Journal of Advances in Modeling Earth Systems最新文献

We introduce a 6.5-km version of the Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High-resolution prediction on Earth-to-Local Domains (SHiELD). This global model is designed to bridge the gap between global medium-range weather prediction and global storm-resolving simulation while remaining practical for real-time forecast. The 6.5-km SHiELD represents a significant advancement over GFDL's flagship global forecast system, the 13-km SHiELD. This global model features a holistically-developed scale-aware suite of physical parameterizations, stepping into the formidable convective “gray zone” of resolutions below 10 km. Comparative analyses with the 13-km SHiELD, conducted over a 3-year hindcast period, highlight noteworthy improvements across global-scale, regional-scale, tropical cyclone (TC), and continental convection predictions. In particular, the 6.5-km SHiELD excels in predicting considerably finer-scale convective systems associated with large-scale frontal systems and extratropical cyclones. The predictions of global temperature, wind, cloud, and precipitation are significantly improved in this global model. Regionally, over the contiguous United States and the Maritime Continent, substantial reductions in prediction biases of precipitation, cloud cover, and wind fields are also found. In the mesoscale realm, the model demonstrates prominent improvements in global TC intensity and continental convective precipitation prediction: biases are relieved, and skill is higher. These findings affirm the superiority of the 6.5-km SHiELD compared to the current 13-km SHiELD, which will advance weather prediction by successfully addressing both synoptic weather systems and specific storm-scale phenomena in the same global model.

Due to their limited resolution, numerical ocean models need to be interpreted as representing filtered or averaged equations. How to interpret models in terms of formally averaged equations, however, is not always clear, particularly in the case of hybrid or generalized vertical coordinate models, which limits our ability to interpret the model results and to develop parameterizations for the unresolved eddy contributions. We here derive the averaged hydrostatic Boussinesq equations in generalized vertical coordinates for an arbitrary thickness-weighted average. We then consider various special cases and discuss the extent to which the averaged equations are consistent with existing ocean model formulations. As previously discussed, the momentum equations in existing depth-coordinate models are best interpreted as representing Eulerian averages (i.e., averages taken at fixed depth), while the tracer equations can be interpreted as either Eulerian or thickness-weighted isopycnal averages. Instead we find that no averaging is fully consistent with existing formulations of the parameterizations in semi-Lagrangian discretizations of generalized vertical coordinate ocean models such as MOM6. A coordinate-following average would require “coordinate-aware” parameterizations that can account for the changing nature of the eddy terms as the coordinate changes. Alternatively, the model variables can be interpreted as representing either Eulerian or (thickness-weighted) isopycnal averages, independent of the model coordinate that is being used for the numerical discretization. Existing parameterizations in generalized vertical coordinate models, however, are not always consistent with either of these interpretations, which, respectively, would require a three-dimensional divergence-free eddy tracer advection or a form-stress parameterization in the momentum equations.

Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3-7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual-mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high-density and medium-density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity.

A solid understanding of the mechanisms behind the presently observed, rapid warming of the northwest North Atlantic Continental Shelf and their biogeochemical impacts is lacking. We hypothesize that a weakening of the Labrador Current System (LCS), especially the shelfbreak jet along the Scotian Shelf, is contributing to these changes and that the future evolution of the LCS will be key to accurate projections. Here we analyze the response of a transient simulation of the high-resolution GFDL Climate Model 2.6 (CM2.6) which realistically simulates the regional circulation but includes only a highly simplified representation of ocean biogeochemistry. Then, we use the CM2.6 to force a medium-complexity regional biogeochemical ocean model, the Atlantic Canada Model, to obtain projections of nutrient availability on the shelf. In the simulation, the shelfbreak jet weakens because of a reduction of the along-shelf pressure gradient caused by a buoyancy gain of the upper water column along the shelf edge. This buoyancy gain is the result of warmer waters along the continental slope. Importantly, we find that the temperature-based criterion used commonly to pinpoint the location of the Gulf Stream is misleading, causing an overestimation of the northward migration of the Gulf Stream. A fixed isotherm may indicate northward movement as a result of basin-wide warming and not necessarily reflect changes in dynamics. The combination of the weakened shelfbreak jet and a lowering of nutrient concentrations in its source water reduce nutrient availability on the northwest North Atlantic shelf by one third by 2100 in the projection analyzed.

All Earth System Models (ESMs) have climatological biases relative to the observed historical climate. The quality of a model and, more importantly, the accuracy of its predictions are often associated with the magnitude and properties of its biases. For more than a decade, new strategies have been developed to empirically reduce such biases in the model components of ESMs during their execution. The present study considers a cyclostationary class of empirical runtime bias corrections to a climate model, referred to here as empirical runtime bias corrections (ERBCs). Such ERBCs are state independent and designed to reduce biases in the climatological annual cycle of the model. We present a new procedure for deriving such ERBCs called Climatological Adaptive Bias Correction (CABCOR). CABCOR is argued to be superior to the standard relaxation approach to defining ERBCs because it requires only a climatological, rather than a multi-year time evolving, observational reference data set. As part of this study, we perform a novel analysis of the relaxation approach in which a mapping is made between the parameter values that define the relaxation and the biases produced by ERBCs in the corrected model. This allows us to identify the optimal bias correction produced by the relaxation approach and to additionally demonstrate that the CABCOR approach can produce bias-corrected models with smaller climatological biases.

Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational Encoder-Decoder (VED) framework to learn the hidden relationship between radiative anomalies and the surface intensification of realistic simulated TCs. The uncertainty of the VED model identifies periods when radiation has more importance for intensification. A close examination of the radiative pattern extracted by the VED model from a 20-member ensemble simulation on Typhoon Haiyan shows that longwave forcing from inner core deep convection and shallow clouds downshear contribute to intensification, with deep convection in the downshear-left quadrant having the most impact overall on the intensification of that TC. Our work demonstrates that machine learning can aid the discovery of thermodynamic-kinematic relationships without relying on axisymmetric or deterministic assumptions, paving the way for the objective discovery of processes leading to TC intensification in realistic conditions.

Terrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure-performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site-level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure-performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.

Emissions of anthropogenic aerosols are rapidly changing, in amounts, composition and geographical distribution. In East and South Asia in particular, strong aerosol trends combined with high population densities imply high potential vulnerability to climate change. Improved knowledge of how near-term climate and weather influences these changes is urgently needed, to allow for better-informed adaptation strategies. To understand and decompose the local and remote climate impacts of regional aerosol emission changes, we perform a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced-complexity climate model FORTE 2.0 (FORTE2). Absorbing and scattering aerosols are perturbed separately, over East Asia and South Asia, to assess their distinct influences on climate. In this paper, we first present an updated version of FORTE2, which includes treatment of aerosol-cloud interactions. We then document and validate the local responses over a range of parameters, showing for instance that removing emissions of absorbing aerosols over both East Asia and South Asia is projected to cause a local drying, alongside a range of more widespread effects. We find that SyRAP-FORTE2 is able to reproduce the responses to Asian aerosol changes documented in the literature, and that it can help us decompose regional climate impacts of aerosols from the two regions. Finally, we show how SyRAP-FORTE2 has regionally linear responses in temperature and precipitation and can be used as input to emulators and tunable simple climate models, and as a ready-made tool for projecting the local and remote effects of near-term changes in Asian aerosol emissions.

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser-resolution ($��\sim �$12 km) ocean general circulation model are used to calculate density-driven terms within a global, higher-resolution ($��\sim �$2.5 km) depth-averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the $��M_2�$ tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30-day sea levels, and non-tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density-driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

Implicit time-stepping for advection is applied locally in space and time where Courant numbers are large, but standard explicit time-stepping is used for the remaining solution which is typically the majority. This adaptively implicit advection scheme facilitates efficient and robust integrations with long time-steps while having negligible impact on the overall accuracy, and achieving monotonicity and local conservation on general meshes. A novel and important aspect for the efficiency of the approach is that only one iteration is needed each time the linear equation solver is called for solving the advection equation. The demonstration in this paper uses the second-order Runge-Kutta implicit/explicit time integration in combination with a second/third-order finite-volume spatial discretization and is tested using deformation flow tracer advection on the sphere and a fully compressible model for atmospheric flows. Tracers are advected over the poles of highly anisotropic latitude-longitude grids with very large Courant numbers and on quasi-uniform hexagonal and cubed-sphere meshes with the same algorithm. Buoyant flow simulations with strong local updrafts also benefit from adaptively implicit advection. Stably stratified compressible flow simulations require a stable combination of implicit treatment of gravity and acoustic waves as well as advection in order to achieve long time-steps.