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Abstract Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs.

Global climate models (GCMs) are routinely relied upon to study the possible impacts of climate change on a wide range of meteorological phenomena, including tropical cyclones (TCs). Previous studies addressed whether GCMs are capable of reproducing observed TC frequency and intensity distributions. This research builds upon earlier studies by examining how well GCMs capture the physically relevant relationship between TC intensity and SST. Specifically, the influence of model resolution on the ability of a GCM to reproduce the sensitivity of simulated TC intensity to SST is examined for the MRI-AGCM (20 km), the GFDL-HiRAM (50 km), the FSU-COAPS (0.94°) model, and two versions of the CAM5 (1° and 0.25°). Results indicate that while a 1°C increase in SST corresponds to a 5.5-7.0 m s−1 increase in observed maximum intensity, the same 1°C increase in SST is not associated with a statistically significant increase in simulated TC maximum intensity for any of the models examined. However, it also is shown that the GCMs all capably reproduce the observed sensitivity of potential intensity to SST. The models generate the thermodynamic environment suitable for the development of strong TCs over the correct portions of the North Atlantic basin, but strong simulated TCs do not develop over these areas, even for models that permit Category 5 TCs. This result supports the notion that direct simulation of TC eyewall convection is necessary to accurately represent TC intensity and intensification processes in climate models, although additional explanations are also explored. [ABSTRACT FROM AUTHOR]

In this study, we take an ensemble modeling approach using the regional climate model RegCM4.3.4-CLM-CN-DV (RCM) to study the impact of including vegetation dynamics on model performance in simulating present-day climate and on future climate projections over West Africa. The ensemble consists of four global climate models (GCMs) as lateral boundary conditions for the RCM, and simulations with both static and dynamic vegetation are conducted. The results demonstrate substantial sensitivity of the simulated precipitation, evapotranspiration, and soil moisture to vegetation representation. Although including dynamic vegetation in the model eliminates potential inconsistencies between surface climate and the bioclimatic requirements of the prescribed vegetation, it enhances model biases causing climate drift. For present-day climate, the ensemble average generally outperforms individual members due to cancelation of model biases. For future changes, although the original GCMs project contradicting future rainfall trends over West Africa, the RCMs-produced trends are generally consistent. The multimodel ensemble projects significant decreases of rainfall over a major portion of West Africa and significant increases over eastern Sahel and East Africa. Projected future changes of evapotranspiration and soil moisture are consistent with those of precipitation, with significant decreases (increases) for western (eastern) Sahel. Accounting for vegetation-climate interactions has localized but significant impacts on projected future changes of precipitation, with a wet signal over a belt of projected increase of woody vegetation cover; the impact on the projected future changes of evapotranspiration and soil moisture over west and central Africa is much more profound. [ABSTRACT FROM AUTHOR]

Convection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in three‐dimensional (3D) models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of parameterizations to various perturbations rather than their behavior under particular strong forcing. We use the linear response function method proposed by Kuang (2010) to compare 12 physical packages in five atmospheric models using single‐column model (SCM) simulations under idealized radiative‐convective equilibrium conditions. The models are forced with anomalous temperature and moisture tendencies. The temperature and moisture departures from equilibrium are compared with published results from a cloud‐resolving model (CRM). Results show that the procedure is capable of isolating the behavior of a convection scheme from other physics schemes. We identify areas of agreement but also substantial differences between convection schemes, some of which can be related to scheme design. Some aspects of the model linear responses are related to their RCE profiles (the relative humidity profile in particular), while others constitute independent diagnostics. All the SCMs show irregularities or discontinuities in behavior that are likely related to threshold‐related mechanisms used in the convection schemes, and which do not appear in the CRM. Our results highlight potential flaws in convection schemes and suggest possible new directions to explore for parameterization evaluation. Plain Language Summary: The transport of heat up and down the atmosphere, called atmospheric convection, is a complex process. To simplify the representation of convection in global climate models (GCMs) scientists use "parameterization," which is essentially mathematical equations of physical processes. However, there are many different ways to formulate these equations, and no agreement on which is better. In this work we aim to understand a few popular ways to parameterize convection. We extract one vertical column from five different GCMs and lightly tickle (perturb) it and then observe its responses. We found that different models respond very differently to the same tickling, and this tells us a lot about the model. Importantly, the specific perturbation that we used can single out the responses of convection‐related equations from equations of other processes. All the models in our study have one thing in common: They are quite jumpy when tickled, especially at the top of the boundary layer where clouds start to form. We suspect the culprits are thresholds placed in the models that sometimes lead to sudden changes in their response. Our work highlights potentially problematic behavior that can give clues on how to make climate models better. Key Points: The linear response function method is applied in single‐column model (SCM) simulations and is able to isolate the behavior of convection schemesLinear responses of the models are related to the mean state relative humidity in both shape and magnitudeAll SCMs show discontinuities in their responses which are likely related to thresholds used in convective parameterization [ABSTRACT FROM AUTHOR]

Abstract: A set of idealized experiments are developed using the Community Atmosphere Model (CAM) to understand the vertical velocity response to reductions in forcing scale that is known to occur when the horizontal resolution of the model is increased. The test consists of a set of rising bubble experiments, in which the horizontal radius of the bubble and the model grid spacing are simultaneously reduced. The test is performed with moisture, through incorporating moist physics routines of varying complexity, although convection schemes are not considered. Results confirm that the vertical velocity in CAM is to first‐order, proportional to the inverse of the horizontal forcing scale, which is consistent with a scale analysis of the dry equations of motion. In contrast, experiments in which the coupling time step between the moist physics routines and the dynamical core (i.e., the “physics” time step) are relaxed back to more conventional values results in severely damped vertical motion at high resolution, degrading the scaling. A set of aqua‐planet simulations using different physics time steps are found to be consistent with the results of the idealized experiments. [ABSTRACT FROM AUTHOR]

Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection. Traditional general circulation models (GCMs) and their multiscale modeling framework (MMF) versions poorly represent CMT due to insufficient information of subgrid‐scale flows at each GCM grid. Here the explicit scalar momentum transport (ESMT) scheme for representing CMT is implemented in the Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) with embedded 2‐D cloud‐resolving models (CRMs), and verified against E3SM‐MMF simulations with 3‐D CRMs and observations. The results show that representing CMT by ESMT helps reduce climatological mean precipitation model bias over the western Pacific and the ITCZ regions, which is attributed to the weakened mean easterlies over the Pacific. Also, CMT from simulations with 2‐D and 3‐D CRMs impose a similar impact on Kelvin waves by reducing their variability and slowing down their phase speed, but opposite impacts on the Madden‐Julian Oscillation (MJO) variability. The ESMT scheme readily captures the climatological mean spatial patterns of the zonal and meridional components of CMT and their variability across multiple time scales, but shows some differences in estimating its magnitude. CMT mainly affects the MJO by decelerating its winds in the free troposphere, but accelerates its near‐surface winds. This study serves as a prototype for implementing CMT scheme in the MMF simulations, highlighting its crucial role in reducing model bias in mean state and spatiotemporal variability. Plain Language Summary: Small‐scale turbulent flows from moist convection typically lead to the vertical redistribution of large‐scale winds (referred to as convective momentum transport [CMT]). Due to the coarse grids that are too large to resolve small‐scale flows, traditional earth system models poorly represent the CMT, and thus rely on parameterizations that empirically describe the magnitude and vertical profiles of the CMT. In contrast, the default Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) is an earth system model with a 2‐D (one horizontal dimension and one vertical dimension) cloud‐resolving model embedded within each coarse grid so as to better resolve small‐scale flows, although it still lacks the necessary information to fully calculate CMT due to the lack of the third dimension. Here we implemented the explicit scalar momentum transport (ESMT) scheme to represent CMT in the E3SM‐MMF associated with 2‐D small‐scale flows at each coarse grid. The results show that in general CMT helps reduce model biases in predicting time‐mean precipitation and winds as well as the spatiotemporal variability of tropical convection. The ESMT scheme reproduces the spatial patterns of CMT as simulated by the E3SM‐MMF model with 3‐D small‐scale flows. Lastly, we focused on the Madden‐Julian Oscillation, the dominant intraseasonal variability in the tropics, as an example to investigate the impact of CMT. Key Points: Convective momentum transport (CMT) affects large‐scale circulation and convective organization, and representing CMT reduces model biases in Energy Exascale Earth System Model‐Multiscale Modeling Framework simulationsExplicit scalar momentum transport scheme captures the spatial pattern of CMT comparable to 3‐D cloud‐resolving models that explicitly model CMTCMT damps the free tropospheric circulation associated with the Madden‐Julian oscillation, but accelerates its near‐surface winds [ABSTRACT FROM AUTHOR]

Using large‐eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid‐scale processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to simulate grid columns of GCMs to generate LES across a cross section of dynamical regimes. The LES setup approximately replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large‐scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES are forced with prescribed surface temperatures, but atmospheric temperature and moisture are free to adjust, reducing the imprinting of GCM fields on the LES. In both the GCM and LES, radiative transfer is treated in a unified but idealized manner (semigray atmosphere without water vapor feedback or cloud radiative effects). We show that the LES in this setup reaches statistically steady states without nudging to thermodynamic GCM profiles. The steady states provide training data for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming. Plain Language Summary: Clouds and their feedbacks remain one of the largest uncertainties in predictions of future climate changes. High‐resolution models can provide faithful simulations of clouds and their underlying turbulence in limited areas, but they have primarily been used in select locations, with limited success in reducing uncertainties in climate predictions. This study presents a framework for driving high‐resolution simulations by a global climate model, which allows us to generate a library of high‐resolution simulations across different cloud regimes. The framework leverages the potential of high‐resolution models to improve parameterizations of clouds and turbulence in climate models and to better understand the cloud feedback mechanisms. Key Points: A framework in which LES is driven by large‐scale forcing from a GCM is developedLES with large‐scale forcing reaches steady states without nudging to thermodynamic GCM profilesLES driven by the GCM is used to simulate different tropical cloud regimes across the Walker circulation [ABSTRACT FROM AUTHOR]

Dust transport and spatial distribution are poorly represented in current global climate models (GCMs) including the Community Atmosphere Model version 5 (CAM5). Particularly, models lack explicit representation of super‐coarse dust, which may have important implications for dust radiative forcing and impacts on biogeochemistry. A nine‐mode version of the modal aerosol model (MAM9) has been developed to address these issues. In this new aerosol scheme, four dust modes have been designed to treat dust particles of sizes up to 20 μm. The MAM9‐simulated results are compared with those from the default four‐mode version of MAM (MAM4) and also with the in situ surface measurements of dust concentration and deposition flux, satellite‐retrieved dust extinction profile, and in situ vertical measurements of dust concentrations from the NASA Atmosphere Tomography Mission (ATom). Overall, MAM9 improves the dust representation in remote regions while maintaining reasonably good results near the dust source regions. In addition, MAM9 reduces the fine dust burden and increases the coarse dust burden globally. The increased coarse dust burden has slightly increased the dust direct radiative effect by 0.01 W m−2 while it enhanced dust indirect radiative effect by 0.36 W m−2, globally. Plain Language Summary: Dust aerosol is the most abundant aerosol in the atmosphere. Although the current CESM‐CAM5 model can capture some dust aerosol distribution features, the long‐distance dust transport and coarse dust burden have been poorly represented. To address these issues, we have developed the nine‐mode version of the modal aerosol model (MAM9) and implemented it to CESM‐CAM5.4. The MAM9 has four dust modes to enhance the size resolution of dust in the model. The standard deviation in each mode is reset based on observational data. Also, dust and sea salt aerosols in our MAM9 are separated into different modes to reduce the wet scavenge across the Pacific and Atlantic Oceans. These changes improved dust transport in remote regions compared with MAM4. Moreover, the changes warm up the simulated atmosphere through the dust direct and indirect radiative effects. Key Points: A nine‐mode version of the modal aerosol model has been developed to improve the dust representation in Community Atmosphere ModelThe dust aerosols simulated by this new implement in remote regions better agree with observational dataThe increased coarse dust burden has increased the dust direct and indirect radiative effects resulting in a warmer atmosphere [ABSTRACT FROM AUTHOR]

Abstract The potential scope of superparameterization (SP) was extended to higher resolutions of the global climate model (GCM) component by devising a technique called blockwise coupling. In this method, a horizontal average of multiple GCM columns, instead of one, is coupled to a cloud‐resolving model (CRM) domain. This enables SP‐GCMs to reduce the computational cost drastically, enabling higher‐resolution GCMs to be superparameterized. A blockwise‐coupled SP‐GCM called SP‐MIROC was implemented by coupling the climate model MIROC6 to the CRM SCALE‐RM. The 4 × 4‐bundled SP‐MIROC successfully reproduced horizontal patterns and frequency distributions of precipitation and realistic amplitudes of equatorial Kelvin waves (EKWs), which were underestimated in the standard MIROC6. As discussed in Yamazaki and Miura (2024b, https://doi.org/10.1029/2023MS003837) of this study, the amplitude boost of EKWs was enabled by a top‐heavy heating in SP‐MIROC. Comparison of power spectra between the 4 × 4‐bundled SP‐MIROC and nonbundled SP‐MIROC indicated that the effective resolution of dynamic variables was not degraded by the blockwise technique. Rather, spectra in the 4 × 4‐bundled SP‐MIROC were more realistic than those in the nonbundled SP‐MIROC. Although the 4 × 4‐bundling limits convective coupling in the smallest GCM scale, it could offer the best match of resolutions between the GCM‐handled dynamics and SP‐derived physics because the effective resolution of the dynamics is lower than the nominal grid spacing.

The Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud-Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative-convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius-Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (-1.1%/K) offsets this increase. Convective-scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large-scale mass flux, while the intensity of convective-scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective-scale rainfall intensities. These results motivate the need for better understanding the role of the large-scale forcing on the formation and intensification of heavy convective rainfall. [ABSTRACT FROM AUTHOR]

Phase 1 of the CGILS large-eddy simulation (LES) intercomparison is extended to understand if subtropical marine boundary-layer clouds respond to idealized climate perturbations consistently in six LES models. Here the responses to quadrupled carbon dioxide ( 'fast adjustment ') and to a composite climate perturbation representative of CMIP3 multimodel mean 2×CO2 near-equilibrium conditions are analyzed. As in Phase 1, the LES is run to equilibrium using specified steady summertime forcings representative of three locations in the Northeast Pacific Ocean in shallow well-mixed stratocumulus, decoupled stratocumulus, and shallow cumulus cloud regimes. The results are generally consistent with a single-LES study of Bretherton et al. () on which this intercomparison was based. Both quadrupled CO2 and the composite climate perturbation result in less cloud and a shallower boundary layer for all models in well-mixed stratocumulus and for all but a single LES in decoupled stratocumulus and shallow cumulus, corroborating similar findings from global climate models (GCMs). For both perturbations, the amount of cloud reduction varies across the models, but there is less intermodel scatter than in GCMs. The cloud radiative effect changes are much larger in the stratocumulus-capped regimes than in the shallow cumulus regime, for which precipitation buffering may damp the cloud response. In the decoupled stratocumulus and cumulus regimes, both the CO2 increase and CMIP3 perturbations reduce boundary-layer decoupling, due to the shallowing of inversion height. [ABSTRACT FROM AUTHOR]

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud-resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

Paleoclimate data assimilation has recently emerged as a promising technique to estimate past climate states. Here we test two of the underlying assumptions of paleoclimate data assimilation as applied so far: (1) climate proxies can be modeled as linear, univariate recorders of temperature and (2) structural errors in GCMs can be neglected. To investigate these two points and related uncertainties, we perform a series of synthetic, paleoclimate data assimilation-based reconstructions where 'pseudo' proxies are generated with physically based proxy system models (PSMs) for coral δ18 O, tree ring width, and ice core δ18 O using two isotope-enabled atmospheric general circulation models. For (1), we find that linear-univariate models efficiently capture the GCM's climate in ice cores and corals and do not lead to large losses in reconstruction skill. However, this does not hold for tree ring width, especially in regions where the trees' response is dominated by moisture supply; we quantify how the breakdown of this assumption lowers reconstruction skill for each proxy class. For (2), we find that climate model biases can introduce errors that greatly reduce reconstruction skill, with or without perfect proxy system models. We explore possible strategies for mitigating structural modeling errors in GCMs and discuss implications for paleoclimate reanalyses. [ABSTRACT FROM AUTHOR]

The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-like events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. These results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns. [ABSTRACT FROM AUTHOR]

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

General circulation models (GCMs) are typically run with coarse vertical resolution. For example, the Energy Exascale Earth System Model (E3SM) has a vertical resolution of about 200 m in the boundary layer, which is far too coarse to resolve sharp gradients often found in the thermodynamic fields capping subtropical marine stratocumulus. In this article, we present a series of multiyear atmosphere only simulations of E3SM version 1 where we progressively increase the vertical resolution in the lower troposphere to scales approaching those often used in large eddy simulation (LES). We report marginal impacts in regards to the simulation of boundary layer clouds when vertical resolution is moderately increased, yet find significant positive impacts when the vertical resolution approaches that typically used in LES (∼10 m). In these experiments, there is a marked change in the simulated turbulence and thermodynamics which leads to more abundant marine stratocumulus. However, these simulations are burdened with excessive computational cost. They are also subject to degradations in overall climate metrics due to time step sensitivities and because some processes and parameterizations are sensitive to changes in the vertical resolution. Plain Language Summary: Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has long been speculated that one of the reasons for this deficiency relates to coarse vertical resolution used in these models. In this study, we increase the vertical resolution to scales that previous process oriented studies suggest are needed to represent these cloud types. We find that increasing the vertical resolution is a necessary ingredient toward simulating stratocumulus, though deficiencies remain and the simulations are computationally expensive. Key Points: High vertical resolution in the lower troposphere is a crucial ingredient to improve marine stratocumulus (Sc) in GCMsThese simulations are expensive and require time step adjustment, which introduces sensitivitiesVertical resolution alone cannot improve coastal Sc, likely concurrent increases in horizontal resolution are needed [ABSTRACT FROM AUTHOR]

Abstract: General circulation models (GCMs) are extensively used to estimate the influence of clouds on the global energy budget and other aspects of climate. Because radiative transfer computations involved in GCMs are costly, it is typical to consider only absorption but not scattering by clouds in longwave (LW) spectral bands. In this study, the flux and heating rate biases due to neglecting the scattering of LW radiation by clouds are quantified by using advanced cloud optical property models, and satellite data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Clouds and the Earth's Radiant Energy System (CERES), and Moderate Resolution Imaging Spectrometer (MODIS) merged products (CCCM). From the products, information about the atmosphere and clouds (microphysical and buck optical properties, and top and base heights) is used to simulate fluxes and heating rates. One‐year global simulations for 2010 show that the LW scattering decreases top‐of‐atmosphere (TOA) upward flux and increases surface downward flux by 2.6 and 1.2 W/m2, respectively, or approximately 10% and 5% of the TOA and surface LW cloud radiative effect, respectively. Regional TOA upward flux biases are as much as 5% of global averaged outgoing longwave radiation (OLR). LW scattering causes approximately 0.018 K/d cooling at the tropopause and about 0.028 K/d heating at the surface. Furthermore, over 40% of the total OLR bias for ice clouds is observed in 350–500 cm−1. Overall, the radiative effects associated with neglecting LW scattering are comparable to the counterpart due to doubling atmospheric CO2 under clear‐sky conditions. [ABSTRACT FROM AUTHOR]

Abstract Atmospheric black carbon (BC) aerosols have been long‐lasting uncertain components in environmental and climate studies. Global climate models (GCMs) potentially overestimate BC absorption efficiency due to a lack of consideration of complex BC microphysical and mixing properties. We extract multiple BC properties from observations and develop an aerosol optical module known as Advanced Black Carbon (ABC) in the framework of the Modal Aerosol Model version 4 (MAM4). The ABC module is implemented in the Community Atmosphere Model version 6 (CAM6) and evaluated by in situ and remote sensing observations. CAM6‐ABC addresses the shortcomings of CAM6‐MAM4 in terms of BC microphysical and mixing properties, particularly their size, mixing state and optical simulations. Sensitivity simulations show that the global BC absorption aerosol optical depth at 550 nm simulated by CAM6‐ABC is reduced by ∼29% compared with that in CAM6‐MAM4. The BC absorption enhancement simulated by CAM6‐ABC is reduced from ∼2.6 of the default MAM4 to ∼1.4, which is closer to the observed values (mostly less than 1.5). With improved BC absorption estimation, the biases of aerosol single‐scattering coalbedo simulations are reduced by 18%–69% compared with global Aerosol Robotic Network observations. Moreover, the globally averaged BC direct radiative effect is reduced from 0.37 to 0.28 W/m2 at the top of the atmosphere. Our new scheme alleviates the overestimation of BC absorption in GCMs by constraining BC microphysical and mixing properties when assessing aerosol radiative and climate effects, and it can be easily implemented in most modal‐based aerosol modules of climate models.

Observations and regional climate modeling (RCM) studies demonstrate that global climate models (GCMs) are unreliable for predicting changes in extreme precipitation. Yet RCM climate change simulations are subject to boundary conditions provided by GCMs and do not interact with large-scale dynamical feedbacks that may be critical to the overall regional response. Limitations of both global and regional modeling approaches contribute significant uncertainty to future rainfall projections. Progress requires a modeling framework capable of capturing the observed regional-scale variability of rainfall intensity without sacrificing planetary scales. Here the United States summer rainfall response to quadrupled CO2 climate change is investigated using conventional (CAM) and superparameterized (SPCAM) versions of the NCAR Community Atmosphere Model. The superparameterization approach, in which cloud-resolving model arrays are embedded in GCM grid columns, improves rainfall statistics and convective variability in global simulations. A set of 5 year time-slice simulations, with prescribed sea surface temperature and sea ice boundary conditions harvested from preindustrial and abrupt four times CO2 coupled Community Earth System Model (CESM/CAM) simulations, are compared for CAM and SPCAM. The two models produce very different changes in mean precipitation patterns, which develop from differences in large-scale circulation anomalies associated with the planetary-scale response to warming. CAM shows a small decrease in overall rainfall intensity, with an increased contribution from the weaker parameterized convection and a decrease from large-scale precipitation. SPCAM has the opposite response, a significant shift in rainfall occurrence toward higher precipitation rates including more intense propagating Central United States mesoscale convective systems in a four times CO2 climate. [ABSTRACT FROM AUTHOR]

Systematic biases in the representation of boundary layer (BL) clouds are a leading source of uncertainty in climate projections. A variation on superparameterization (SP) called 'ultraparameterization' (UP) is developed, in which the grid spacing of the cloud-resolving models (CRMs) is fine enough (250 × 20 m) to explicitly capture the BL turbulence, associated clouds, and entrainment in a global climate model capable of multiyear simulations. UP is implemented within the Community Atmosphere Model using 2° resolution (∼14,000 embedded CRMs) with one-moment microphysics. By using a small domain and mean-state acceleration, UP is computationally feasible today and promising for exascale computers. Short-duration global UP hindcasts are compared with SP and satellite observations of top-of-atmosphere radiation and cloud vertical structure. The most encouraging improvement is a deeper BL and more realistic vertical structure of subtropical stratocumulus (Sc) clouds, due to stronger vertical eddy motions that promote entrainment. Results from 90 day integrations show climatological errors that are competitive with SP, with a significant improvement in the diurnal cycle of offshore Sc liquid water. Ongoing concerns with the current UP implementation include a dim bias for near-coastal Sc that also occurs less prominently in SP and a bright bias over tropical continental deep convection zones. Nevertheless, UP makes global eddy-permitting simulation a feasible and interesting alternative to conventionally parameterized GCMs or SP-GCMs with turbulence parameterizations for studying BL cloud-climate and cloud-aerosol feedback. [ABSTRACT FROM AUTHOR]

Diabatic heating plays a crucial role in the formation and maintenance of the West African Monsoon. A dynamical core configuration of a General Circulation Model (GCM) is used to test the influence of diabatic heating from different sources and regions on the strength and northward penetration of the monsoon circulation. The dynamical core is able to capture the main features of the monsoon flow, and when forced with heating tendencies from various different GCMs it recreates many of the differences seen between the full GCM monsoon circulations. Differences in atmospheric short-wave absorption over the Sahara and Sahel regions are a key driver of variation in the models' monsoon circulations, and this is likely to be linked to how aerosols, clouds and surface albedo are represented across the models. The magnitude of short-wave absorption also appears to affect the strength and position of the African easterly jet (AEJ), but not that of the tropical easterly jet (TEJ). The dynamical core is also used here to understand circulation changes that occur during the ongoing model development process that occurs at each modeling centre, providing the potential to trace these changes to specific alterations in model physics. [ABSTRACT FROM AUTHOR]

As revealed from studies using conventional general circulation models (GCMs), the thermodynamic contribution to the tropical cloud feedback dominates the dynamic contribution, but these models have difficulty in simulating the subsidence regimes in the tropics. In this study, we analyze the tropical cloud feedback from a 2 K sea surface temperature (SST) perturbation experiment performed with a multiscale modeling framework (MMF). The MMF explicitly represents cloud processes using 2-D cloud-resolving models with an advanced higher-order turbulence closure in each atmospheric column of the host GCM. We sort the monthly mean cloud properties and cloud radiative effects according to circulation and stability regimes. We find that the regime-sorted dynamic changes dominate the thermodynamic changes in terms of the absolute magnitude. The dynamic changes in the weak subsidence regimes exhibit strong negative cloud feedback due to increases in shallow cumulus and deep clouds while those in strongly convective and moderate-to-strong subsidence regimes have opposite signs, resulting in a small contribution to cloud feedback. On the other hand, the thermodynamic changes are large due to decreases in stratocumulus clouds in the moderate-to-strong subsidence regimes with small opposite changes in the weak subsidence and strongly convective regimes, resulting in a relatively large contribution to positive cloud feedback. The dynamic and thermodynamic changes contribute equally to positive cloud feedback and are relatively insensitive to stability in the moderate-to-strong subsidence regimes. But they are sensitive to stability changes from the SST increase in convective and weak subsidence regimes. These results have implications for interpreting cloud feedback mechanisms. [ABSTRACT FROM AUTHOR]

A surrogate climate change is designed to investigate low cloud feedbacks in the northeastern Pacific by using Single Column Models (SCMs), Cloud Resolving Models (CRMs), and Large Eddy Simulation models (LES), as part of the CGILS study (CFMIP-GASS Intercomparison of LES and SCM models). The constructed large-scale forcing fields, including subsidence and advective tendencies, and their perturbations in the warmer climate are shown to compare well with conditions in General Circulation Models (GCMs), but they are free from the impact of any GCM parameterizations. The forcing fields in the control climate are also shown to resemble the mean conditions in the ECMWF-Interim Reanalysis. Applications of the forcing fields in SCMs are presented. It is shown that the idealized design can offer considerable insight into the mechanisms of cloud feedbacks in the models. Caveats and advantages of the design are also discussed. [ABSTRACT FROM AUTHOR]

Abstract Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases.

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) consists of simulations at three fixed sea‐surface temperatures (SSTs: 295, 300, and 305 K) and thus allows for a calculation of the climate feedback parameter based on the change of the top‐of‐atmosphere radiation imbalance. Climate feedback parameters range widely across RCEMIP, roughly from −6 to 3 W m−2 K−1, particularly across general‐circulation models (GCMs) as well as global and large‐domain cloud‐resolving models (CRMs). Small‐domain CRMs and large‐eddy simulations have a smaller range of climate feedback parameters due to the absence of convective self‐aggregation. More than 70–80% of the intermodel spread in the climate feedback parameter can be explained by the combined temperature dependencies of convective aggregation and shallow cloud fraction. Low climate sensitivities are associated with an increase of shallow cloud fraction (increasing the planetary albedo) and/or an increase in convective aggregation with warming. An increase in aggregation is associated with an increase in outgoing longwave radiation, caused primarily by mid‐tropospheric drying, and secondarily by an expansion of subsidence regions. Climate sensitivity is neither dependent on the average amount of aggregation nor on changes in deep/anvil cloud fraction. GCMs have a lower overall climate sensitivity than CRMs because in most GCMs convective aggregation increases with warming, whereas in CRMs, convective aggregation shows no consistent temperature trend. Plain Language Summary: To determine how much Earth will warm in response to anthropogenic greenhouse gas emissions, we need to understand the atmospheric response to this forcing. The amount of warming in response to a given forcing is called climate sensitivity. Although global climate models are a useful tool to estimate climate sensitivity, estimates remain uncertain, in particular because the response of tropical clouds to warming is uncertain. The weakness of climate models is their coarse grid spacing, with which they cannot resolve important aspects of the weather like clouds and convection. In this study, we use a popular idealization for the tropics, the radiative‐convective equilibrium setup, to compare climate sensitivities across a wide range of models including global climate models and cloud‐resolving models. We find that more than 70–80% of variations in climate sensitivity across these models result from changes in shallow cloud fraction and changes in the spatial organization of convection with warming. Our results indicate that climate sensitivity might be underestimated by global climate models, in which the amount of spatial organization of convection mostly increases with warming, leading to low climate sensitivities, while the cloud‐resolving models show no consistent trend in spatial organization, and thus have higher climate sensitivities. Key Points: Climate feedback parameter (λ) is impacted by change of convective aggregation with temperature, not by its average valueCombined temperature dependencies of convective aggregation and shallow cloud fraction explain 70–80% of intermodel spread in λλ is more negative in GCMs than in CRMs because only GCMs show a mean increase of convective aggregation with warming [ABSTRACT FROM AUTHOR]

Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations. Plain Language Summary: Moist physics parameterization, an algorithm for clouds and convection based on empirical relationships and limited observations, is the main source of biases in rainfall and atmosphere circulation in global climate model simulations. A sophisticated deep learning algorithm with refined inherent architecture is introduced as a new parameterization in this study. It is trained with a year‐long simulation from a global climate model that has a two‐dimensional high‐resolution cloud‐scale model embedded in each climate model grid box, where clouds and convection are explicitly simulated. In addition, we consider energy conservation and past history of atmospheric states, clouds, and convection. It is designed to predict heating and moistening rates from convection and clouds, as well as cloud water and ice amount. This new parameterization accurately reproduces target simulations in 1‐year independent testing and also performs well in predicting convective events in both midlatitude summer continental land convection and tropical monsoon convection. Key Points: An advanced deep residual convolutional neural network is used for moist physics parameterizationThe neural network is trained with a 1‐year‐long simulation from superparameterized CAM5 with actual global land‐ocean distributionIt reproduces accurately the target simulation in independent test data evaluation and in single‐column model prognostic validations [ABSTRACT FROM AUTHOR]

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations. Plain Language Summary: This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The data set of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We do not find a conclusive result for how cloud clustering changes as the climate warms. Key Points: Temperature, humidity, and clouds in radiative‐convective equilibrium vary substantially across modelsModels agree that self‐aggregation dries the atmosphere and reduces high cloudinessThere is no consistency in how self‐aggregation depends on warming [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are usually initialized and driven through the boundaries of their limited area domain by data provided by global models (GCMs). The mismatch between the low‐resolution GCM initial conditions and RCM's high resolution introduces physical inconsistencies between the various components of the RCM. These inconsistencies can be resolved by running the RCM during a period that is considered unreliable: the spin‐up period. There is no deterministic definition of the length that the spin‐up period should have. Here we try to provide general guidelines that can be used to the advantage of the community. We base our analysis on Weather Research and Forecasting (WRF) simulations over a Euro‐Cordex compliant domain and find that for 2‐m temperature and precipitation, rather short spin‐up periods (1 week) can be sufficient. Nevertheless, longer periods (6 months) are advisable, and start dates in non‐winter months should be pursued, as this ensures a more realistic representation of the snow cover. Thus, the issue is not only about the spin‐up length. As the soil subsystem evolves slowly and requires longer periods to reach equilibrium than the longest considered here (1 year), seasonality plays an important role in minimizing the impact of the unreliability of the soil initialization. Fortunately, except for goals where the deep soil‐atmosphere feedback are critical, the lack of equilibrium between them can be ignored, as it seems to have little effect on the simulation of the atmospheric variables most frequently used in RCM studies. Plain Language Summary: High‐resolution climate simulations performed with the so‐called regional climate models (RCMs) are usually initialized from coarser databases that provide an inconsistent picture at the higher resolution of the RCM. To overcome it and obtain reliable RCM simulations, RCMs should run over a time span to reach physical equilibrium. During this "trash" period (called spin‐up period), the RCM outputs should be discarded for later analysis, for example, for assessing climate change impacts. In order to guarantee the reliability of RCM simulations and minimize their high computational cost, determining an optimal length of the spin‐up period is critical. For a European domain with ~50‐km resolution and for a particular, but widely used, RCM, the WRF model, here we find that half a year of spin‐up would be enough for atmospheric variables (temperature and precipitation), always that the RCM initialization occurs in summer months; otherwise, the snow cover initial state is misrepresented, making it inadvisable longer spin‐up periods implying winter start dates. However, soil variables (especially water content) cannot be trusted even after up to one full year of spin‐up. Fortunately, the negligible impact of this issue for most RCM applications involving only atmospheric variables emerges also from our analysis. Key Points: Spin‐up periods of 3 to 6 months could be enough for WRF climate simulations over EuropeSeasonality plays a major role in initializing the simulations, making it most advisable in summer to avoid poorly represented snow coversAfter 1 year of run, the soil subsystem is not yet in equilibrium, which should not affect applications involving atmospheric variables [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convection within the cloud‐resolving model embedded in each GCM column, has been shown to be a promising tool for simulating MCSs, particularly over the Tropics. In this work, we use ground‐based radar‐observed precipitation, North American Regional Reanalysis data, and a high‐resolution Weather Research and Forecasting simulation to evaluate in detail the MCS‐associated precipitation over the central United States predicted by a prototype MMF simulation that has a 2° host‐GCM grid. We show that the prototype MMF with nudged winds fails to capture the convective initiation in three out of four major MCS events during May 201x1 and underpredicts the precipitation rates for the remaining event, because the model cannot resolve the mesoscale drylines/fronts that are important drivers for initiating convection over the Southern Great Plains region. By reducing the host‐GCM grid spacing to 0.25° in the MMF and nudging the winds, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the cloud‐resolving model embedded in each GCM column further helps to boost precipitation rate. Plain Language Summary: Massive thunderstorms contribute a large proportion of warm season rainfall over the central United States. Previous studies have shown that the Multiscale Modeling Framework (MMF), which embeds a cloud‐resolving model (CRM) into each global climate model grid column to simulate convection, provides a promising tool for simulating massive thunderstorms in the Tropics. However, it is unclear whether the MMF can simulate similar thunderstorms in midlatitudes such as in the central United States. Therefore, this study compares the MMF simulations with detailed available observations over the central United States. We find that the commonly used MMF with 2°‐host‐GCM (~200 km) grid spacing has difficulty in reproducing the observed rainfall because the host‐GCM grid spacing is too coarse to capture the mesoscale circulations (at scales of approximately tens of kilometers) that are important for triggering the convection. When the host‐GCM‐grid spacing is reduced to a quarter degree (~25 km), the model succeeds to trigger the convection, so the rainfall simulation is improved. This study shows the importance of better representation of mesoscale circulations in models for predicting massive thunderstorms. Key Points: Springtime MCS‐associated precipitation properties over Central U.S. are comprehensively evaluatedThe MMF simulation with a coarse host grid fails to capture convective initiation over the Southern Great Plain (SGP) regionA nudged MMF simulation with a 0.25° host grid improves convective initiation and better captures the precipitation over the SGP region [ABSTRACT FROM AUTHOR]

The effect of global climate model (GCM) time step-which also controls how frequently global and embedded cloud resolving scales are coupled-is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time-mean shortwave cloud forcing (∼10 W/m2) and longwave cloud forcing (∼5 W/m2) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom-heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. The results may also be useful for helping to tune them. [ABSTRACT FROM AUTHOR]

Experiments designed to separate the effect of atmospheric warming from the effect of shifts of the eddy-driven jet on shortwave (SW) cloud feedback are performed with three global climate models (GCMs). In each model a warming simulation produces a robust SW cloud feedback dipole, with a negative (positive) feedback in the high-latitudes (subtropics). The cloud brightening in high-latitudes that characterizes warming simulations is not produced by jet shifts alone in any of the models, but is highly sensitive to perturbations of freezing temperature seen by the cloud microphysics scheme, indicating that thermodynamic mechanisms involving the phase of cloud condensate dominate the SW feedback at high-latitudes. In one of the models a poleward jet shift causes significant cloud dimming throughout the midlatitudes, but in two models it does not. Differences in cloud response to jet shifts in two of the models are attributed to differences in the shallow convection parameterizations. [ABSTRACT FROM AUTHOR]

This study presents the first attempt to simulate a full cycle of the quasi‐biennial oscillation (QBO) in a global storm‐resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about 5km $5\,\mathrm{k}\mathrm{m}$ and 400m $400\,\mathrm{m}$, respectively, we show that an untuned state‐of‐the‐art GSRM is already on the verge of simulating a QBO‐like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary‐scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary‐scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary‐scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio‐temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution. Plain Language Summary: The quasi‐biennial oscillation (QBO) is a wind system located in the equatorial stratosphere between ∼17 ${\sim} 17$ and ∼35km ${\sim} 35\hspace*{.5em}\mathrm{k}\mathrm{m}$ and consists of westerly and easterly wind jets that alternately propagate downward with time. The QBO has been shown to influence surface weather, so it is important to simulate the QBO realistically in the computer models typically used for climate research. However, these models often struggle to simulate a realistic QBO because they represent the processes leading up to the QBO, that is, tropical rain showers and short atmospheric waves excited by these rain showers, only empirically through so‐called parameterizations. In this study, we attempt for the first time to simulate the QBO in a model that directly represents these processes through an ultra‐fine grid. We find that our model maintains QBO‐like stratospheric winds throughout the simulation, and in the central stratosphere, the model simulates the characteristics of the QBO reasonably well for the right reasons. However, in the lowermost stratosphere, the simulated QBO is not realistic and does not move downward with time as observed due to a misrepresentation of long waves in the tropical atmosphere. These results will guide future model development to improve the model's representation of the QBO. Key Points: The ability of the general circulation model ICON, which explicitly simulates deep convection and gravity waves, to simulate a quasi‐biennial oscillation (QBO) is testedICON simulates a reasonable downward propagation and momentum balance of QBO‐like jets in the upper QBO domain in the first simulation yearSimulated QBO‐like jets stall in lower QBO domain due to a lack of planetary wave forcing due to weak convectively coupled equatorial waves [ABSTRACT FROM AUTHOR]

All‐sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all‐sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all‐sky assimilation framework with HCVs is modified to assimilate Fengyun‐3C/D Microwave Humidity Sounder‐II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi‐variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm‐core and the secondary circulation. This paper analyzes the characteristics of variational all‐sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all‐sky radiance assimilation. Plain Language Summary: Accurate forecasting of clouds and precipitation remains a challenging task in numerical weather prediction. Effective utilization of observation under cloudy conditions through data assimilation is crucial for improving the weather prediction. Current typical all‐sky assimilation methods can only indirectly assimilate cloud and precipitation in the model trajectory. In this study, the impact of enabling such direct updates is explored through the modification of the radiance assimilation framework. It is found that the enhanced radiance assimilation framework can better understand the cloud and precipitation information and further influence the thermal and dynamical processes. Typhoon cases are used to test this new assimilation framework and found that the modeled hydrometeors are closer to observations and improves typhoon intensity prediction. This study demonstrates a potential approach by directly updating hydrometeors using satellites radiance and lead to more accurate weather predictions, especially for typhoon cases. Key Points: A 3D‐Var all‐sky radiance assimilation framework for FY‐3C/D MWHS‐II with hydrometeors control variables (HCVs) is establishedHydrometeors are initialized by radiance directly, through the combined effect with background error and the observation operatorBetter analysis of hydrometeors leads to improved typhoon inner‐core structure, thus enhancing the ability of typhoon intensity prediction [ABSTRACT FROM AUTHOR]

In simulations of radiative‐convective equilibrium (RCE), and with sufficiently large domains, organized convection enhances top of atmosphere outgoing longwave radiation due to the reduced cloud coverage and drying of the mean climate state. As a consequence, estimates of climate sensitivity and cloud feedbacks may be affected. Here, we use a multi‐model ensemble configured in RCE to study the dependence of explicitly calculated cloud feedbacks on the existence of organized convection, the degree to which convection within a domain organizes, and the change in organized convection with warming sea surface temperature. We find that, when RCE simulations with organized convection are compared to RCE simulations without organized convection, the propensity for convection to organize in RCE causes cloud feedbacks to have larger magnitudes due to the inclusion of low clouds, accompanied by a much larger inter‐model spread. While we find no dependence of the cloud feedback on changes in organization with warming, models that are, on average, more organized have less positive, or even negative, cloud feedbacks. This is primarily due to changes in cloud optical depth in the shortwave, specifically high clouds thickening with warming in strongly organized domains. The shortwave cloud optical depth feedback also plays an important role in causing the tropical anvil cloud area feedback to be positive which is directly opposed to the expected negative or near zero cloud feedback found in prior work. Plain Language Summary: Tropical clouds play an important role in the uncertainty associated with understanding how the Earth's climate responds to an imposed warming. Here, we look at how organized cloud systems associated with the tropics affect the processes that govern the climate response to warming. We find that strongly organized cloud systems reduce how strongly the Earth responds to warming. This is primarily associated with the changes in the optical properties of high clouds. These cloud properties impact the overall understanding of the role tropical clouds play in modulating the Earth's temperature and calls into question prior assumptions of its behavior. Key Points: Domains with organized convection have cloud feedbacks with the same sign but larger inter‐model spread than domains without organizationThe cloud feedback parameter is less positive, or even negative, for more strongly organized domainsIn radiative‐convective equilibrium, the tropical anvil cloud area feedback is positive due to the shortwave cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Understanding soil erosion and sediment transport from the hillslope scale to the regional scale is crucial for studies on water quality, soil‐water conservation, the lateral carbon cycle, environmental zoning and vulnerability. However, most existing erosion and sediment transport models are only applicable at the hillslope scale or for small watersheds with fine spatial resolutions (typically much less than 1 km). This study presents a process‐based soil erosion and sediment transport model for model applications designed for applications with coarse spatial (e.g., ≥10 km) and temporal (e.g., from hourly to daily) resolutions. This new model, referred to as VIC‐SED, effectively accounts for interactions between erosion and hydrological processes. This is achieved by tightly coupling the erosion processes with a hydrologically based Three‐layer Variable Infiltration Capacity (VIC‐3L) land surface model (LSM) and to a multi‐scale routing (MSR) model. VIC‐SED considers the impacts of (a) the spatio‐temporal variability of rainfall intensity on erosion processes and (b) soil moisture on the soil detachment process. VIC‐SED is evaluated in two watersheds. Results demonstrate that VIC‐SED is capable of reproducing water and suspended sediment discharges at coarse spatial resolutions and varying temporal scales varying from 15‐min to daily intervals. Our study indicates that the VIC‐SED model is a promising tool for studying and assessing the impacts of climate and land cover changes on suspended sediment yields over large regions using coarse spatial and temporal resolutions. Plain Language Summary: Soil erosion is a global issue impacting soil‐water conservation, making the qualification of sediment transport crucial for evaluating water quality and landscape evolution. Existing models predominantly focus on hillslopes or small watersheds, with limited applicability to larger spatial scales. This limitation is partly due to the complexity of erosion processes and the empirical formulations involved in the erosion and sediment transport. To address this gap, we developed a novel process‐based soil erosion and sediment transport model for large‐scale land surface modeling. This model is integrated with the sophisticated Three‐layer Variable Infiltration Capacity (VIC‐3L) hydrological model and a multi‐scale routing (MSR) scheme for water and sediment transport. It explicitly accounts for the spatial and temporal variability of rainfall intensity and formulates the impact of soil moisture on the soil detachment process. Results from two applications demonstrate the model's capability to predict soil erosion and sediment dynamics at large spatial and temporal scales. Key Points: A novel process‐based SED model is developed for applications with coarse spatial and temporal resolutions of precipitationThe SED model is coupled with VIC accounting for subgrid‐scale variability of rainfall intensity, soil, vegetation, and flow path lengthThe coupled VIC‐SED model is evaluated with two case studies and its model parameters are insensitive to spatial and temporal resolutions [ABSTRACT FROM AUTHOR]

Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy, which is largely controlled by boundary‐layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere‐only and ocean‐atmosphere coupled models. The diurnal cycles of convection in both simulations are significantly improved with small changes to their mean states. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at subdiurnal time scales. Relaxed quasi‐equilibrium for convective available potential energy holds in significant measure as a result. Future model improvement will focus on the remaining biases in the diurnal cycle, which may be further reduced by including stochastic entrainment and cold pools. Plain Language Summary: In this study, we tackled a common challenge in general circulation models concerning the timing of intense rainfall over land during summertime. Many models tend to predict the peak of precipitation too early in the day. To address this, our study introduced a new approach to simulate convection by accounting for the role of shallow convection in stabilizing rapid changes in the atmospheric boundary layer at shorter time scales. This approach delayed maximum precipitation over land by approximately three hours. This adjustment significantly improved the simulated precipitation, aligning them more closely with observations from satellite data. Overall, our research contributes to improving numerical models, bringing them closer to accurately simulating the intricate dynamics of convection and precipitation over land. Key Points: A new convective closure is applied to Geophysical Fluid Dynamics Laboratory's CMIP6 climate models AM4 (atmosphere‐only) and CM4 (ocean‐atmosphere coupled)The diurnal cycle of precipitation is significantly improved over landThe new closure does not significantly change many aspects of AM4 and CM4's mean state and variability aside from their diurnal precipitation cycles [ABSTRACT FROM AUTHOR]

On ensemble‐based coupled data assimilation, cross‐component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross‐component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so‐called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS‐inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal‐to‐noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS‐based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow‐varying oceanic component, but not vice versa. In comparison with cross‐component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias. Plain Language Summary: The sequential data assimilation, exemplified by the ensemble‐based methods, focuses on adjusting model state and/or parameter instantaneously. Its underlying theoretical basis lies in the fact that random fluctuations characterize the Earth system in nature. Previous studies on ensemble‐based parameter estimation concentrated on the climatological response of state to parameter. However, this study emphasizes the instantaneous impact of parameter on state, denoted as the instantaneous parameter sensitivity (IPS), which is crucial for interpretating the feasibility of cross‐component parameter estimation (CPE) in the scope of strongly coupled data assimilation (CDA). Assimilating fast‐varying atmospheric observation has been employed to estimate slow‐varying oceanic state, but it cannot be taken for granted to optimize oceanic parameter. Conversely, assimilating oceanic observation can optimize atmospheric parameter and coupling coefficient since the ocean significantly responds to their direct modulation. The IPS serves as a tool for researchers to preliminarily assess the feasibility of CPE, representing a valuable advance particularly in the realm of strongly CDA. Key Points: The instantaneous parameter sensitivity plays a crucial role in interpreting the feasibility of ensemble‐based parameter estimationThe significant instantaneous response of model state to parameter maintains a high signal‐to‐noise ratio in their error covarianceAtmospheric parameter and coupling coefficient related to ocean‐atmosphere interaction can be optimized by assimilating oceanic observation [ABSTRACT FROM AUTHOR]

The computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high‐resolution aerosol assimilation system. A 2‐dimensional variational method (2DVar) was implemented in a non‐hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar‐orbital satellites. The model results were evaluated against ground‐based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite‐estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite‐retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground‐based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes. Plain Language Summary: High‐resolution models and data assimilation techniques should be used to obtain more accurate aerosol distributions. We combined a high‐resolution model and an assimilation method at a low computational cost. We also integrated multiple aerosol satellite products. The new procedure successfully reproduced the aerosol observations. The results were significantly better when observations for multiple grids and times were available. Surface measurements on land, for which satellites often lack aerosol signals, are helpful. Key Points: Combined geostationary and polar orbital satellite aerosol products were assimilatedThe aerosol assimilation results improved with increasing spatiotemporal coverage of the observationsEven though the column‐integrated aerosol optical depth was assimilated, surface aerosol (PM2.5) concentration improved [ABSTRACT FROM AUTHOR]

Abstract The responses of tropical anvil cloud and low‐level cloud to a warming climate are among the largest sources of uncertainty in our estimates of climate sensitivity. However, most research on cloud feedbacks relies on either global climate models with parameterized convection, which do not explicitly represent small‐scale convective processes, or small‐domain models, which cannot directly simulate large‐scale circulations. We investigate how self‐aggregation, the spontaneous clumping of convection in idealized numerical models, depends on cloud‐radiative interactions with different cloud types, sea surface temperatures (SSTs), and stages of aggregation in simulations that form part of RCEMIP (the Radiative‐Convective Equilibrium Model Intercomparison Project). Analysis shows that the presence of anvil cloud, which tends to enhance aggregation when collocated with anomalously moist environments, is reduced in nearly all models when SSTs are increased, leading to a corresponding reduction in the aggregating influence of cloud‐longwave interactions. We also find that cloud‐longwave radiation interactions are stronger in the majority of General Circulation Models (GCMs), typically resulting in faster aggregation compared to Cloud‐system Resolving Models (CRMs). GCMs that have stronger cloud‐longwave interactions tend to aggregate faster, whereas the influence of circulations is the main factor affecting the aggregation rate in CRMs.

Abstract Convections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in‐cloud inhomogeneities, a new convective cloud model explicitly considering in‐cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub‐plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single‐column tests show a stronger mass flux profile and low‐level detrainment with the new model. In global simulations, the long‐lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is over‐enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

General circulation models (GCMs) typically have a grid size of 25–200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for subgrid‐scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational data sets and global cloud‐system resolving model (CRM) simulations to learn subgrid variability. This article describes an NN parametrization trained by coarse‐graining a near‐global CRM simulation with a 4‐km horizontal grid spacing. The NN predicts the residual heating and moistening averaged over (160 km)2 grid boxes as a function of the coarse‐resolution fields within the same atmospheric column. This NN is coupled to the dynamical core of a GCM with the same 160‐km resolution. A recent study described how to train such an NN to be stable when coupled to specified time‐evolving advective forcings in a single‐column model, but feedbacks between NN and GCM components cause spatially extended simulations to crash within a few days. Analyzing the linearized response of such an NN reveals that it learns to exploit a strong synchrony between precipitation and the atmospheric state above 10 km. Removing these variables from the NN's inputs stabilizes the coupled simulations, which predict the future state more accurately than a coarse‐resolution simulation without any parametrizations of subgrid‐scale variability, although the mean state slowly drifts. Key Points: A neural‐network parametrization is coupled to a general circulation modelThe neural network is trained by coarse‐graining a near‐global cloud‐system resolving simulationThe coupled simulation doubles the forecast accuracy of the general circulation model [ABSTRACT FROM AUTHOR]

This study investigates the effects of irrigation on global water resources by performing and analyzing Community Land Model 4.0 (CLM4) simulations driven by downscaled/bias-corrected historical simulations and future projections from five General Circulation Models (GCMs). For each climate scenario, three sets of numerical experiments were performed: (1) a CTRL experiment in which all crops are assumed to be rainfed; (2) an IRRIG experiment in which the irrigation module is activated using surface water (SW) to feed irrigation; and (3) a PUMP experiment in which a groundwater pumping scheme coupled with the irrigation module is activated for conjunctive use of surface water and groundwater (GW) for irrigation. The parameters associated with irrigation and groundwater pumping are calibrated based on a global inventory of census-based water use compiled by the Food and Agricultural Organization (FAO). Our results suggest that irrigation could lead to two major effects: SW (GW) depletion in regions with irrigation primarily fed by SW (GW), respectively. Furthermore, irrigation depending primarily on SW tends to have larger impacts on low-flow than high-flow conditions, suggesting increased vulnerability to drought. By the end of the 21st century, combined effect of increased irrigation water demand and amplified temporal-spatial variability of water supply may lead to severe local water scarcity for irrigation. Regionally, irrigation has the potential to aggravate/alleviate climate-induced changes of SW/GW although such effects are negligible when averaged globally. Our study highlights the need to account for irrigation effects and sources in assessing regional climate change impacts. [ABSTRACT FROM AUTHOR]

Previous research quantified the sensitivity of limiting intensity to SST for observed tropical cyclones (TCs) and for TCs generated by two global climate models (GCMs). On average, a 1° C increase in sea surface temperature (SST) is associated with a 7.9 m s−1 increase in the statistical upper limit of observed intensity. Conversely, a 1°C increase in SST does not significantly affect the limiting intensity of GCM-generated TCs. The study presented here builds on previous research in two ways: (1) A comparison is made between the statistically defined limiting intensity and the physically defined potential intensity, and (2) a test is performed on the ability of a ∼0.94° resolution GCM to reproduce the observed statistical relationship between potential intensity and SST. Data from NASA's Modern Era Reanalysis are used to approximate the observed sensitivity of potential intensity to SST for the 1982-2008 time period. Results indicate that the sensitivity of potential intensity to SST is not statistically different from the sensitivity of observed maximum or limiting intensity to SST. This result links the statistically defined sensitivity to the physically based theory of hurricanes. Potential intensity is also estimated from the FSU/COAPS GCM. Although the FSU/COAPS model does not capture the observed sensitivity of TC maximum or limiting intensity to SST, the model reproduces the observed sensitivity of potential intensity to SST. The model generates suitable atmospheric conditions for the development of strong TCs, however strong TCs do not develop, possibly as a result of insufficient resolution. [ABSTRACT FROM AUTHOR]

Horizontal resolution of general circulation models (GCMs) has significantly increased during the last decade, however these changes were not accompanied by similar changes in vertical resolution. In our study, the Community Atmosphere Model, version 5 (CAM5) is used to study the sensitivity of climate to vertical resolution and nonorographic gravity wave drag. Nonorographic gravity wave drag is typically omitted from low-top GCMs, however as we show, its influence on climate can be seen all the way to the surface. We show that an increase in vertical resolution from 1200 to 500 m in the free troposphere and lower stratosphere in CAM5 improves the representation of near-tropopause temperatures, lower stratospheric temperatures, and surface wind stresses. In combination with nonorographic gravity waves, CAM5 with increased vertical resolution produces a realistic Quasi-Biennial Oscillation (QBO), has an improved seasonal cycle of temperature in the extratropics, and represents better the coupling between the stratosphere and the troposphere. [ABSTRACT FROM AUTHOR]

Representing spatially varying precipitation for current grid length scales used in General Circulation Models (GCMs) is a continuing challenge. Furthermore, to fully capture the hydrologic effects of nonuniform precipitation, a representation of soil moisture heterogeneity and distribution of spatially varying precipitation must exist within the same framework. For this study, the explicit and sampling methods of Sellers et al. (2007) are tested off-line using the Simple Biosphere Model (SiB3) in an arid, semiarid, and wet site, and are numerically compared to the bulk method, which is currently used in GCMs. To carry out the numerical experiments, an arbitrary grid area was defined by (1) a single instance of SiB3 (bulk method), (2) 100 instances of SiB3 (explicit method), and (3) less than 100 instances of SiB3 (sampling method). Precipitation was randomly distributed over fractions of the grid area for the explicit and sampling methods, while the standard SiB3 exponential distribution relating precipitation intensity to the grid area wet fraction was used in the bulk method. Comparing the sampling and bulk method to the explicit method indicates that 10 instances of SiB3 in the sampling method better captures the spatial variability in soil moisture and grid area flux calculations produced by the explicit method, and deals realistically with spatially varying precipitation at little additional computational cost to the bulk method. [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]

The common practice in dynamic downscaling is to nest a higher‐resolution regional climate model (RCM) into a global model that resolves the large‐scale circulation. However, nested RCMs can develop distinct large‐scale features that substantially diverge from those of the driving model. This is especially problematic over regions such as South America (SA), where the climate features strong teleconnection with remote oceans. Here we propose to explicitly resolve the atmospheric processes underlying the teleconnection by expanding the RCM domain to include the influential oceans. Using the coupled RegCM4.3.4‐CLM4.5 model, RCM simulations designed under the new paradigm demonstrate a substantial improvement of model skills over those using the standard CORDEX SA domain. Analysis of the underlying physical mechanisms indicates that the RCM captures the large‐scale dynamics and climate teleconnections substantially better when it includes the influential oceans. The Big Brother experimental protocol is then used to identify sources of uncertainties and skills, and the results suggest that the nesting practice cannot effectively capture the impact of forcings and processes acting outside the RCM domain. This uncertainty introduces substantial systematic bias to RCM simulations yet is not sampled by existing coordinated regional modeling projects (e.g., CORDEX) due to the use of a single domain focusing over land. Explicitly including oceans within the domain substantially reduces the sensitivity of the SA model climate to domain size/location and promises great potential for RCM applicability in studying regional mechanisms and feedback processes of SA's hydroclimate. Key Points: Large‐scale circulation in nested RCMs can substantially deviate from those of the driving GCMs resulting in large circulation biasesThe bias is, in part, due to the nesting practice and can become excessive for regions with strong teleconnectionsOur results over SA reveal that including influential SSTs in RCM domains enhances the model skill in resolving key large‐scale processes [ABSTRACT FROM AUTHOR]

Abstract: Many conventional General Circulation Models (GCMs) in the Global Land‐Atmosphere Coupling Experiment (GLACE) tend to produce what is now recognized as overly strong land‐atmosphere (L‐A) coupling. We investigate the effects of cloud Superparameterization (SP) on L‐A coupling on timescales beyond diurnal where it has been recently shown to have a favorable muting effect hydrologically. Using the Community Atmosphere Model v3.5 (CAM3.5) and its Superparameterized counterpart SPCAM3.5, we conducted soil moisture interference experiments following the GLACE and Atmospheric Model Intercomparison Project (AMIP) protocols. The results show that, on weekly‐to‐subseasonal timescales, SP also mutes hydrologic L‐A coupling. This is detectable globally, and happens through the evapotranspiration‐precipitation segment. But on seasonal timescales, SP does not exhibit detectable effects on hydrologic L‐A coupling. Two robust regional effects of SP on thermal L‐A coupling have also been explored. Over the Arabian Peninsula, SP reduces thermal L‐A coupling through a straightforward control by mean rainfall reduction. More counterintuitively, over the Southwestern US and Northern Mexico, SP enhances the thermal L‐A coupling in a way that is independent of rainfall and soil moisture. This signal is associated with a systematic and previously unrecognized effect of SP that produces an amplified Bowen ratio, and is detectable in multiple SP model versions and experiment designs. In addition to amplifying the present‐day Bowen ratio, SP is found to amplify the climate sensitivity of Bowen ratio as well, which likely plays a role in influencing climate change predictions at the L‐A interface. [ABSTRACT FROM AUTHOR]

Abstract: Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid‐scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k‐means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution. [ABSTRACT FROM AUTHOR]

Abstract: Low clouds strongly impact the radiation budget of the climate system, but their simulation in most GCMs has remained a challenge, especially over the subtropical stratocumulus region. Assuming a Gaussian distribution for the subgrid‐scale total water and liquid water potential temperature, a new statistical cloud scheme is proposed and tested in NCAR Community Atmospheric Model version 5 (CAM5). The subgrid‐scale variance is diagnosed from the turbulent and shallow convective processes in CAM5. The approach is able to maintain the consistency between cloud fraction and cloud condensate and thus alleviates the adjustment needed in the default relative humidity‐based cloud fraction scheme. Short‐term forecast simulations indicate that low cloud fraction and liquid water content, including their diurnal cycle, are improved due to a proper consideration of subgrid‐scale variance over the southeastern Pacific Ocean region. Compared with the default cloud scheme, the new approach produced the mean climate reasonably well with improved shortwave cloud forcing (SWCF) due to more reasonable low cloud fraction and liquid water path over regions with predominant low clouds. Meanwhile, the SWCF bias over the tropical land regions is also alleviated. Furthermore, the simulated marine boundary layer clouds with the new approach extend further offshore and agree better with observations. The new approach is able to obtain the top of atmosphere (TOA) radiation balance with a slightly alleviated double ITCZ problem in preliminary coupled simulations. This study implies that a close coupling of cloud processes with other subgrid‐scale physical processes is a promising approach to improve cloud simulations. [ABSTRACT FROM AUTHOR]