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Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the turbulent-kinetic-energy-based (TKE-based) simplified higher-order closure (SHOC) – a simplified version of the assumed-double-Gaussian-PDF (probability density function) higher-order-closure method. In this paper, we implement a stochastic-multiplume mass-flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian-PDF closures and couple it to SHOC (SHOC + MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference data from large-eddy simulations, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC + MF parameterization shows weak sensitivity to the vertical grid resolution and model time step. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

Although the surface energy budget is essential to determine Earth's climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends. [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 paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario. [ABSTRACT FROM AUTHOR]

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation. [ABSTRACT FROM AUTHOR]

Wind stress and turbulent heat fluxes are the major driving forces that modify the ocean dynamics and thermodynamics. In the Nucleus for European Modelling of the Ocean (NEMO) ocean general circulation model, these turbulent air–sea fluxes (TASFs) can critically impact the simulated ocean characteristics. This paper investigates how the various bulk parameterizations used to calculate turbulent air-sea fluxes in NEMOv4 can lead to substantial differences in the estimation of sea surface temperatures (SSTs). Specifically, we study the contributions of different aspects and assumptions of the bulk parameterizations in driving the SST differences in the NEMO global model configuration at 1/4 ∘ of horizontal resolution. These aspects include the use of the skin temperature instead of the bulk SST in the computation of turbulent heat flux components and the estimation of wind stress and turbulent heat flux components, which vary in each parameterization due to different bulk transfer coefficients. The analysis of a set of short-term sensitivity experiments where the only change is related to one of the aspects of the bulk parameterizations shows that parameterization-related SST differences are primarily sensitive to wind stress differences and to the implementation of skin temperature in the computation of turbulent heat flux components. In addition, in order to highlight the role of SST–turbulent heat flux negative feedback at play in ocean simulations, we compare the TASF differences obtained using the NEMO ocean model with the estimations by , who compared the different bulk parameterizations using prescribed SSTs. Our estimations of turbulent heat flux differences between bulk parameterizations are weaker than those found by. [ABSTRACT FROM AUTHOR]

The newly developed fast Earth system model CLIMBER-X is presented. The climate component of CLIMBER-X consists of a 2.5-D semi-empirical statistical–dynamical atmosphere model, a 3-D frictional–geostrophic ocean model, a dynamic–thermodynamic sea ice model and a land surface model. All the model components are discretized on a regular lat–long grid with a horizontal resolution of 5∘×5∘. The model has a throughput of ∼ 10 000 simulation years per day on a single node with 16 CPUs on a high-performance computer and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to >100000 years. A comprehensive evaluation of the model performance for the present day and the historical period shows that CLIMBER-X is capable of realistically reproducing many observed climate characteristics, with results that generally lie within the range of state-of-the-art general circulation models. The analysis of model performance is complemented by a thorough assessment of climate feedbacks and model sensitivities to changes in external forcings and boundary conditions. Limitations and applicability of the model are critically discussed. CLIMBER-X also includes a detailed representation of the global carbon cycle and is coupled to an ice sheet model, which will be described in separate papers. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate. [ABSTRACT FROM AUTHOR]

Synoptic eddies (with a period of two to eight days) are active in the South China Sea-Philippine Sea (SCS-PS) and control weather variations. In addition, the intensity and frequency of synoptic eddies may change along with variations in sea surface temperatures (SST). This paper presented the influence of SST in the northwest Indian Ocean on synoptic eddies in the lower troposphere over the SCS-PS in June. Our statistical analysis showed a significant negative correlation between the SST in the northwest Indian Ocean and the synoptic scale eddy kinetic energy (EKE) in the SCS-PS. By analyzing the EKE budget of synoptic eddies, we found that the variation in the synoptic scale EKE over the SCS-PS is mainly due to the change in the monthly zonal wind gradient, which affects the barotropic energy conversion between the monthly mean flow and the synoptic eddies. Additionally, the northwest Indian Ocean SST modulates the monthly flow over the SCS-PS by alternating the strength of the Walker circulation in the west Pacific and Indian Ocean. Finally, the influence of SST in the northwest Indian Ocean on EKE in the SCS-PS was reproduced using the simplified atmospheric general circulation model, SPEEDY. [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]

Observations of vibrationally excited hydroxyl (OH*) emissions are widely used to obtain information on atmospheric dynamics and composition. In this paper, several analytical approximations are presented for characteristics of the hydroxyl layer in the Martian atmosphere, such as OH* concentrations at the maximum and the height of the maximum. Relationships are also given for estimating the influence of various factors on the OH* layer in nighttime conditions. These characteristics are determined by the ambient temperature and the concentration of atomic oxygen, including their vertical gradients. The obtained relationships are applied to the results of numerical modeling using the global atmospheric circulation model to predict the seasonal behavior of the hydroxyl layer on Mars. Based on the modeling data, the annual and intra-annual variations in the concentration of excited hydroxyl and the height of the OH* layer on Mars show both similarities to and considerable differences from those on Earth. The concentration and the height of the maximum in the equatorial, northern, and southern middle latitudes vary with the season, with the maximum concentrations and the lowest height being recorded in the first half of the year. Model calculations confirmed the presence of a peak in the OH* concentration in polar latitudes in winter at a height of approximately 50 km with volume emission densities of 2.1, 1.4, and 0.6 × 104 photons cm–3 s–1 for the transitions of vibrational levels 1–0, 2–1, and 2– 0, respectively. The resulting relationships can be used to analyze measurements and interpret their variations. [ABSTRACT FROM AUTHOR]

To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation. [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]

Atmospheric radiation is one of the most important atmospheric physics, and its expensive computation cost severely restricts the numerical simulation of atmospheric general circulation models. Therefore, it is necessary to study an efficient radiation parameterization scheme. Due to the powerful computing power of GPU, more and more numerical models are being transplanted to GPU. The CUDA C version (CC-RRTMG_SW) of the rapid radiative transfer model for general circulation models (RRTMG) shortwave radiation scheme (RRTMG_SW) has successfully run on GPU, but its computing efficiency is not yet very high, and the performance potential of GPU computing needs to be realized further. This paper is dedicated to optimizing CC-RRTMG_SW and exploring its maximum computing performance on GPU. First, a three-dimensional acceleration algorithm for CC-RRTMG_SW is proposed. Then, some optimization methods, such as decoupling data dependency, optimizing memory access, and I/O optimization, are studied. Finally, the optimized version of CC-RRTMG_SW is developed, namely CC-RRTMG_SW++. The experimental results demonstrate that the proposed acceleration algorithm and performance optimization methods are effective. CC-RRTMG_SW++ achieved good acceleration effects on different GPU architectures, such as NVIDIA Tesla K20, K40, and V100. Compared to RRTMG_SW running on a single Intel Xeon E5-2680 v2 CPU core, CC-RRTMG_SW++ obtained a speedup of 99.09 × on a single V100 GPU without I/O transfer. Compared to CC-RRTMG_SW, the computing efficiency of CC-RRTMG_SW++ increased by 174.46%. [ABSTRACT FROM AUTHOR]

Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the TKE-based Simplified Higher-Order Closure (SHOC)--a simplified version of the assumed doubled-Gaussian PDF higher-order closure method. In this paper, we implement a stochastic multiplume Mass-Flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian PDF closures and couple it to SHOC (SHOC+MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference large-eddy simulation data, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC+MF parameterization shows weak sensitivity to the vertical grid resolution and model time step. [ABSTRACT FROM AUTHOR]

In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (ML) parameterizations for convection and clouds have been demonstrated to have the potential to perform better than conventional parameterizations. Most previous studies were conducted on aqua-planet and idealized models, and the problems of simulation instability and climate drift still exist. Developing an ML parameterization scheme remains a challenging task in realistically configured models. In this paper, a set of residual deep neural networks (ResDNNs) with a strong nonlinear fitting ability is designed to emulate a super-parameterization (SP) with different outputs in a hybrid ML–physical general circulation model (GCM). It can sustain stable simulations for over 10 years under real-world geographical boundary conditions. We explore the relationship between the accuracy and stability by validating multiple deep neural network (DNN) and ResDNN sets in prognostic runs. In addition, there are significant differences in the prognostic results of the stable ResDNN sets. Therefore, trial and error is used to acquire the optimal ResDNN set for both high skill and long-term stability, which we name the neural network (NN) parameterization. In offline validation, the neural network parameterization can emulate the SP in mid- to high-latitude regions with a high accuracy. However, its prediction skill over tropical ocean areas still needs improvement. In the multi-year prognostic test, the hybrid ML–physical GCM simulates the tropical precipitation well over land and significantly improves the frequency of the precipitation extremes, which are vastly underestimated in the Community Atmospheric Model version 5 (CAM5), with a horizontal resolution of 1.9 ∘ × 2.5 ∘. Furthermore, the hybrid ML–physical GCM simulates the robust signal of the Madden–Julian oscillation with a more reasonable propagation speed than CAM5. However, there are still substantial biases with the hybrid ML–physical GCM in the mean states, including the temperature field in the tropopause and at high latitudes and the precipitation over tropical oceanic regions, which are larger than those in CAM5. This study is a pioneer in achieving multi-year stable climate simulations using a hybrid ML–physical GCM under actual land–ocean boundary conditions that become sustained over 30 times faster than the target SP. It demonstrates the emerging potential of using ML parameterizations in climate simulations. [ABSTRACT FROM AUTHOR]

Modern general circulation models continue to require parameterizations of subgrid transport due to planetary boundary layer (PBL) turbulence and convection. Some schemes that unify these processes rely on assumed joint probability distributions of vertical velocity and moist conserved thermodynamic variables to predict the subgrid-scale contribution to the mean state of the atmosphere. The multivariate double-Gaussian mixture has been proposed as an appropriate model for PBL turbulence and shallow convection, but it is unable to reproduce important features of shallow cumulus convection. In this study, a novel unified PBL turbulence–convection–cloud macrophysics scheme is presented based on the eddy-diffusivity/mass-flux framework. The new scheme augments the double-Gaussian representation of subgrid variability with multiple stochastic mass-flux plumes at minimal added computational cost. Improved results for steady-state maritime and transient continental shallow convection from a single-column model implementation of the new scheme are shown with respect to reference large-eddy simulations. Improvements are seen in the cloud layer due to mass-flux plumes occupying the extreme moist, low liquid-water potential temperature tail of the joint temperature–moisture distribution. Significance Statement: Computer models of the atmosphere used to predict future climate are unable to directly represent air motion at small spatial scales because it would take too long to run the model over the entire planet. Instead, models typically use coarse model grid spacing and a simplified statistical representation of the physical processes that cause small-scale motions. This paper improves a particular simplified representation by adding a mechanism to represent statistically rare events of strong small-scale air motion that coherently transport air from near the surface to higher in the atmosphere. This increased transport also improves the representation of clouds, a particularly difficult phenomenon to simulate in models. [ABSTRACT FROM AUTHOR]

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement of Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the Southeast Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the longstanding biases associated with marine stratocumulus in GCMs when compared to the standard resolution control simulation. [ABSTRACT FROM AUTHOR]

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held–Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection–condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima, which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the largescale condensation and formation cycle. In addition, counterintuitive relationships}such as cloud condensate and cloud fraction responding in opposing directions}emphasize the need for careful dissection of physical mechanisms. In keeping with advection–condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain. [ABSTRACT FROM AUTHOR]

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

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management. • Development of streamflow prediction model for climatic projection data. • Streamflow estimation using limited input data in small catchment. • Obtaining far future streamflow for water resources pre-planning. • Discussion of the impact of climate change on daily streamflow extremes. [ABSTRACT FROM AUTHOR]

Global climate models, like any in silico numerical experiments, are affected by different types of bias. Uncertainty quantification remains a challenge in any climate detection and attribution analysis. A fundamental methodological question is to determine which statistical summaries, while bringing relevant signals, can be robust with respect to multimodel errors. In this paper, we propose a simple statistical framework that significantly improves signal detection in climate attribution studies. We show that the complex bias correction step can be entirely bypassed for models for which bias between the simulated and unobserved counterfactual worlds is the same as between the simulated and unobserved factual worlds. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. The detected anthropogenic signal in yearly maxima of daily precipitation clearly emerges at the beginning of the twenty-first century. In addition, no CMIP model seems to outperform the others and a weighted linear combination of all improves the estimation of emergence times. Significance Statement: We show that the bias in multimodel global climate simulations can be efficiently handled when the appropriate metric is chosen. This metric leads to an easy-to-implement statistical procedure based on a checkable assumption. This allows us to demonstrate that optimal convex combinations of CMIP outputs can improve the signal strength in finding emergence times. Our data analysis procedure is applied to yearly maximum of precipitation from CMIP5 and CMIP6 databases. The attribution of the anthropogenic forcing clearly emerges in extreme precipitation at the beginning of the twenty-first century. [ABSTRACT FROM AUTHOR]

Although tropical interbasin interactions at interannual time scales are presently receiving much attention, their controlling factors and variations on longer time scales are under debate. Tropical convection plays a crucial role in the occurrence and nonstationarity of them. In this paper, we investigate the dependence of interannual tropical Atlantic–Pacific basin interactions on convection-related features of the tropical oceans' climatology, especially the ITCZ position. We contrast a CGCM control simulation with an experiment in which tropical convection is modified by an artificial perturbation outside the tropics that reduces the incident shortwave radiation in a region of the South Atlantic. Based on previous work, this modification is expected to shift in latitude the climatological position of the simulated ITCZ. The experiment shows altered Walker circulations, stronger interannual variability over the tropical oceans, a westward extension of the Atlantic Niño pattern and of convection, and shallower thermocline in the Pacific, making the basin more sensitive to both local and remote perturbations. As a consequence, the experiment shows enhanced interannual Atlantic–Pacific basin interactions at the equator, and weaker teleconnections between the north tropical Atlantic and the equatorial Pacific. The latter seems to occur because the impact of the warm Atlantic SST anomalies is offset by the presence of warm SST anomalies in El Niño region. Despite the uncertainties raised because the simulations are relatively short, we conclude that this work presents a potential explanation for the long-term changes in the tropical basin interactions and offers a novel and useful methodology for their analysis. [ABSTRACT FROM AUTHOR]

Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each climate simulation of the MME. In contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM–RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM–RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes (i) piecewise linear functions to model the changes in the three GEV parameters and (ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM–RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps. Annual maxima are available for 20 adjusted GCM–RCM pairs from the EURO-CORDEX experiment under the scenario Representative Concentration Pathway (RCP) 8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs by -2.9 kN m -2 (-50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends the recent idea to constrain climate projection ensembles using past observations to climate extremes. [ABSTRACT FROM AUTHOR]

The literature offers conflicting findings about which regions contribute most to increases in the global radiative feedback after a forcing increase. This paper explains the disagreement by discriminating between two common definitions of the local feedback, which use either local temperature or global temperature as their basis. Although the two definitions of feedback have been previously compared in aquaplanet models with slab oceans, here the definitions are compared for the first time in an atmosphere-ocean general circulation model (MPI-ESM1.2) integrated over four doublings of atmospheric CO2 concentrations. Large differences between the definitions can be seen in all feedbacks, but especially in the temperature and water vapor feedbacks. Differences of up to 10 W m-2 K-1 over the Southern Ocean can be explained by the pattern of surface warming, which weights the local feedbacks and reduces their contribution to the global mean. This finding is, however, dependent on the resolution of analysis, because the local-temperature definition is mathematically inconsistent across spatial scales. Furthermore, attempts to estimate the effect of "pattern weighting" by separating local feedbacks and warming patterns at the gridcell level fail, because the radiative change in key tropical regions is also determined by tropospheric stability via the global circulation. These findings indicate that studies of regional feedback change are more sensitive to methodological choices than previously thought, and that the tropics most likely dominate regional contributions to global radiative feedback change on decadal to centennial time scales. [ABSTRACT FROM AUTHOR]

Previous studies have shown that Kelvin–Rossby instability is a viable mechanism for producing equatorial superrotation in small and/or slowly rotating planets. It is shown in this paper that this mechanism can also produce superrotation with terrestrial parameters when the baroclinic forcing moves to low latitudes, explaining previous results by Williams. The transition between superrotating and subrotating flow occurs abruptly as the baroclinic forcing moves poleward. Although Kelvin–Rossby instability weakens when the baroclinic zone moves away from the equator, the key factor explaining the abrupt transition is the change in the baroclinic eddies. When differential heating is contained within the tropics, baroclinic eddies do not decelerate the subtropical jet and the upper-tropospheric flow approximately conserves angular momentum, providing conditions favorable for Kelvin–Rossby instability. In contrast, when baroclinic eddies are generated in the extratropics, they decelerate the subtropical jet and prevent the Kelvin–Rossby coupling. Due to this sensitivity to baroclinic eddies, the system exhibits hysteresis: near the transition parameter, extratropical eddies can prevent superrotation when they are initially present. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the results of the Weather Research and Forecasting (WRF) model of solar radiation for moderate climatic zones. This analysis covered the area of northeastern Germany. Due to very unfavorable solar energy conditions in this region for at least 1/3 of the year, we decided to select the dates with the most representative conditions: passing warm fronts, cold fronts, and occluded fronts (two cases each). As the reference, two cloudless conditions during high-pressure situations were chosen. Two different shortwave radiation schemes—Rapid Radiative Transfer Model for general circulation model (RRTMG) and Dudhia—were tested. The obtained results were compared with in situ data measured at Deutscher Wetterdienst (DWD) stations and then with European Medium-Range Weather Forecast reanalysis (ERA5) data. The results showed that for high-pressure situations, the mean correlations with measured data were above 90%. The Dudhia scheme, in addition to the expected good results for the high-pressure situation, showed better results than RRTMG for the warm and cold fronts as well. The forecast using the RRTMG scheme gave the best results for the occluded front, which were also better than those of the ERA5 model. [ABSTRACT FROM AUTHOR]

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

Climate models project an intensification of the wintertime North Atlantic Ocean storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean–atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies. Significance Statement: By the end of this century, the North Atlantic Ocean storm track is projected to intensify on its eastward flank. Such intensification will have large societal impacts, mostly over western Europe. Thus, it is critical to better understand the mechanism underlying the intensification of the storm track. Here we investigate the role of ocean coupling in the future intensification of the North Atlantic storm track and find that ocean heat transport processes are responsible for the strengthening of the storm track. Our results suggest that better monitoring the changes in ocean heat transport will hopefully improve climate change adaption strategies. [ABSTRACT FROM AUTHOR]

Titan’s stratosphere has been observed in a superrotation state, where the atmosphere rotates many times faster than the surface does. Another characteristic of Titan’s atmosphere is the presence of a thick haze layer. In this paper, we performed numerical experiments using a general circulation model to explore the effects of the haze layer on the stratospheric superrotation. We employed a semigray radiation model of Titan’s atmosphere following McKay et al., which takes account of sunlight absorption by the haze particles. The phase change of methane or seasonal changes were not taken into account. Our model with radiation parameters tuned for Titan yielded a global eastward wind around the equator with larger velocities at higher altitudes, except at around 70 km, after 105 Earth days. Although the atmosphere is not in an equilibrium state, the zonal wind profiles are approximately consistent with the observed one. By changing the parameters of the radiation model, we found that the intensity and the location of the maximum zonal wind velocity highly depended on the optical thickness and the altitude of the haze layer, respectively. Analysis of our experiments suggests that the quasi-stationary stratospheric superrotation is maintained by the balance between the meridional circulation decoupled from the surface and the eddies that transport angular momentum equatorward. This is different from, but similar to, the so-called Gierasch mechanism, in which momentum is supplied from the surface. This structure may explain the no-wind region at about 80 km in altitude. [ABSTRACT FROM AUTHOR]

The newly developed fast Earth system model CLIMBER-X is presented. The climate component of CLIMBER-X consists of a 2.5D semi-empirical statistical-dynamical atmosphere model, a 3D frictional-geostrophic ocean model, a dynamic-thermodynamic sea ice model and a land surface model. All model components are discretized on a regular lat-lon grid with a horizontal resolution of 5° × 5°. The model has a throughput of ~10,000 simulation years per day on a single node with 16 CPUs on a high performance computer and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to > 100,000 years. A comprehensive evaluation of the model performance for present day and the historical period shows that CLIMBER-X is capable of realistically reproducing many observed climate characteristics, with results that generally lie within the range of state-of-the-art general circulation models. The analysis of model performance is complemented by a thorough assessment of climate feedbacks and model sensitivities to changes in external forcings and boundary conditions. CLIMBER-X also includes a detailed representation of the global carbon cycle and is coupled to an ice sheet model, which will be described in separate papers. CLIMBER-X is available as open--source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate. [ABSTRACT FROM AUTHOR]

In this paper, we report on the mixing structures and transport properties of the Adriatic Sea surface, as a semi-enclosed basin of the Mediterranean Sea, from October 2006 until December 2011. Lagrangian transport models were used to simulate synthetic trajectories from the mean flow fields obtained by the Massachusetts Institute of Technology general circulation model implemented for the Adriatic. We examine the dispersion properties of numerical pair particles, through the calculation of time-averaged finite-size Lyapunov exponents (FSLEs) in the Adriatic Sea during selected months in each year. The results show the significant effects of river runoff and wind forcing, especially the Bora wind field, on the mixing activities of numerical pair particles by the generation of vortices, which appear as a tangle of filaments on the FSLE maps. The stretch/compression lines, which contain high values of the FSLEs, work as robust transport barriers, most having been detected along boundary currents on the eastern and western flanks of the Adriatic, particularly during winter. Numerical experiments have indicated that stable flows, with less mixing activity, occur in the northern part of the Adriatic in June and September of each year, while the Southern Adriatic Pit has flows with larger seasonal fluctuations and high values of eddy kinetic energy because of the influence of wind and energetic currents entering from the Ionian Sea. [ABSTRACT FROM AUTHOR]

Wind stress and turbulent heat fluxes are the major driving forces which modify the ocean dynamics and thermodynamics. In the NEMO ocean general circulation model, these turbulent air-sea fluxes (TASFs), which are components of the ocean model boundary conditions, can critically impact the simulated ocean characteristics. This paper investigates how the different bulk parametrizations to calculated turbulent air-sea fluxes in the NEMO4 (revision 12957) drives substantial differences in sea surface temperature (SST). Specifically, we study the contribution of different aspects and assumptions of the bulk parametrizations in driving the SST differences in NEMO global model configuration at ¼ degree of horizontal resolution. These include the use of the skin temperature instead of the bulk SST in the computation of turbulent heat flux components, the estimation of wind stress and the estimation of turbulent heat flux components which vary in each parametrization due to the different computation of the bulk transfer coefficients. The analysis of a set of short-term sensitivity experiments, where the only experimental change is related to one of the aspects of the bulk parametrizations, shows that parametrization-related SST differences are primarily sensitive to the wind stress differences across parametrizations and to the implementation of skin temperature in the computation of turbulent heat flux components. Moreover, in order to highlight the role of SST-turbulent heat flux negative feedback at play in ocean simulations, we compare the TASFs differences obtained using NEMO ocean model with the estimations from Brodeau et al. (2017), who compared the different bulk parametrizations using prescribed SST. Our estimations of turbulent heat flux differences between bulk parametrizations is weaker with respect to Brodeau et al. (2017) differences estimations. [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]

There has been a growing interest in model-wise uncertainty decomposition, which quantifies contribution of individual models such as emission scenarios, global circulation models, bias correction techniques and hydrological models, to the total uncertainty of a hydrological projection. However, little research has been conducted for model-wise uncertainty decomposition in spite of its usefulness. In this paper, we propose a novel method for decomposing the total uncertainties into model-wise uncertainties. The proposed model-wise uncertainty decomposition method can be applied with general uncertainty measures, which include mean absolute deviation and variance measures. Moreover, the proposed method provides an intuitive interpretation of the quantified model-wise uncertainties. The results of analyzing real data by the proposed method are presented. [ABSTRACT FROM AUTHOR]

Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each chain of the MME. By contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM-RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM-RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes i) piecewise linear functions to model the changes in the three GEV parameters ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM-RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500m elevation for the 23 massifs of the French Alps, which is of major interest for the structural design of roofs. Annual maxima are available for 20 adjusted GCM-RCM pairs from the EURO-CORDEX experiment, under the scenario RCP8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs, by -2.9kNm-2 (-50%) on average between 1986-2005 and 2080-2099 at 1500m elevation and RCP8.5. This paper extends to climate extremes the recent idea to constrain climate projection ensembles using past observations. [ABSTRACT FROM AUTHOR]

Previous studies have recognized the societal relevance of climatic extremes on the seasonal time scale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet orwarm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogs to even themost extreme ERA-Interimevents. This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of ElNiño to La Niña transitions and the largest ERA-Interim midlatitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensitymeasures and estimate how often an extreme winter like the coldNorth American 2013/14 winter is expected anywhere inmidlatitude regions. Furthermore, we present a large set of simulated analogs to this event, whichmakes it possible to studycommonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [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]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [ABSTRACT FROM AUTHOR]

This paper describes version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). Meteorology for driving GEOS-Chem has been archived from the E2.1 contributions to phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the pre-industrial era and the recent past. In addition, meteorology is available for the near future and end of the century for seven future scenarios ranging from extreme mitigation to extreme warming. Emissions and boundary conditions have been prepared for input to GEOS-Chem that are consistent with the CMIP6 experimental design. The model meteorology, emissions, transport, and chemistry are evaluated in the recent past and found to be largely consistent with GEOS-Chem driven by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) product and with observational constraints. [ABSTRACT FROM AUTHOR]

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets. [ABSTRACT FROM AUTHOR]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

The boreal summer intraseasonal oscillation (BSISO) is among the most pronounced subseasonal variability in the tropics during boreal summer. Compared with its wintertime counterpart, the so-called Madden-Julian oscillation (MJO), the BSISO convection displays more complicated spatiotemporal evolution, characterized by northward propagation over the northern Indian Ocean and western North Pacific as well as eastward propagation along the equator. It exerts a strong effect on a broad range of tropical weather and climate phenomena, such as tropical cyclogenesis, monsoon onset, and active/break cycles, among others. Our fundamental understanding of the BSISO has steadily advanced: so far various aspects of the BSISO have been described, and several theories that aim to elucidate its northward propagation have been proposed. Yet, our skill to simulate the BSISO by general circulation models remains unsatisfactory, though it has been improved. This paper reviews some fundamental aspects of the BSISO from the viewpoint of observation, theory, and modeling. [ABSTRACT FROM AUTHOR]

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models. [ABSTRACT FROM AUTHOR]

The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s. [ABSTRACT FROM AUTHOR]

This paper investigates climate change impacts on the hydrology and water quality of Brazilian semi-arid reservoirs. An integrated approach coupling climate, hydrological and water quality models was proposed. Five general circulation models (GCMs) and two future scenarios (SSP2-4.5 and SSP5-8.5) were used for the projections. The simulations showed that uncertainty in climate forecast significantly impacted hydrological and water quality outputs. Some scenarios revealed a strong decrease in streamflow and storage volume. On the other hand, 40% of multi-GCM projections indicated reservoir eutrophication. The impacts on hydrology and water quality under SSP5-8.5 were more significant than those under SSP2-4.5. The results also indicated that drought events may be prolonged under climate change, principally at the end of the 21st century for SSP5-8.5, and may significantly increase the total phosphorus concentration. This suggests that tropical semi-arid reservoirs could be more vulnerable to eutrophication in scenarios of climate change than those in other regions. [ABSTRACT FROM AUTHOR]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

• Vertical grids impact the modeling of baroclinic modes and water mass distribution. • 32-layer HYCOM is adequate to represent first 5 baroclinic modes in mid-latitudes. • A simulation with 16 layers exhibits no dense overflow water and a shallower AMOC. • Simulations with 24 to 96 layers exhibit overall similar North Atlantic circulation. In contrast to the large volume of studies on the impact of horizontal resolution in oceanic general circulation models (OGCMs), the impact of vertical resolution has been largely overlooked and there is no consensus on how one should construct the vertical grid to represent the vertical structure of the baroclinic modes as well as the distribution of distinct water masses throughout the global ocean. In this paper, we document the importance of vertical resolution in the representations of vertical modes and water masses in the North Atlantic and show i) that vertical resolution is unlikely to undermine the resolution capability of the horizontal grid in representing the vertical modes and a 32-layer isopycnal configuration is adequate to represent the first five baroclinic modes in mid-latitudes and ii) that vertical resolution should focus on representing water masses. A coarse vertical resolution (16-layer) simulation exhibits virtually no transport in the dense overflow water which leads to a weaker and significantly shallower Atlantic meridional overturning circulation (AMOC) despite resolving the first baroclinic mode throughout the domain, whereas there are overall very small differences in the subtropical and subpolar North Atlantic circulation in the simulations with finer vertical resolution (24 to 96 layers). We argue that accurately representing the water masses is more important than representing the baroclinic modes for an OGCM in modeling the low-frequency large-scale circulation. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]