Search

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

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

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

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

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

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

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

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

This study further evaluates the modeling approach of Jia et al. (2019), https://doi.org/10.1029/2019gl081960 (JEA19) to investigate the potential effects of mesoscale sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a slab ocean model to produce two ensembles of simulations: one in which the SST exhibits realistic mesoscale variability, and another in which the mesoscale SST variability is suppressed. The results of the present study suggest that the modeling approach yields the desired SST differences between the two ensembles at the mesoscales. They also show, however, that the approach can lead to undesirable SST differences at the large scales, if the prescribed pair of oceanic heat flux fields have large scale differences. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by nonlinear interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat flux fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Plain Language Summary: This study evaluates a modeling approach to investigate the potential effects of mesoscale (scales smaller than 100 km) ocean sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a thermodynamical model of the ocean. Two ensembles of model simulations are prepared: one in which the ocean exhibits realistic mesoscale SST variability, and another in which that variability is suppressed. The results suggest that the modeling approach yields the desired mesoscale SST differences. They also show, however, that the approach can lead to undesirable large scale SST differences, if there are large scale differences between the prescribed oceanic heat transport fields of the two ensembles. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by two‐way interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat transport fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Key Points: The large scale response in an uncoupled atmospheric simulation is the response of the model rather than that of the atmosphereA coupled modeling approach is necessary to investigate the large scale atmospheric response to mesoscale sea surface temperature variabilityA pair of oceanic heat fluxes that do not have large scale differences is critical to produce a realistic large scale atmospheric response [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require O(105) model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(102) model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties. Plain Language Summary: Calibrating climate models with available data and quantifying their uncertainties are essential to make climate predictions accurate and actionable. A primary source of uncertainties in climate models comes from representation of small‐scale processes such as moist convection. Parameters in convection schemes and other parameterizations are usually calibrated by hand, using only a small fraction of data that are available. As a result, the calibration process may miss information about the small‐scale processes in question. This paper presents a proof‐of‐concept, in an idealized setting, of how parameters in climate models can be calibrated using a substantial fraction of the available data, and how uncertainties in the parameters can be quantified. We employ a new algorithm, called calibrate‐emulate‐sample (CES), which makes such calibration and uncertainty quantification feasible for computationally expensive climate models. CES reduces the hundreds of thousands of model runs usually required to quantify uncertainties in computer models to hundreds, thereby achieving about a factor 1,000 speedup. It leads to more robust calibration and uncertainty quantification in the presence of noise arising from chaotic variability of the climate system. We show how uncertainties in climate model parameters can be translated into quantified uncertainties of climate predictions through ensemble integrations. Key Points: We use time averaged climate statistics to calibrate convective parameters and quantify their uncertaintiesWe demonstrate use of the calibrate‐emulate‐sample algorithm to provide efficient calibration and uncertainty quantificationParametric uncertainty in climate predictions is quantified by sampling from the learnt convective parameter distribution [ABSTRACT FROM AUTHOR]

Abstract This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.