to derive daily categorizations. A total of 15 clusters are created from the data set; once the mean GCM bias is removed, historical cluster frequencies in the GCM data set are not statistically different from those of the reanalysis data set. In the future, significant changes in frequency are observed across most of the transition season clusters, as they broaden in seasonality at the expense of winter clusters, some of which nearly entirely disappear. Changes are greater moving further into the future, and greater for the more carbon-intensive special report on emissions scenarios (SRES) scenarios (A1FI, A2) than the less-intensive scenario tested (B1). Diagnostics test how well the mean patterns of the reanalysis data set, GCM historical data set and the future GCM data sets resemble each other. For some clusters, mean bias between the historical and future data sets grows substantially by the end of the 21st century under the more carbon-intensive scenarios. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In this paper, downscaling models are developed using various linear regression approaches, namely direct, forward, backward and stepwise regression, for obtaining projections of mean monthly maximum and minimum temperatures ( Tmax and Tmin) to lake-basin scale in an arid region in India. The effectiveness of these regression approaches is evaluated through application to downscale the predictands for the Pichola lake region in the state of Rajasthan in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (i) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948-2000 and (ii) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001-2100. The selection of important predictor variables becomes a crucial issue for developing downscaling models as reanalysis data are based on a wide range of meteorological measurements and observations. A simple multiplicative shift was used for correcting predictand values. Direct regression was found to yield better performance among all other regression techniques for the training data set, while the forward regression technique performed better in the validation data set, explored in the present study. For trend analysis, the Mann-Kendall non-parametric test was performed. The results of downscaling models show that an increasing trend is observed for Tmax and Tmin for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT scenario by using predictors. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climate change impact assessment on water resources with downscaled General Circulation Model (GCM) simulation output is characterized by uncertainty due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). Disagreement between different GCMs and scenarios in regional climate change impact studies indicates that overreliance on a single GCM with a scenario could lead to inappropriate planning and adaptation responses. This paper focuses on modeling GCM and scenario uncertainty using possibility theory in projecting streamflow of Mahanadi river, at Hirakud, India. A downscaling method based on fuzzy clustering and Relevance Vector Machine (RVM) is applied to project monsoon streamflow from three GCMs with two green house emission scenarios. Possibilities are assigned to all the GCMs with scenarios based on their performance in modeling the streamflow of the recent past (1991-2005), when there are signals of climate forcing. The possibilities associated with different GCMs and scenarios are used as weights in computing the possibilistic mean of the CDFs projected for three standard time slices 2020s, 2050s, and 2080s. The result shows that the value of streamflow at which the CDF reaches 1 reduces with time, which shows the reduction in probability of occurrence of extreme high flow events in future. Historic record of monsoon streamflow of Mahanadi river also shows similar decreasing trend, which may be due to the effect of high surface warming. Reduction in Mahandai streamflow is likely to pose a major challenge for water resources engineers in meeting water demands in future. [ABSTRACT FROM AUTHOR]

Hydrologic implications of global climate change are usually assessed by downscaling appropriate predictors simulated by general circulation models (GCMs). Results from GCM simulations are subjected to a number of uncertainties due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). With a relatively small number of GCMs available and a finite number of scenarios simulated by them, uncertainties in the hydrologic impacts at a smaller spatial scale become particularly pronounced. In this paper, a methodology is developed to address such uncertainties for a specific problem of drought impact assessment with results from GCM simulations. Samples of a drought indicator are generated with downscaled precipitation from available GCMs and scenarios. Since it is very unlikely that such small samples resulting from GCM scenarios fit a known parametric distribution, nonparametric methods such as kernel density estimation and orthonormal series methods are used to determine the probability distribution function (PDF) of the drought indicator. Principal component analysis, fuzzy clustering, and statistical regression are used for downscaling the mean sea level pressure (MSLP) output from the GCMs to precipitation at a smaller spatial scale. Reanalysis data from the National Center for Environmental Prediction (NCEP) are used in relating precipitation with MSLP. The information generated through the PDF of the drought indicator in a future year may be used in long-term planning decisions. The methodology is demonstrated with a case study of the drought-prone Orissa meteorological subdivision in India. [ABSTRACT FROM AUTHOR]

Cirrus clouds play an important role in the Earth's radiative energy budget, thereby affecting the climate state and climate change. Orographic gravity wave (OGW)‐induced sub‐grid scale vertical velocity (i.e., cooling rate) is not resolved by large‐scale models and its impact on ice formation in cirrus clouds is not well quantified. In this study, one sub‐grid scale OGW scheme (e.g., McFarlane) is used in the Community Atmosphere Model version 6 (CAM6) to generate vertical velocity variance (σw) for cirrus formation. Results from the default model and simulations with the OGW‐induced σw are evaluated against the DOE ARM Small Particles in Cirrus (SPARTICUS) campaign observations. The OGW based on the McFarlane scheme increases the sub‐grid scale σw over mountains compared to the default model and improves the model agreement with the SPARTICUS observations. Larger σw due to OGWs can trigger more frequent homogeneous nucleation in orographic cirrus and generates a higher number concentration of ice crystals observed during the SPARTICUS campaign. Moreover, our evaluation of the model simulations against satellite observations indicates that the McFarlane scheme generates high in‐cloud ice number concentrations (>200 L−1) in the upper troposphere over mountains and high plateaus at mid‐ and high‐latitudes of the winter hemisphere as shown in the observations. More ice crystals with smaller sizes absorb more infrared radiation (+0.523 ± 0.125 W m−2). The net radiative cloud forcing change at the top of the atmosphere is +0.330 W m−2 due to the orographic cirrus. Plain Language Summary: Cirrus clouds occurring frequently in the upper troposphere play an important role in the Earth's radiative budget and the global climate. Large mountains in the globe can produce strong gravity waves propagating upward to the upper troposphere. These orographic gravity waves can induce large vertical velocities and form orographic cirrus which can extend hundreds of miles in the atmosphere. This study introduces orographic gravity waves induced by the mountains to cirrus ice formation in a global climate model, the Community Atmosphere Model version 6 (CAM6). We find that a large amount of ice crystals forms in the simulated cirrus over mountains in the winter hemisphere when orographic gravity waves are included in the cirrus formation. The simulation results are improved when comparing with aircraft and satellite observations. Orographic cirrus induces a warming effect on the global climate. Key Points: Vertical velocity variance (σw) generated by orographic gravity waves (OGWs) is introduced to the cirrus formation in CAM6The sub‐grid scale σw is increased by OGW‐induced fluctuations over mountains in orographic cirrus and agrees better with observationsThe large σw induced by OGWs generates high in‐cloud ice number concentrations (>200 L−1) in orographic cirrus as observed [ABSTRACT FROM AUTHOR]

Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions.We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method.We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied.The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios. [ABSTRACT FROM AUTHOR]

The thermal structure of the upper atmosphere in the Venus polar region is reproduced by the assimilation of horizontal winds in low‐ and mid‐latitudes derived from the Akatsuki ultra‐violet images. The obtained structure is well consistent with infrared and radio occultation measurements. In particular, the unique thermal features in the Venus polar vortex such as the cold collar and the warm polar region in our model are realistic: the level where the cold collar is located and the temperature difference between the cold collar and the warm polar region are consistent with data. The reasons why the thermal structure of the upper atmosphere in the Venus polar region is realistic are that the zonal wind distribution around the cloud top level and the structure of the residual mean meridional circulation induced by the thermal tides are improved with respect to the model without data assimilation. Our results also suggest that the thermal structure in the Venus upper polar region is closely related to the atmospheric dynamics. Plain Language Summary: Data assimilation is one of the powerful techniques to correct an output from a numerical model by comparing with the observations and enables us to obtain the modeling results consistent with the measurements. In this study, by the data assimilation of horizontal winds in low‐ and mid‐latitudes derived by the Akatsuki ultra‐violet images, the thermal structure of the upper atmosphere in the Venus polar region has been reproduced by the General Circulation Model. The zonal wind distribution around the cloud top level in the model is well consistent with the measurements due to the data assimilation, and then the unique thermal structure in the Venus polar vortex such as the cold collar and the warm polar region reproduced by the model is also improved with respect to the model without data assimilation because the structures of the atmospheric circulation and the atmospheric waves are realistic due to the improvement of the zonal wind distribution. Our results also suggest that the thermal structure of the upper atmosphere in the Venus polar region is closely related to the atmospheric dynamics. Key Points: The thermal structure in the Venus upper polar region is reproduced by the assimilation of horizontal winds derived from Akatsuki UVIThe reproduced thermal structure becomes realistic and consistent with the observations due to the data assimilationOur results suggest that the thermal structure in the Venus upper polar region is closely related to the atmospheric dynamics [ABSTRACT FROM AUTHOR]

Bias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions. [ABSTRACT FROM AUTHOR]

We describe the model performance of a new global coupled climate model configuration, CM4‐MG2. Beginning with the Geophysical Fluid Dynamics Laboratory's fourth‐generation physical climate model (CM4.0), we incorporate a two‐moment Morrison‐Gettelman bulk stratiform microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature‐dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere‐ocean‐land‐sea ice simulations, following Coupled Model Intercomparison Project Phase 6 protocols. CM4‐MG2 generally captures CM4.0's baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward "double" Intertropical Convergence Zones south of the equator, and a stronger Madden‐Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger‐than‐observed El Nin∼ $\tilde{\mathrm{n}}$o‐Southern Oscillation. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4‐MG2. Plain Language Summary: A sophisticated cloud microphysical scheme, along with a mineral dust and temperature‐dependent ice nucleation scheme, have been implemented in a new configuration of the Geophysical Fluid Dynamics Laboratory's most recent climate model (CM4.0). This microphysical scheme predicts both mass and number concentrations of cloud drops, ice crystals, rain, and snow, and treats aerosol‐cloud interactions more consistently. The ice nucleation on mineral dust aerosol in large‐scale clouds is represented more realistically. Centennial‐scale global coupled atmosphere‐ocean‐land‐sea ice simulations from this new configuration compare favorably with observations—with improved subtropical stratocumulus clouds and better tropical intraseasonal variability (i.e., the 30‐ to 90‐day Madden‐Julian Oscillation). The new configuration also reduces the magnitude of future global warming in response to anthropogenic emissions. Key Points: The performance of two‐moment microphysics with prognostic precipitation and an ice nucleation scheme in CM4.0: CM4‐MG2, is documentedThe overall performance of CM4‐MG2 is comparable to or better than CM4.0, based on a set of Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima and historical simulationsNotable improvements include enhanced coastal stratocumulus and a stronger Madden‐Julian Oscillation. CM4‐MG2 has a lower climate sensitivity than CM4.0 [ABSTRACT FROM AUTHOR]

The D/H ratio and its implications on the atmospheric escape, make it an essential observable to study the current and past inventory of water on Mars. With the arrival of the Trace Gas Orbiter around Mars, new measurements of the D/H ratio are now available and require tools to interpret the observations and understand the HDO cycle. We here present simulations of an updated version of the Laboratoire de Météorologie Dynamique Mars Global Climate Model which includes HDO and in particular the fractionation processes it undergoes. We compare our model simulations with the HDO observations in solar occultation from the Atmospheric Chemistry Suite mid‐infrared channel on board the Trace Gas Orbiter (Alday et al., 2021; https://doi.org/10.5281/ZENODO.5100448). The model successfully reproduces the general trends of the D/H ratio, indicating that the main physical processes are captured by theory. A consistent simulation of condensation processes is found to be key in the representation of the D/H ratio. Improvements in the representation of clouds and on the water cycle will help improving the representation of the HDO cycle and better help extrapolate back in times the conditions of water escape on Mars. Plain Language Summary: Understanding how the Martian climate affects the isotopic ratio of water is key to understand the history of water on the planet. We use a general circulation model to simulate the cycle of HDO, an isotope of water, in the atmosphere of Mars. We compare our model results with spacecraft observations from the Atmospheric Chemistry Suite spectrometer on board the Trace Gas Orbiter, currently in orbit around Mars. Our model provides a good qualitative agreement with the observations. We find that the condensation of water vapor into ice is a critical process for determining the isotopic ratio of the vapor phase. Key Points: We simulated the HDO cycle on Mars using a 3D Global Climate ModelGCM simulations were compared with solar occultations from Atmospheric Chemistry Suite on board the Trace Gas OrbiterA good representation of the condensation processes is key to reproduce the observed D/H ratio [ABSTRACT FROM AUTHOR]

The influence of atmospheric composition on the climates of present‐day and early Earth has been studied extensively, but the role of ocean composition has received less attention. We use the ROCKE‐3D ocean‐atmosphere general circulation model to investigate the response of Earth's present‐day and Archean climate system to low versus high ocean salinity. We find that saltier oceans yield warmer climates in large part due to changes in ocean dynamics. Increasing ocean salinity from 20 to 50 g/kg results in a 71% reduction in sea ice cover in our present‐day Earth scenario. This same salinity change also halves the pCO2 threshold at which Snowball glaciation occurs in our Archean scenarios. In combination with higher levels of greenhouse gases such as CO2 and CH4, a saltier ocean may allow for a warm Archean Earth with only seasonal ice at the poles despite receiving ∼20% less energy from the Sun. Plain Language Summary: The composition of the atmosphere, especially the abundance of greenhouse gases, famously influences Earth's climate system. We use a climate model to show that the composition of the ocean can also have a major impact on surface temperature and ice cover. We focus specifically on the amount of salt dissolved in seawater, and we find that saltier oceans tend to result in warmer climates. These effects are modest today, but salt may be a key ingredient for early Earth habitability in the distant past when the Sun was less bright. Key Points: Saltier oceans result in warmer climates with less sea iceWarming with increasing salinity is strongly affected by changing ocean dynamicsA saltier ocean may have helped keep early Earth warm when the Sun was less luminous [ABSTRACT FROM AUTHOR]

Representing the rainy season of the maritime continent is a challenge for global and regional climate models. Here, we compare regional climate models (RCMs) based on the coupled model intercomparison project phase 5 (CMIP5) model generation with high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. The onset and the total precipitation of the rainy season for both model experiments are compared against observational datasets for Southeast Asia. A realistic representation of the monsoon rainfall is essential for agriculture in Southeast Asia as a delayed onset jeopardizes the possibility of having three annual crops. In general, the coupled historical runs (Hist‐1950) and the historical force atmosphere run (HighresSST) of the high‐resolution model intercomparison project (HighResMIP) suite were consistently closer to the observations than the RCM of CMIP5 used in this study. We find that for the whole of Southeast Asia, the HighResMIP models simulate the onset date and the total precipitation of the rainy season over the region closer to the observations than the other model sets used in this study. High‐resolution models in the HighresSST experiment showed a similar performance to their low‐resolution equivalents in simulating the monsoon characteristics. The HighresSST experiment simulated the anomaly of the onset date and the total precipitation for different El Niño‐southern oscillation conditions best, although the magnitude of the onset date anomaly was underestimated. [ABSTRACT FROM AUTHOR]

Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top‐of‐atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude compared with anthropogenic climate change in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof‐of‐concept with an idealized general circulation model (GCM). UQ is performed using the calibrate‐emulate‐sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect‐model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of the non‐Gaussian posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the spread of the parameter posterior distribution leads to a reduction in the uncertainty of climate model predictions. Plain Language Summary: Climate models rely on empirical representations of physical processes that cannot be resolved with available computational resources. Empirical representations of physical processes, such as turbulence and cloud physics, reduce the computational cost of simulations, but introduce new unknown parameters into the climate model. The unknown parameters contribute to uncertainties associated with climate model predictions. Historically, fixed values of the model parameters have been hand‐tuned using scientific intuition and a limited amount of available data. We develop methods for the computationally efficient estimation of the unknown climate model parameters and their uncertainty from data, by using optimization and machine learning. Many processes and observable statistics of Earth's climate used to produce this data are influenced by seasonal variations. We demonstrate that the incorporation of seasonal information into these statistics significantly improves the resulting calibration of climate model parameters, in contrast to using annually averaged information alone. We show that including seasonal information also reduces the uncertainty associated with the model parameters, which consequently reduces the uncertainty of climate model predictions. Key Points: We use time‐averaged climate statistics to calibrate and quantify uncertainty of model parameters in an idealized general circulation model with a seasonal cycleWe show a reduction in parameter error up to 10x by using seasonally averaged statistics for Bayesian learning, compared to annual averagesWe demonstrate a 2–5 factor reduction in parametric uncertainty when including seasonal information, compared to annual averages [ABSTRACT FROM AUTHOR]

The vertical profiles of water vapor and its semi‐heavy hydrogen isotope HDO provided by instruments on ExoMars Trace Gas Orbiter constitute a unique new data set to understand the Martian water cycle including its isotopic composition. As water vapor undergoes hydrogen isotopic fractionation upon deposition (but not sublimation), the D/H isotopic ratio in water is a tracer of phase transitions, and a key quantity to understand the long‐term history of water on Mars. Here, we present 3D global simulations of D/H in water vapor and compare them to the vertically resolved observations of D/H and water ice clouds taken by NOMAD during the second half of Mars year 34. D/H is predicted to be constant with height up to the main cloud level, above which it drops because of strong fractionation, explaining the upper cut‐off in the NOMAD observations when HDO drops below detectability. During the global and regional dust storms of 2018/2019, we find that HDO ascends with H2O, and that the D/H ratio is constant and detectable up to larger heights. The simulations are within the provided observational uncertainties over wide ranges in season, latitude and height. Our work provides evidence that the variability of the D/H ratio in the lower and middle atmosphere of Mars is controlled by fractionation on water ice clouds, and thus modulated by diurnally and seasonally varying cloud formation. We find no evidence of other processes or reservoirs that would have a significant impact on the D/H ratio in water vapor. Plain Language Summary: The isotopic composition of atmospheric water on Mars provides insights about the phase transitions in the water cycle, because the light (H2O) and semi‐heavy (HDO) form of water vapor deposit at different saturation pressures. Knowing the isotopic composition of atmospheric water also provides insights on the long‐term evolution of water on Mars. The NOMAD instrument on ExoMars Trace Gas Orbiter provided the first vertical profiles of the D/H ratio in water vapor on Mars. The data set shows a large variability with season and latitude. To understand this behavior, a general circulation model is required. We provide detailed simulations of the D/H ratio in Martian water vapor and compare them with the NOMAD observations. The model predicts that the D/H ratio is constant in the lower atmosphere and decreases across a layer of strong cloud formation that varies with season. During the global dust storm of 2018, this cloud layer was severely lifted. The simulations compare well to the observations, both out and in the dust storm, and explain their upper cut‐off by this predicted decrease in D/H. No other processes than cloud formation, nor special surface ice reservoirs with strongly different D/H values, were needed to reproduce the observations. Key Points: Hydrogen fractionation by clouds in Mars water vapor is simulated and evaluated with NOMAD D/H observations in and out of dust stormsThe model D/H ratio is constant and drops when clouds form, explaining the upper cut‐off in the NOMAD profiles as HDO becomes undetectableNOMAD water ice observations provide evidence that fractionation by clouds is the main factor controlling the HDO distribution on Mars [ABSTRACT FROM AUTHOR]

The uncertainty in polar cloud feedbacks calls for process understanding of the cloud response to climate warming. As an initial step toward improved process understanding, we investigate the seasonal cycle of polar clouds in the current climate by adopting a novel modeling framework using large eddy simulations (LES), which explicitly resolve cloud dynamics. Resolved horizontal and vertical advection of heat and moisture from an idealized general circulation model (GCM) are prescribed as forcing in the LES. The LES are also forced with prescribed sea ice thickness, but surface temperature, atmospheric temperature, and moisture evolve freely without nudging. A semigray radiative transfer scheme without water vapor and cloud feedbacks allows the GCM and LES to achieve closed energy budgets more easily than would be possible with more complex schemes. This enables the mean states in the two models to be consistently compared, without the added complications from interaction with more comprehensive radiation. We show that the LES closely follow the GCM seasonal cycle, and the seasonal cycle of low‐level clouds in the LES resembles observations: maximum cloud liquid occurs in late summer and early autumn, and winter clouds are dominated by ice in the upper troposphere. Large‐scale advection of moisture provides the main source of water vapor for the liquid‐containing clouds in summer, while a temperature advection peak in winter makes the atmosphere relatively dry and reduces cloud condensate. The framework we develop and employ can be used broadly for studying cloud processes and the response of polar clouds to climate warming. Plain Language Summary: The polar regions are changing rapidly. Clouds and their feedbacks remain uncertain due to small‐scale unresolved processes in climate models, which contribute to uncertainties in polar climate projection. In order to understand the mechanisms that control polar clouds, we focus on their seasonal cycle in the current climate. We adopt an idealized framework for driving high‐resolution simulations by a global climate model. With minimal components represented, we find similar features between the simulated and observed polar clouds. In particular, liquid‐containing clouds reach maximum in summer, which coincides with the summer peak in moisture advection from lower latitudes. Therefore, projection of polar clouds will depend on future changes in heat and moisture advection. This framework will allow us to study the response of polar clouds to climate warming. Key Points: Large eddy simulations driven by time‐varying large‐scale forcings from an idealized general circulation model is used to simulate the seasonal cycle of Arctic cloudsSimulated low‐level cloud liquid is maximal in late summer to early autumn, and minimal in winter, consistent with observationsLarge‐scale advection provides the main moisture source for cloud liquid and shapes its seasonal cycle [ABSTRACT FROM AUTHOR]

The climates of terrestrial planets with a small amount of water on their surface, called land planets, are significantly different from the climates of planets having a large amount of surface water. Land planets have a higher runaway greenhouse threshold than aqua planets, which extends the inner edge of the habitable zone inward. Land planets also have the advantage of avoiding global freezing due to drier tropics, leading to a lower planetary albedo. In this study, we systematically investigate the complete freezing limit for various surface water distributions using a three‐dimensional dynamic atmospheric model. As in a previous study, we found that a land planet climate has dry tropics that result in less snow and fewer clouds. The complete freezing limit decreases from that for aqua planets (92% S0, where S0 is Earth's present insolation) to that for land planets (77% S0) with an increasing dry area. Values for the complete freezing limit for zonally uniform surface water distributions are consistently lower than those for meridionally uniform surface water distribution. This is because the surface water distribution in the tropics in the meridionally uniform cases causes ice‐albedo feedback until a planet lapses into the complete freezing state. For a surface water distribution using the topographies of the terrestrial planets, the complete freezing limit has values near those for the meridionally uniform cases. Our results indicate that the water distribution is important for the onset of a global ice‐covered state for Earth‐like exoplanets. Plain Language Summary: Some exoplanets are thought to be Earth‐like rocky planets within the habitable zone, where liquid water is stable on the planetary surface. Land planets with a small amount of surface water have the advantage of maintaining liquid water on their surface. We investigated the complete freezing limit using a three‐dimensional general circulation model assuming various surface water distributions. The insolations at the complete freezing limit gradually decrease from that for a water‐rich planet to that for a dry planet with an increasing dry area. Our results showed that the amount of water significantly affects the initiation of a global ice‐covered state for Earth‐like exoplanets. Key Points: The onset of a globally ice‐covered state becomes lower as the area of dry region increasesAt lower insolation, land planets are warmer than aqua planets because of less cloud and snow which lead to lower planetary albedoThe surface water distribution is one of the keys to determining the complete freezing limit [ABSTRACT FROM AUTHOR]

This study evaluates the precipitation over East Asia simulated by the variable resolution CAM‐MPAS featuring the Model for Prediction Across Scales (MPAS) atmospheric dynamical core coupled with the physics parameterizations of the Community Atmosphere Model (CAM) version 5.4. Two CAM‐MPAS experiments, one with a global quasi‐uniform resolution mesh of 120 km (MPAS‐UR) and the other with a variable resolution mesh of 30–120 km refined over East Asia (MPAS‐VR), are conducted from 1989 to 2005 following the Atmospheric Model Intercomparison Project protocol. Two regional climate model simulations at ∼25 km resolution from the Coordinated Regional Downscaling Experiment East Asia second plan are also analyzed for comparison. Results show that CAM‐MPAS performs better than the selected regional models in simulating the precipitation climatology over East Asia. Compared with MPAS‐UR, MPAS‐VR with refinement over East Asia better simulates the precipitation over and around the Tibetan Plateau (TP), the upper‐level circulation, the precipitation frequency and intensity in various regions of China, and the interannual precipitation pattern associated with the East Asian summer monsoon. Both decreased grid spacing and the use of finer‐scale terrain information contribute to the improvements in MPAS‐VR relative to MPAS‐UR. However, decreased grid spacing is more essential for simulating the precipitation magnitude over TP and the location of peak precipitation south of TP, likely due to the better resolved physical and dynamical processes associated with orographic precipitation. The 30–120 km CAM‐MPAS model is shown to be a promising tool for precipitation simulation over East Asia. Plain Language Summary: Precipitation and associated cloud and radiation processes can affect the energy budgets and water supply over land and thus the livelihoods of billions of people over East Asia. However, traditional low‐resolution global climate models still have difficulty in realistically simulating the mean‐state and variability of precipitation over East Asia, partly due to their insufficient resolution to resolve the complex terrain and associated synoptic‐scale systems. In this work, we perform model experiments using the Community Atmosphere Model coupled with the atmospheric dynamical core from the Model for Prediction Across Scales with a variable resolution mesh to assess the impact of regional refinement on the precipitation simulation over East Asia. Experiment with higher resolution (30 vs. 120 km) over East Asia better simulates the total precipitation amount over and around the Tibetan Plateau (TP), as well as precipitation frequency, intensity, and interannual variability associated with the East Asian summer monsoon. The finer grid spacing is found to contribute more to the improvements in areas adjacent to TP than the use of finer terrain information, likely due to better resolved physical and dynamical processes associated with orographic precipitation. Key Points: The precipitation climatology over East Asia can be reasonably reproduced by a global climate model CAM‐MPASRegional refinement over East Asia improves the precipitation simulation over East Asia in various aspectsFiner grid spacing is more essential for precipitation simulation around large‐scale terrain than using finer‐scale terrain information [ABSTRACT FROM AUTHOR]

We present here results of new isotope‐enabled simulations with an enhanced ECHAM6‐wiso model version nudged to the ERA5 reanalyses, at two different spatial resolutions, for the period 1979–2018. The isotopic content of snow on sea ice is considered, yielding surface water vapor with lower isotope ratios over sea ice covered areas, and the kinetic fractionation factors for oceanic evaporation are assumed as independent of wind speed. Also, the supersaturation equation was slightly re‐tuned for a better agreement with the Antarctic isotope observations. In addition to the spatial resolution, the impacts of the improved ECHAM6 model physics and the chosen updated ERA5 reanalyses data set on our simulation results are investigated. For this purpose, detailed comparisons to simulation results obtained from the predecessor ECHAM5‐wiso model nudged to ERA5 reanalyses and from ECHAM6‐wiso nudged to ERA‐Interim are performed. Compared to the ERA‐Interim reanalyses, the nudging to ERA5 does not result in substantial changes of modeled surface isotope values on a global scale but water transport over the tropics clearly changed, with increased precipitation amounts over the Amazonian area and related changes of isotopic contents in water vapor in the high troposphere. An ECHAM6‐wiso simulation at high spatial resolution (0.9° horizontal resolution and 95 vertical levels) generally further improves the modeling of annual mean isotope values. The modeling of temporal isotopic variations at seasonal and (sub‐)daily time scales is also slightly improved for the high spatial resolution configuration. The latter will be very useful for an improved interpretation of various water isotope records. Plain Language Summary: The stable water isotopes are tracers of climate processes occurring in diverse parts of the hydrological cycle. They are widely used to reconstruct past climate variations or to better understand the mechanisms that control the transport of water vapor caused by different weather patterns. Within the last decade, high temporal resolution isotope records in precipitation, water vapor, snow/ice and tree rings have become more and more popular. Comparisons of these observations with simulated outputs from isotope‐enabled climate models are very useful for understanding how climate processes affect these stable water isotopes. Here we present such new simulations at different spatial resolutions for the period 1979–2018, and guided with two different reanalyses data sets: the older ERA‐Interim data set, and its successor ERA5. We show that the spatial model resolution is important for the agreement of modeled isotope signals with observational data. While the impact of the chosen reanalyses data on the isotope‐related simulated global results are rather small, clear changes are found in the tropics related to the distribution of transport of water vapor in the atmosphere. Several updates in the model, related to the snow on sea ice and the evaporation over the ocean, are also introduced. Key Points: Isotope‐enabled high resolution ECHAM6‐wiso simulation nudged to the new ERA5 data set for the period 1979–2018 are presentedNudging to ERA5 changes the water vapor transport in the tropics and related isotopic contents of meteoric water samplesAmong all simulations performed, the one with higher spatial model resolution generally improves the performed model‐data comparisons most [ABSTRACT FROM AUTHOR]

We have investigated the seasonal and regional variations in the evaporation rate of both pure liquid water and liquid brine solutions on the surface of Mars. Our evaporation rate estimates are performed using a novel parameterization method taking into account the effects of concentration difference, between the evaporating liquid water solution and the atmosphere, in addition to the effects of near‐surface winds. The method is based on atmospheric surface‐layer calculations obtained from a global circulation model. We show that near‐surface winds strongly enhance strong seasonal variability in the evaporation rate of liquid solutions on Mars. The evaporation rate of a liquid brine solution can vary seasonally up to a factor of 3 and changes by two orders of magnitude depending on the surface elevation and properties of the terrain. Plain Language Summary: The search for liquid water on modern‐day Mars is one of the main drivers of exploration efforts because of its implications for habitability and value as an in‐situ resource. Recent investigations have shown the plausible surface meteorological conditions for the existence of transient pure and saline water (brine) on the surface of Mars. The possibility of transient pure and briny water on the surface of Mars was suggested based on visual and spectroscopical evidence. A transient liquid water solution, pure or saline, can only exist on the surface of Mars, when the surface temperature and pressure permit its stability against boiling and freezing. The time duration that a liquid water solution can stay on the Martian surface depends on its evaporation rate. Here, we investigated the seasonal and regional variations in the evaporation rate of liquid water. We find that the residence time can vary up to a factor of 3 throughout a full Mars year with a higher geographical dependency that can cause the evaporation rate to vary by two orders of magnitude. Key Points: We use a novel evaporation rate parameterization method with the MarsWRF modelOur results show the importance of wind speed variations on the evaporation rate variationsWe report strong seasonal variations up to a factor of 3 in the evaporation rate of liquid water [ABSTRACT FROM AUTHOR]

Many of the current, Coupled Model Intercomparison Project 6 (CMIP6), General Circulation Models (GCMs) show climate sensitivity higher than currently accepted uncertainty ranges. There is a weak correlation between increases in vertical resolution and in climate sensitivity from CMIP5. In particular, the MO Hadley Centre GCM's vertical resolution has more than doubled, and its climate sensitivity has also increased substantially. We therefore compare estimates of climate sensitivity from the CMIP6 model HadGEM3‐GC3.1‐LL, with 85 levels, and a version with 242 levels. This is far higher resolution than in any previously published simulations with a mainstream GCM, though still less than many scales important for clouds. The climate sensitivity and feedbacks, including the cloud feedback, change little. This suggests that vertical resolution did not drive recent increases in GCM climate sensitivity, though this result should be further tested in other GCMs and over a broader range of resolutions. Plain Language Summary: "Climate sensitivity" is how much climate is warmed, in the global and time average, by more CO2 in the air, largely from burning fossil fuels. However, it remains very uncertain. General Circulation Models (GCMs) are our most detailed and physically based models of climate, widely used in many sorts of study of possible climate change. "All models are wrong, but some are useful"—their complexity can make it hard to understand and so assess their behavior. They also have to approximate many physical processes on smaller scales—particularly cloud. More recent GCMs tend to have higher climate sensitivity, many higher than was ever thought likely. Cloud processes seem largely responsible. A possible cause is increased vertical resolution (calculations being done on more levels through the atmosphere) in the newer GCMs. There is a correlation between increased vertical resolution and increased climate sensitivity, but this could be by chance. We have therefore compared one new‐generation GCM with its standard vertical resolution and with 3 times higher (considerably higher than any previously published mainstream GCM work). The climate sensitivity barely changes, and the processes involved, including cloud processes, are clearly working almost identically. But other GCMs might be quite different. Key Points: Many Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Models (GCMs) have climate sensitivities larger than the Intergovernmental Panel on Climate Change uncertainty range or other assessments, largely thanks to cloud feedbacksThis increase in sensitivity is correlated with an increase in vertical resolution—particularly important for cloud processesHowever, tripling the vertical resolution in one CMIP6 GCM, we found that climate sensitivity and feedbacks barely change [ABSTRACT FROM AUTHOR]

A quasi‐periodic variation of the lower cloud amount induced by atmospheric waves was reproduced in our Venus general circulation model for the first time. This result agrees well with previous 2.3‐μm infrared nightside measurements. The cloud amount variation is mainly caused by a temperature fluctuation with a period of ∼5.5 Earth days near the cloud base, which is associated with a Kelvin‐like gravity wave with a zonal wavenumber‐1 structure. Our result might be able to explain the mechanism of the periodical variation of the brightness in the Venusian low‐latitudes shown by the previous 2.3‐μm infrared nightside measurements. It is also suggested that the temporal variation of the mass loading due to the temperature fluctuation is related to the abrupt appearance and disappearance of the large cloud particle (Mode 3) and explains the sharp discontinuity of the lower cloud found by the recent Akatsuki and ground‐based infrared nightside measurements. Plain Language Summary: Previous ground‐based measurements showed a quasi‐periodic brightness variation with a period of ∼5.5 Earth days in low‐latitudes on Venus. In this study, we investigated the mechanism by a Venus general circulation model with a simplified cloud physics. The result suggests that the quasi‐periodic brightness variation can be attributed to a temperature fluctuation near the cloud base associated with a planetary‐scale wave in the equatorial region. This temporal variation of the mass loading might be related to the abrupt appearance and disappearance of the large cloud particle. This could explain the change in opacity of the disruption associated with the sharp discontinuity of the lower cloud found by the recent Akatsuki and ground‐based infrared nightside measurements. Key Points: Quasi‐periodic variation of the cloud amount near the cloud base in low‐latitudes with a period of ∼5.5 Earth days is reproduced by a Venus GCMThe temporal variation of cloud amount is induced by a Kelvin‐like gravity wave with a zonal wavenumber‐1Observed quasi‐periodic variations of 2.3‐μm infrared brightness may be explained by the cloud amount variation reproduced in the model [ABSTRACT FROM AUTHOR]

By interacting with radiation, aerosols perturb the Earth's energy budget and thus the global precipitation amount. It was previously shown that aerosol‐radiation interactions lead to a reduction in the global‐mean precipitation amount. We have further demonstrated in aqua‐planet simulations that the local response to absorbing aerosols differs between the tropics and the extra‐tropics. In this study we incorporate an energy budget perspective to further examine the latitudinal‐dependence of the effect of aerosol‐radiation interaction on precipitation in idealized global simulations. We demonstrate that the transition between a positive local precipitation response in the tropics and a negative local precipitation response in the extra‐tropics occurs at relatively low latitudes (∼10°), indicating a transition between the deep‐tropics (in which the Coriolis force is low, hence direct thermally driven circulation, and associated divergence/convergence of energy/moisture, can form as a result of the diabatic‐heating) and their surroundings. In addition, we gradually increase the level of complexity of the simulations and demonstrate that, in the case of absorbing aerosols, the effect of land is to counteract some of the response both inside and outside the deep‐tropics due to the reduction in surface latent‐heat flux that opposes the diabatic‐heating. The effect of scattering aerosols is also examined and demonstrates a decrease in precipitation over land in both the tropics and extra‐tropics and no effect over the ocean. Finally, we examine these results in a more realistic set‐up and demonstrate that, although the physical mechanisms still operate, they are not significant enough to be discerned from the model's natural‐variability. Key Points: Energy budget perspective is used to examine the latitudinal dependence of the effect of aerosol‐radiation interaction on precipitationA transition between a positive response in the tropics and a negative response in the extra‐tropics occurs at relatively low latitudesScattering and absorbing aerosol effects on precipitation over land and ocean and under more realistic conditions are examined [ABSTRACT FROM AUTHOR]

In this study, we find significant sensitivity to the choice of time step for the Energy Exascale Earth System Model's atmospheric component, leading to large decreases in the magnitude of cloud forcing when the time step is reduced to 10 s. Reducing the time step size for the microphysics increases precipitation, leading to a drying of the atmosphere and an increase in surface evaporation. This effect is amplified when the microphysics is substepped together with other cloud physics processes. Coupling the model's dynamics and physics more frequently reduces cloud fraction at lower altitudes, while producing more cloud liquid at higher altitudes. Reducing the deep convection time step also reduces low cloud mass and cloud fraction. Together, these results suggest that cloud physics in a global circulation model can depend strongly on time step and, in particular, on the frequency with which cloud‐related processes are coupled with each other and with the model dynamics. Plain Language Summary: Computer simulations of the Earth's atmosphere take the state of the atmosphere at one point in time, then predict the state of the atmosphere a short interval of time into the future. The length of this time interval is known as the "time step". By doing this repeatedly, models can produce a simulated history of the atmosphere for years or even centuries. A smaller time step size requires more computer time but should ideally lead to more accurate results. In this study, we reduce the time step for the atmosphere in the Energy Exascale Earth System Model from half an hour to 10 s. The simulation with a smaller time step has more rain, which removes water from the atmosphere and reduces the fraction of the Earth's surface that is covered by clouds. We also experiment with changing the time step for only some parts of the model and not others. We find that the effects of the time step size on the model are related mostly to the frequency of coupling between processes rather than the time step used for any individual processes. Key Points: Changing time step has as large an effect on Energy Earth System Model, version 1's climate as doubling horizontal resolutionShorter time steps dry the atmosphere and increase surface precipitationCoupling between processes is the most important source of model time step sensitivity [ABSTRACT FROM AUTHOR]

The deuterium to hydrogen (D/H) ratio is commonly used to investigate the history of water on Mars, yet the mechanisms controlling present‐day HDO behavior are poorly understood. Significant variations of the D/H ratio were first predicted on the basis of a 3D global climate model, which were later confirmed by ground‐based observations. This behavior, consisting of lower HDO/H2O ratios in the colder regions of Mars, is related to the isotopic fractionation occurring at condensation. We leverage this previous effort and present an updated implementation, using the modern version of the model, that remains in agreement with the older version. We explore the impact of the global dust storm (GDS) that occurred during Martian year 34 (MY34) on HDO. Our simulations indicate that HDO is on average 40% more abundant at 100 km during the MY34 GDS year than during a regular year, with likely large consequences for the escape flux of water that year. Plain Language Summary: HDO, the semi‐heavy isotope of water, when compared to water, is a good indicator of how much water has been escaping from the atmosphere of Mars over the ages. Ultimately, it can be used to estimate the past reservoir of water available on Mars in its early youth. Because HDO has a slightly higher molecular mass compared to H2O, condensation induces an enrichment of HDO in the ice phase compared with the vapor phase. This subsequently causes spatial and temporal variations of the deuterium to hydrogen ratio. We use a global circulation model to simulate the HDO cycle in the atmosphere. Our model is an upgrade of the previous model presented in Montmessin et al. (2005, Journal of Geophysical Research, 110(E3), E10004. doi:10.1029/2004JE002357). We then simulate the effect of the global dust storm that affected Mars during the summer of 2018, and show that it should have had a strong impact on the vertical distribution of HDO, allowing it to reach higher altitudes. Such simulations are intended to be compared with observations from the Trace Gas Orbiter, currently in orbit around Mars. Key Points: We reimplemented the HDO cycle in a more recent version of the Laboratoire de Météorologie Dynamique Mars global climate modelWe reproduce the observed gradient of the deuterium to hydrogen ratio between cold and warm regionsThe global dust storm of Martian year 34 has a strong effect on the vertical distribution of HDO [ABSTRACT FROM AUTHOR]

This study investigates the potential impacts of climate change on future flows in the main stem of the Connecticut and Merrimack rivers within Massachusetts. The study applies two common climate projections based on (Representative Concentration Pathways), RCP 4.5 and RCP 8.5 and downscaled gridded climate projections from 14 global climate models (GCMs) to estimate the 100‐year, 24‐h extreme precipitation events for two future time‐periods: near‐term (2021–2060) and far‐term (2060–2099). 100‐year 24‐h precipitation events at near‐ and far‐term are compared to GCM‐driven historical extreme precipitation events during a base period (1960–1999) and results for RCP 8.5 scenario show average increases between 25%–50% during the near‐term compared to the base period and increases of over 50% during the far‐term. Streamflow conditions are generated with a distributed hydrological model where downscaled climate projections are used as inputs. For the near‐term, the medians of the GCMs using the RCP 4.5 and RCP 8.5 suggest 2.9%–8.1% increases in the 100‐year, 24‐h flow event in the Connecticut and an increase of 9.9%–13.7% in the Merrimack River. For the far‐term, the medians of the GCMs using the RCP 4.5 and RCP 8.5 suggest a 9.0%–14.1% increase in the Connecticut and 15.8%–20.6% for the Merrimack River. Ultimately, the results presented here can be used as a guidance for the long‐term management of infrastructures on the Connecticut and Merrimack River floodplains. [ABSTRACT FROM AUTHOR]

Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions. [ABSTRACT FROM AUTHOR]

Subkilometer processes are critical to the physics of aerosol‐cloud interaction (ACI) but have been dependent on parameterizations in global model simulations. We thus report the strength of ACI in the Ultra‐Parameterized Community Atmosphere Model (UPCAM), a multiscale climate model that uses coarse exterior resolution to embed explicit cloud‐resolving models with enough resolution (250 m horizontal, 20 m vertical) to quasi‐resolve subkilometer eddies. To investigate the impact on ACIs, UPCAM's simulations are compared to a coarser multiscale model with 4 km horizontal resolution. UPCAM produces cloud droplet number concentrations (Nd) and cloud liquid water path (LWP) values that are higher than the coarser model but equally plausible compared to observations. Our analysis focuses on the Northern Hemisphere (20–50°N) oceans, where historical aerosol increases have been largest. We find similarities in the overall radiative forcing from ACIs in the two models, but this belies fundamental underlying differences. The radiative forcing from increases in LWP is weaker in UPCAM, whereas the forcing from increases in Nd is larger. Surprisingly, the weaker LWP increase is not due to a weaker increase in LWP in raining clouds, but a combination of weaker increase in LWP in nonraining clouds and a smaller fraction of raining clouds in UPCAM. The implication is that as global modeling moves toward finer than storm‐resolving grids, nuanced model validation of ACI statistics conditioned on the existence of precipitation and good observational constraints on the baseline probability of precipitation will become key for tighter constraints and better conceptual understanding. Plain Language Summary: How aerosol particles impact the climate through their interactions with clouds is a significant source of uncertainty in quantifying the drivers of climate change over the past hundred years. Global climate models have so far been heavily reliant on approximations of the physical processes that occur at subkilometer scales, even though processes at those scales are important for representing the physics behind aerosol‐cloud interactions. To address this gap, we develop and run a multiscale global model that embeds a finer‐scale model (250 m in the horizontal and 20 m in the vertical) within the columns of a coarser‐resolution global model. A pair of simulations with preindustrial and present‐day aerosol emissions is used to quantify the impact of human aerosol emissions. They show that the climate impact of resolving subkilometer resolutions is relatively small. However, this masks some key differences. The increase in cloud water with increasing aerosols is substantially weaker when subkilometer motions are resolved. Most of this weakening is due to a weaker response in nonraining clouds and there being fewer clouds that rain in the high‐resolution model. The simulation results point to observations of specific processes that can help further constrain the impact of aerosols on clouds and climate. Key Points: Aerosol‐cloud interactions in a global model that resolves subkilometer processes are compared to those in a coarser 4 km modelOver the Northern Hemisphere ocean, resolving subkilometer scales leads to a weaker increase in liquid water path with aerosolsWeaker LWP increase is due to fewer precipitating clouds and weaker LWP increase in nonraining clouds [ABSTRACT FROM AUTHOR]

Global dust storms are the most thermodynamically significant dust events on Mars. The most recent of these events occurred in 2018. Although it was monitored by several spacecraft in orbit and on the surface, many questions remain regarding its onset, expansion and decay. Here, we model the 2018 event with the National Aeronautics and Space Administration (NASA) Ames Mars Global Climate Model in order to better understand the evolution of the storm. Our results highlight a mechanism for the expansion of the storm: the initial equatorial regional storm creates a zonal atmospheric temperature gradient causing strong equatorial eastward winds and thus rapid eastward transport of dust and subsequent lifting. The model shows rapid back and forth transfer of dust between western and eastern hemispheres reservoirs, which may also play an important role in the storm's development through teleconnections involving replenishment of surface dust. The model also shows that gigantic dust plumes occur during the storm's mature phase, injecting dust up to 80 km. Our analysis shows that their upward motion in the atmosphere is due to the ascending branches of Hadley cells, whose intensity is reinforced during the storm with increasing dustiness. We show that the global atmospheric warming during the storm cause vapor and water ice clouds to migrate to higher altitudes, in line with recent observations. Finally, we find that the choice of effective radius for the lifted dust particle size distribution impacts the intensity of the Hadley circulation and could explain some of the differences obtained between model results and observations. Plain Language Summary: Global dust storms are planetary‐scale events on Mars but remain difficult to predict with climate models because the mechanisms of their onset and evolution are not well known and involve many subtle positive or negative feedbacks between the circulation and heating. The most recent of these events, which began in June 2018, was monitored by several spacecraft in orbit and on the surface. Here, we model this global dust storm with the NASA Ames Mars Global Climate Model to better understand the evolution of the storm. We find that the global dust storm is characterized by a rapid eastward transport of dust in the equatorial regions and subsequent lifting. We highlight rapid back and forth transfer of dust between western and eastern hemispheres reservoirs, which may play an important role in the storm development through long‐distance connections between regions involving replenishment of surface dust. We also investigate large dust plumes occurring during the mature phase of the storm and injecting dust up to 80 km. Finally, we find that the height at which water condenses to form clouds increases during the storm, leading to more water vapor in the upper atmosphere. Key Points: We model the Mars Year 34 global dust storm with a Mars climate model to understand its evolution and assess sources and sinks of dustWe show back and forth dust transfer between surface reservoirs, suggesting that teleconnections play a key role in the storm's evolutionThe upper atmosphere is enriched in water vapor as thinner water ice clouds migrate to high altitudes in response to atmospheric heating [ABSTRACT FROM AUTHOR]

We construct a simple cloud model for a Venus general circulation model (GCM), which includes condensable gases of H2O and H2SO4 vapors, and condensation, evaporation, and sedimentation of sulfuric acid cloud particles. The zonally averaged mass loading of the cloud reproduced in the model takes its maximum and minimum in high and middle latitudes, respectively. This latitudinal distribution is consistent with the infrared measurements. The thick cloud is formed in high latitudes at 43–55 km altitudes by vertical winds associated with disturbances enhanced in the low static stability layer. The moderately thick cloud in low latitudes is attributed mainly to the transport of H2SO4 vapor by the mean meridional circulation. The horizontal cloud distribution in low latitudes has zonal wave numbers 1 and 2 structures, which change in time significantly. These characteristics of the low‐latitude cloud would be associated with atmospheric waves in the cloud layer. The mixing ratio of H2O vapor increases with latitude in the cloud layer due to the vertical wind disturbances in the low static stability layer in high latitudes. This latitudinal trend is qualitatively consistent with the infrared measurements. The mixing ratio of H2SO4 vapor increases with latitude in the subcloud layer because a large amount of the cloud is evaporated there due to the sedimentation of cloud particles in the thick lower cloud in the polar region. The present results suggest that the Venus cloud distribution in the lower cloud layer is strongly affected by waves and/or disturbances as well as the mean meridional circulation. Plain Language Summary: Venus is totally covered by thick clouds composed of concentrated sulfuric acid droplets. Although a lot of measurements of the Venus cloud have been conducted so far, the mechanism on how these thick clouds are generated and maintained in the Venus atmosphere has not been fully understood. In the present study, we constructed a simple cloud model for a Venus general circulation model (GCM) to investigate the three‐dimensional distributions of the cloud and condensable gases. We succeeded in reproducing these distributions consistent with the previous observations, except for the sulfuric acid vapor mixing ratio in low latitudes. The present result elucidates that the cloud distribution is strongly affected by waves and/or disturbances as well as mean meridional circulation, and the lower cloud changes in time and space as shown in the infrared measurements. It is suggested that the atmospheric motions are crucial to understand how the Venus cloud is produced and maintained. Key Points: Three‐dimensional Venus cloud formation process is investigated by using a general circulation model with simple cloud physicsThick clouds are produced and maintained in high latitudes by small‐scale disturbances with significant vertical windsThe lower cloud, which is affected by waves/disturbances as well as mean meridional circulation, changes in time and space significantly [ABSTRACT FROM AUTHOR]

Most general circulation models compute radiation fluxes by assuming that water vapor is uniform within individual grid layers, which leads to an underestimation of satellite‐observed longwave (LW) cloud radiative forcing (LWCF). To fix this problem, we calculated water vapor content separately for clear and cloudy portions and used them to compute LW radiation. The impacts of this modification were examined by comparing two global simulations with and without the modification (NEW and OLD, respectively). Global‐annual mean LWCF from NEW was 1.8 W m−2 higher than that of OLD, thus remedying a long‐standing negative bias of LWCF. This improvement is a combined result of more clear‐sky and less all‐sky upward LW flux at the top of the atmosphere than OLD. Large increases in LWCF and clear‐sky LW flux occurred in the tropical deep convection and midlatitude storm track regions where upper‐ and middle‐level clouds are abundant. Although only the LW radiation scheme was modified, global‐annual mean shortwave cloud radiative forcing also increased, particularly in the vicinity of the eastern subtropical marine stratocumulus decks through radiative feedback processes. With this improved treatment, it may be possible to tune general circulation models in a more flexible and physical way without introducing compensating errors. Plain Language Summary: Most general circulation models have computed radiation fluxes by assuming that the water vapor is uniform within individual grid layers. However, this assumption has resulted in the underestimation of satellite‐observed longwave cloud radiative forcing, since water vapors in cloudy portions are in reality larger than those in clear regions. Thus, in this study, we calculated the water vapors in clear and cloudy portions separately and used it to calculate the longwave radiation. It turns out that our new approach substantially reduces the long‐standing negative bias of longwave cloud radiative forcing in a general circulation model and creates the possibility of tuning the models in a more flexible and physical way without introducing compensating errors. Key Points: We modified a longwave radiation scheme to account for subgrid variation of water vapor between clear and cloudy portionsThis modification substantially remedies a long‐standing negative bias of global mean longwave cloud radiative forcingIt also enhances shortwave cloud radiative forcing over subtropical oceans through radiative feedback processes. [ABSTRACT FROM AUTHOR]

While most observations indicate well‐buffered clouds to aerosol perturbations, global models do not. Among the suggested mechanisms for this discrepancy is the models' lack of connections between cloud droplet size and two processes that can contribute to reduced cloudiness when droplets become more numerous and smaller: evaporation and entrainment. In this study, we explore different implementations of size‐dependent evaporation and entrainment in the global atmospheric model CAM5.3‐Oslo. We study their impact on the preindustrial‐to‐present day change in liquid water path (LWPPD‐PI) and the corresponding aerosol indirect effect (AIEPD‐PI). Impacts of the 2014–2015 fissure eruption in Holuhraun, Iceland, are also presented. Our entrainment modifications only have a moderate effect on AIEPD‐PI (changes from −1.07 W m −2 to −0.98 W m −2), and a small impact on the signal from the Holuhraun eruption compared to other suggested compensating mechanisms. Simulations with added size‐dependent evaporation in the top of the stratiform clouds also show small evaporation differences between PI and PD. Moderate changes in AIEPD‐PI were achieved when also including an entrainment feedback to the evaporation changes, mixing air between the cloudtop layer and the layer above. These changes were not associated with the size dependency, but changes in the cloud susceptibility to aerosols in both PI and PD when adding evaporation. We find that increased evaporation of smaller droplets at stratiform cloud tops can reduce LWP, but can increase LWP in some areas due to enhanced shallow convection caused by destabilization. Key Points: Enhanced evaporation does not necessarily result in decreased cloud liquid waterPD‐PI differences in droplet size are too small for size‐dependent evaporation to give large impacts on aerosol indirect effects (AIE)Size‐dependent entrainment dampens the second AIE, but not as much as damping the dependency of cloud droplet number to autoconversion [ABSTRACT FROM AUTHOR]

Pluto's atmosphere is mainly nitrogen and is in solid‐gas equilibrium with the surface nitrogen ice. As a result, the global nitrogen ice distribution and the induced nitrogen condensation‐sublimation flows strongly control the atmospheric circulation. It is therefore essential for Global Climate Models to accurately account for the global nitrogen ice distribution in order to realistically simulate Pluto's atmosphere. Here we present a set of new numerical simulations of Pluto's atmosphere in 2015 performed with a Global Climate Model using a 50‐km horizontal resolution (3.75° × 2.5°) and taking into account the latest topography and ice distribution data, as observed by the New Horizons spacecraft. In order to analyze the seasonal evolution of Pluto's atmosphere dynamics, we also performed simulations at coarser resolution (11.25° × 7.5°) but covering three Pluto years. The model predicts a near‐surface western boundary current inside the Sputnik Planitia basin in 2015, which is consistent with the dark wind streaks observed in this region. We find that this atmospheric current could explain the differences in ice composition and color observed in the northwestern regions of Sputnik Planitia, by significantly impacting the nitrogen ice sublimation rate in these regions through processes possibly involving conductive heat flux from the atmosphere, transport of dark materials by the winds, and surface albedo positive feedbacks. In addition, we find that this current controls Pluto's general atmospheric circulation, which is dominated by a retrorotation, independently of the nitrogen ice distribution outside Sputnik Planitia. This exotic circulation regime could explain many of the geological features and longitudinal asymmetries in ice distribution observed all over Pluto's surface. Key Points: High‐resolution simulations of Pluto's climate show that the circulation is dominated by ∼10‐m/s retrograde winds during most of the yearNitrogen condensation‐sublimation flows in Sputnik Planitia are creating an intense western boundary currentAtmospheric heat flux, transport of tholins, and albedo feedbacks could explain the albedo contrasts observed in Sputnik Planitia [ABSTRACT FROM AUTHOR]

The response of the Greenland Ice Sheet (GrIS) to a warmer climate is uncertain on long time scales. Climate models, such as those participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), are used to assess this uncertainty. The Community Earth System Model version 2.1 (CESM2) is a CMIP6 model capable of running climate simulations with either one‐way coupling (fixed ice sheet geometry) or two‐way coupling (dynamic geometry) to the GrIS. The model features prognostic snow albedo, online downscaling using elevation classes, and a firn pack to refreeze percolating melt water. Here we evaluate the representation of the GrIS surface energy balance and surface mass balance in CESM2 at 1° resolution with fixed GrIS geometry. CESM2 agrees closely with ERA‐Interim reanalysis data for key controls on GrIS SMB: surface pressure, sea ice extent, 500 hPa geopotential height, wind speed, and 700 hPa air temperature. Cloudsat‐CALIPSO data show that supercooled liquid‐containing clouds are adequately represented, whereas comparisons to Moderate Resolution Imaging Spectroradiometer and CM SAF Cloud, Albedo, and Surface Radiation data set from Advanced Very High Resolution Radiometer data second edition data suggest that CESM2 underestimates surface albedo. The seasonal cycle and spatial patterns of surface energy balance and surface mass balance components in CESM2 agree well with regional climate model RACMO2.3p2, with GrIS‐integrated melt, refreezing, and runoff bracketed by RACMO2 counterparts at 11 and 1 km. Time series of melt, runoff, and SMB show a break point around 1990, similar to RACMO2. These results suggest that GrIS SMB is realistic in CESM2, which adds confidence to coupled ice sheet‐climate experiments that aim to assess the GrIS contribution to future sea level rise. Key Points: The simulation of GrIS climate and SMB in CESM2 compares well to reanalyses and RACMO2 dataGrIS‐integrated melt, runoff, and refreezing in CESM2 are bracketed by RACMO2 estimates at 11 and 1 kmA break point in SMB is identified at year 1993±8, driven by increased melt and runoff [ABSTRACT FROM AUTHOR]

Using a new observational technique, the NGIMS (Neutral Gas and Ion Mass Spectrometer) instrument on the MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft has the unique capability to measure horizontal thermospheric winds. Measured along the orbit track from periapsis (∼150 km) to ∼200 km, these are the first in situ observations of thermospheric winds at Mars. Significantly, this also means that simulated winds from a global circulation model can be compared to in situ observations from this part of the Martian atmosphere for the first time. In this study, observations from five NGIMS neutral wind campaigns have been compared to simulations from the Mars Global Ionosphere‐Thermosphere Model (M‐GITM), a ground to exosphere 3‐D general circulation model. By comparing NGIMS neutral wind observations to model simulations, the processes driving the winds and their variations in the upper atmosphere are examined. These comparisons show that for certain observational periods, the M‐GITM simulated winds can generally replicate the magnitude and/or direction of the NGIMS wind observations, while in others, significant differences occur. In general, wind observations from NGIMS campaigns with large orbit‐to‐orbit variability are not well replicated by M‐GITM, while campaigns with higher observed wind speeds are better captured by the model. Additionally, using these data‐model comparisons, the relative role of normal solar forcing and corresponding differential heating in driving thermospheric winds at Mars is found to be variable, likely acting as the primary driver under some conditions and secondary to other physical processes under others. Plain Language Summary: The MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking monthly measurements of the speed and direction of the winds in the upper atmosphere of Mars between about 150 and 200 km above the surface. The observed wind speeds and directions change with time and location, and sometimes fluctuate quickly. These measurements are compared to simulations from a computer model of the Mars atmosphere called M‐GITM (Mars Global Ionosphere‐Thermosphere Model). This is the first comparison between direct measurements of winds in the upper atmosphere of Mars and simulated winds and is important because it can help to inform us what physical processes are acting on the observed winds. Some wind measurements have similar wind speeds or directions to those predicted by the M‐GITM model, but sometimes, there are large differences between the simulated and measured winds. The disagreements between wind observations and model simulations suggest that processes other than normal solar forcing may become relatively more important during these observations and alter the expected circulation pattern. Understanding the processes that drive the winds in the upper atmosphere of Mars provides key context for understanding how the atmosphere behaves as a whole system. Key Points: New thermospheric wind observations at Mars are compared to a global circulation model for the first timeWinds during observational campaigns with high average wind speeds and little variability were best replicated in model simulationsData‐model disagreements suggest processes other than normal solar forcing may become important and alter the expected circulation pattern [ABSTRACT FROM AUTHOR]

The origin of the East Asian summer monsoon (EASM)—the sole monsoon existing in the subtropics‐midlatitude in the northern hemisphere—has long been recognized as an elevated heating and mechanical forcing of the Tibetan Plateau, and the land‐sea zonal heat contrast. However, the relative contribution of individual processes to the generation of the East Asian summer monsoon and therefore the underlying basic physics are unexplored. Here we show that the EASM is mainly driven by the dynamical effect of the Tibetan Plateau, in which forced topographic Rossby waves induce downstream southerlies, a crucial factor in EASM precipitation. From idealized general circulation model simulations, the dynamical effect of mountains is revealed to account for ~65% of the total East Asian summer precipitation, whereas the elevated heating and land‐sea heat contrast are only responsible for ~15% each and the mountain‐drag effect accounts for less than 5%. Key Points: The origin of the East Asian summer monsoon is the fluid dynamical effect due to the Tibetan PlateauThis dynamical response to Tibetan Plateau forcing explains ~65% of total precipitation over East Asian summer monsoon regionMountain forced stationary Rossby waves induce zonal pressure gradient and southerly winds over East Asia [ABSTRACT FROM AUTHOR]

Impacts of horizontal winds assimilation on thermal tides are investigated by using the Venus atmospheric general circulation model for the Earth Simulator local ensemble transform Kalman filter data assimilation system. The assimilated data are horizontal winds derived from Venus ultraviolet images taken by the Venus Monitoring Camera onboard the Venus Express orbiter. The results show that three‐dimensional structures of the thermal tides are significantly improved not only in the horizontal winds but also in the temperature field, even though the observations are available only at the cloud top level on the southern dayside hemisphere approximately once an Earth day. The reproduced temperature fields agree well with recent radio occultation measurements of the Venus Climate Orbiter Akatsuki. The zonal mean fields of the zonal wind and temperature are also improved globally. This study would enable reanalysis of past Venus observations. Key Points: Three‐dimensional structures of the thermal tides are significantly improved by the data assimilation of horizontal winds derived from Venus UV imagesReproduced temperature fields agree well with those obtained by radio occultation measurements of AkatsukiZonally averaged atmospheric structures are improved globally even though the observations are very limited in space and time [ABSTRACT FROM AUTHOR]

A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling. Key Points: Prognostic precipitation (both rain and snow) and its radiative effects are introduced into the MIROC6‐SPRINTARS aerosol‐climate modelAerosol‐cloud‐precipitation‐climate interactions are evaluated using multisensor satellite data sets for different treatments of rainThe radiative effect of snow is significant for both longwave and shortwave radiation and must be included for more reliable climate modeling [ABSTRACT FROM AUTHOR]

Fully developed superrotation is reproduced for the first time in very long term simulations with a dynamical Venus general circulation model (GCM) driven by a zonally averaged component of the realistic solar heating only. Starting from a motionless state with temperature distribution including a low static stability layer in the lower cloud layer, the fast superrotating zonal flow of ~100 m/s is established after 500 Earth years. In this experiment, the mean meridional circulation is responsible for generating the superrotation, because effects of thermal tides, topography, radiation process, and cloud physics are excluded in the present simulations. Sensitivity experiments indicate that the vertical eddy viscosity smaller than 0.02 m2/s is necessary for the fast superrotation to appear. The low static stability layer also strongly affects the superrotation with high‐latitude jets through angular momentum transport by baroclinic/barotropic instability in the cloud layer. Plain Language Summary: The generation mechanism of the atmospheric superrotation on Venus is one of the most important topics in the Earth and planetary sciences. Here we reproduced the fully developed superrotation for the first time in a Venus general circulation model (GCM) driven by a zonally averaged component of the realistic solar heating only. Our GCM enables us to perform long‐term simulations with medium resolution higher than those used in previous studies. It is newly found that mean meridional circulation driven by the realistic solar heating can generate the fully developed superrotation of ~100 m/s with the vertical eddy viscosity smaller than 0.02 m2/s. It is also elucidated that a weakly stratified layer observed in the cloud layer, but neglected in previous GCMs, also strongly affects the superrotation with high‐latitude jets through angular momentum transport by baroclinic/barotropic instability. Existence of the range of the vertical eddy viscosity in which the fast superrotation can be generated by the reasonable solar heating has broad implications for the atmospheric sciences in light of optimal parameters for the GCMs. The present results are of fundamental importance in the Earth and planetary sciences to understand the variety of general circulation of the planetary atmospheres. Key Points: Superrotation is generated from a motionless state by the mean meridional circulation mechanismSmallness of the vertical eddy viscosity is a key for the mean meridional circulation mechanism to generate fast superrotationBaroclinic/Barotropic instability in the low static stability layer plays an important role on the structure of superrotation [ABSTRACT FROM AUTHOR]