Journal of Advances in Modeling Earth Systems最新文献

Understanding soil erosion and sediment transport from the hillslope scale to the regional scale is crucial for studies on water quality, soil-water conservation, the lateral carbon cycle, environmental zoning and vulnerability. However, most existing erosion and sediment transport models are only applicable at the hillslope scale or for small watersheds with fine spatial resolutions (typically much less than 1 km). This study presents a process-based soil erosion and sediment transport model for model applications designed for applications with coarse spatial (e.g., ≥10 km) and temporal (e.g., from hourly to daily) resolutions. This new model, referred to as VIC-SED, effectively accounts for interactions between erosion and hydrological processes. This is achieved by tightly coupling the erosion processes with a hydrologically based Three-layer Variable Infiltration Capacity (VIC-3L) land surface model (LSM) and to a multi-scale routing (MSR) model. VIC-SED considers the impacts of (a) the spatio-temporal variability of rainfall intensity on erosion processes and (b) soil moisture on the soil detachment process. VIC-SED is evaluated in two watersheds. Results demonstrate that VIC-SED is capable of reproducing water and suspended sediment discharges at coarse spatial resolutions and varying temporal scales varying from 15-min to daily intervals. Our study indicates that the VIC-SED model is a promising tool for studying and assessing the impacts of climate and land cover changes on suspended sediment yields over large regions using coarse spatial and temporal resolutions.

The performance of global ocean biogeochemical models can be quantified as the misfit between modeled tracer distributions and observations, which is sought to be minimized during parameter optimization. These models are computationally expensive due to the long spin-up time required to reach equilibrium, and therefore optimization is often laborious. To reduce the required computational time, we investigate whether optimization of a biogeochemical model with shorter spin-ups provides the same optimized parameters as one with a full-length, equilibrated spin-up over several millennia. We use the global ocean biogeochemical model MOPS with a range of lengths of model spin-up and calibrate the model against synthetic observations derived from previous model runs using a derivative-free optimization algorithm (DFO-LS). When initiating the biogeochemical model with tracer distributions that differ from the synthetic observations used for calibration, a minimum spin-up length of 2,000 years was required for successful optimization due to certain parameters which influence the transport of matter from the surface to the deeper ocean, where timescales are longer. However, preliminary results indicate that successful optimization may occur with an even shorter spin-up by a judicious choice of initial condition, here the synthetic observations used for calibration, suggesting a fruitful avenue for future research.

Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling-based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre-industrial control or historical runs. This suggests that pre-industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

We investigate the role of mesoscale organization for the response of trade cumulus (Tc) clouds to climate change. Among four recently identified states of Tc organization, the “Sugar” state has the lowest and the “Flower” state the highest cloud fraction and cloud radiative effect. Using large-eddy simulations, we find that the Flower Tc state is more sensitive to climate change than the Sugar Tc state. In the considered case, the short-wave cloud radiative effect weakens by 0.28 W m⁻² in the Sugar state and by 1.5 W m⁻² in the Flower state over the course of 21st century under the RCP8.5 emissions scenario. This is accompanied by a reduction of the short-wave cloud radiative effect variance on the mesoscale. The primary mechanism is stabilization of the boundary layer by stronger long-wave radiative heating at the inversion associated with higher greenhouse gas levels. This weakens the boundary layer mesoscale circulation that is responsible for aggregation of moisture and formation of the Flower Tc state. Thus, in the considered case, organization on the mesoscale amplifies the positive feedback of Tc clouds to climate change. Owing to the widespread occurrence of boundary layer mesoscale circulations in the Tc regime, this mechanism could modulate the Tc response to climate change in general.

Wind-generated near-inertial internal waves (NIWs) are characterized by dominant long-range equatorward radiation due to the gradient of the planetary vorticity, known as the β-refraction effect. In this study, we analyze the effects of horizontal model resolution on the long-range equatorward radiation of NIWs. In a high-resolution Community Earth System Model (CESM-HR) with a 0.1° oceanic resolution, about 25% (15%) of NIW energy flux injected downward the surface boundary layer base poleward of 30°N (30°S) radiates into the lower-latitude region. This ratio decreases to about 15% (8%) in a low-resolution CESM (CESM-LR) with a 1° oceanic resolution. The higher long-range equatorward radiation efficiency in the CESM-HR than the CESM-LR is directly attributed to the faster equatorward group velocity of the NIWs of the first three vertical modes, which reflects the better representation of equatorward propagation and beta-refraction of smaller scale NIWs in the CESM-HR. The enhancement of equatorward wavenumber induced by the β-refraction is inhibited in the CESM-LR, which underrepresent the long-range equatorward radiation of NIWs. These results underscore the necessity of high-resolution ocean models in accurately simulating the spatial variabilities of NIWs and their induced turbulent diapycnal mixing in the global ocean.

Large uncertainties persist in modeling shallow, low clouds because of many interacting nonlinear processes and multiple cloud-controlling environmental factors. In addition, sharp changes in behavior occur when environmental thresholds are met. Model studies that follow a traditional approach of exploring the effects of factors “one-at-a-time” are unable to capture interactions between factors. We simulate a stratocumulus cloud based on the Second Dynamics and Chemistry of Marine Stratocumulus field study using a large-eddy simulation model coupled with a two-moment cloud microphysics scheme. The simulations are used to train a Gaussian process emulator, which we then use to visualize the relationships between two cloud-controlling factors and domain-averaged cloud properties. Only 29 model simulations were required to train the emulators, which then predicted cloud properties at thousands of new combinations of the two factors. Emulator response surfaces of cloud liquid water path and cloud fraction show two behavioral regimes, one of thin and patchy yet steady stratocumulus and one of thick, growing stratocumulus with cloud fraction near 1. Internal variability (initial-condition uncertainty) creates unrealistic “bumpy” response surfaces. However, we show that the variability causing the bumpiness can be characterized in an emulator “nugget term” that is adjusted to match the distribution of a small number of initial-condition ensemble simulations at various points on the surface, thereby allowing a smoother, deterministic response surface to be constructed. Accounting for variability allows the transition between regimes, and the joint interactions of parameters, to be visualized in a more deterministic way that has not been done before.

We synthesize knowledge from numerical weather prediction, inverse theory, and statistics to address the problem of estimating a high-dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics, machine learning/artificial intelligence, and in modern Earth science. We create several new adaptive methods for high-dimensional covariance estimation, but one method, which we call Noise-Informed Covariance Estimation (NICE), stands out because it has three important properties: (a) NICE is conceptually simple and computationally efficient; (b) NICE guarantees symmetric positive semi-definite covariance estimates; and (c) NICE is largely tuning-free. We illustrate the use of NICE on a large set of Earth science–inspired numerical examples, including cycling data assimilation, inversion of geophysical field data, and training of feed-forward neural networks with time-averaged data from a chaotic dynamical system. Our theory, heuristics and numerical tests suggest that NICE may indeed be a viable option for high-dimensional covariance estimation in many Earth science problems.

Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy, which is largely controlled by boundary-layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere-only and ocean-atmosphere coupled models. The diurnal cycles of convection in both simulations are significantly improved with small changes to their mean states. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at subdiurnal time scales. Relaxed quasi-equilibrium for convective available potential energy holds in significant measure as a result. Future model improvement will focus on the remaining biases in the diurnal cycle, which may be further reduced by including stochastic entrainment and cold pools.

On ensemble-based coupled data assimilation, cross-component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross-component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so-called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS-inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal-to-noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS-based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow-varying oceanic component, but not vice versa. In comparison with cross-component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.

The computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high-resolution aerosol assimilation system. A 2-dimensional variational method (2DVar) was implemented in a non-hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar-orbital satellites. The model results were evaluated against ground-based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM_2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite-estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM_2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite-retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground-based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.