Journal of Advances in Modeling Earth Systems最新文献

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The editors of JAMES thank the 919 reviewers who provided 1,413 reviews during 2023. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.

The correct representation of the Maud Rise open-ocean polynya in the Weddell Sea remains a challenge for ocean models. Here we reproduce the most recent polynya openings in 2016–2017 using a regional configuration, and assess their dependencies on vertical convective mixing schemes and freshwater forcing, both separately and in combination. We test three vertical convective mixing schemes: the enhanced vertical diffusion (EVD), the Eddy-Diffusivity Mass-Flux (EDMF) parameterization, and a modified version of EDMF accounting for thermobaric effects. Using simulations for the period 2007–2017, we find that the modified EDMF reproduces the observed climatological evolution of the mixed layer depth better than the original EDMF and the EVD, but a polynya fails to open due to excessive freshwater forcing. We thus use the modified EDMF to perform sensitivity experiments with reduced precipitation during 2012–2017. The imposed freshwater forcing strongly affects the number of years with polynyas. The simulation with the best representation of the 2016–2017 polynyas is analyzed to evaluate the triggering mechanisms. The 2016 polynya was induced by the action of thermobaric instabilities on a weak ambient stratification. This opening preconditioned the water column for 2017, which produced a stronger polynya. By examining the impacts of the different convective mixing schemes, we show that the modified EDMF generates more realistic patterns of deep convection. Our results highlight the importance of surface freshwater forcing and thermobaricity in governing deep convection around Maud Rise, and the need to represent thermobaric instabilities to realistically model Maud Rise polynyas.

This work describes the implementation and evaluation of the Slab Ocean Model component of the Energy Exascale Earth System Model version 2 (E3SMv2-SOM) and its application to understanding the climate sensitivity to ocean heat transports (OHTs) and CO₂ forcing. E3SMv2-SOM reproduces the baseline climate and Equilibrium Climate Sensitivity (ECS) of the fully coupled E3SMv2 experiments reasonably well, with a pattern correlation close to 1 and a global mean bias of less than 1% of the fully coupled surface temperature and precipitation. Sea ice extent and volume are also well reproduced in the SOM. Consistent with general model behavior, the ECS estimated from the SOM (4.5 K) exceeds the effective climate sensitivity obtained from extrapolation to equilibrium in the fully coupled model (4.0 K). The E3SMv2 baseline climate also shows a large sensitivity to OHT strengths, with a global surface temperature difference of about 4.0°C between high-/low-OHT experiments with prescribed forcings derived from fully coupled experiments with realistic/weak ocean circulation strengths. Similar to their forcing pattern, the surface temperature response occurs mainly over the subpolar regions in both hemispheres. However, the Southern Ocean shows more surface temperature sensitivity to high/low-OHT forcing due to a positive/negative shortwave cloud radiative effect caused by decreases/increases in mid-latitude marine low-level clouds. This large temperature sensitivity also causes an overcompensation between the prescribed OHTs and atmosphere heat transports. The SOM's ECS estimate is also sensitive to the prescribed OHT and the associated baseline climate it is initialized from; the high-OHT ECS is 0.5 K lower than the low-OHT ECS.

Scale analysis based on coarse-graining has been proposed recently as an alternative to Fourier analysis. It requires interpolation to a regular mesh for data from unstructured-mesh models. We propose an alternative coarse-graining method which relies on implicit filters using powers of discrete Laplacians. This method can work on arbitrary (structured or unstructured) meshes and is applicable to the direct output of unstructured-mesh models. Illustrations and detailed discussions are provided for discrete fields placed at vertices of triangular meshes. The case with placement on triangles is also briefly discussed.

Convective Self-Aggregation (CSA) is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid-scale processes. This study examines the role of mixing and entrainment, provided by the turbulence model and the implicit numerical diffusion. The study compares the results of two models, WRF and SAM, by varying turbulence models, initial conditions, and horizontal spatial resolution. At a coarse grid resolution of 3 km, the removal of turbulent mixing prevents CSA in models with low numerical diffusivity but is preserved in models with high numerical diffusivity. When the horizontal grid resolution is refined to 1 km, CSA can only be achieved by increasing explicit turbulent mixing, even with a small amount of shallow clouds. Therefore, the sensitivity of CSA to horizontal grid resolution is not primarily caused by the decrease in shallow clouds. The analysis of the total water path spectrum suggests that the amplitude of initial humidity perturbations introduced by convection in the free troposphere is the key factor. This amplitude is regulated by turbulent mixing and diffusion at small scales. Prior to the onset of CSA, increased mixing makes updrafts more sensitive to the dryness of the free troposphere, which strengthens the moisture-convection feedback. This leads to an increased distance between convective cores and a stronger humidity perturbation in the free troposphere, which can destabilize the RCE state.

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

While low-resolution climate models at present struggle to appropriately simulate convectively coupled large-scale atmospheric disturbances such as equatorial Kelvin waves (EKWs), superparameterization helps better reproduce such phenomena. To evaluate such model differences based on physical mechanisms, a linearized theoretical framework of convectively coupled EKWs was developed in a form readily applicable to model evaluation by allowing background stability and diabatic heating to have arbitrary vertical profiles rather than assuming simplified ones. A system of linearized equations of convection-coupled gravity waves was derived as a two-dimensional model of the convectively coupled EKWs. In this work, the basic states are taken from observations, CTL-MIROC and SP-MIROC experiments introduced in Part 1. The tendency of convectively coupled gravity waves to grow faster under top-heavy heating is confirmed for realistic stratification profiles, as found in previous studies under constant stratifications. A comparison of linear unstable solutions with basic states taken from SP-MIROC and CTL-MIROC shows that the top-heavy heating profile in SP-MIROC largely contributes to the enhancement of the EKW-like unstable modes, while subtle differences of stratification profiles considerably affect EKW behaviors. The bottom-heavy heating bias in the CTL-MIROC likely originates from insufficient modeling of subgrid stratiform precipitation in tropical organized systems. It is desirable to incorporate such stratiform components in cumulus parameterizations to achieve better EKW reproducibility.

Earth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land-atmosphere interactions. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large-eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two-column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models.

Strongly coupled data assimilation allows observations of one Earth system component (e.g., the ocean) to directly update another component (e.g., the atmosphere). The majority of the information transfer in strongly coupled atmosphere-ocean systems is passed through vertical correlations between atmospheric boundary layer and ocean mixed layer fields. In this work we use correlations from a global, coupled model to study vertical observation-space localization techniques for strongly coupled data assimilation. We generate target correlations using a bootstrapping approach from a single 24 hr forecast from a realistic global, weakly coupled atmosphere-ocean cycling system with an 80-member ensemble, which is the ensemble size currently used by the NOAA operational global data assimilation system. We compare data assimilation methods with different localization schemes using single-update, offline experiments. We develop a new strategy for optimal observation space localization, called Empirical Optimal R-localization (EORL), to give an upper bound on the improvement we can expect with any localization scheme. We then evaluate Gaspari-Cohn localization, which is a commonly used parametric localization function and review its performance with respect to the optimal localization scheme. We investigate how the performance of these localization strategies changes with increasing ensemble sizes. Our results show that strongly coupled data assimilation has the potential to be an improvement over weakly coupled data assimilation when large ensembles are used. We also show that the Gaspari-Cohn localization function does not appear to be a particularly good choice for cross-fluid vertical localization.

We present a two-dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux-gradient experiments using a three-dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large-scale transport features such as the influence on trace gas concentrations of the quasi-biennial oscillation and variations in inter-hemispheric transport rates. Stratosphere-troposphere exchange is comparable to a three-dimensional model and inter-hemispheric transport is faster by up to 0.3 years than typical transport times of three-dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC-11 from an incorrect a priori emissions field well using three-dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.