Journal of Advances in Modeling Earth Systems最新文献

All-sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all-sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all-sky assimilation framework with HCVs is modified to assimilate Fengyun-3C/D Microwave Humidity Sounder-II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi-variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm-core and the secondary circulation. This paper analyzes the characteristics of variational all-sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all-sky radiance assimilation.

For modern land surface models (LSMs) representing a singular bulk vegetation layer, the longwave optical properties (i.e., emissivity, reflectivity, and transmittivity) of vegetation layer are derived with a simplified constraint of assuming zero leaf reflectivity. This constraint is necessary, for instance, to the Beer–Lambert (B–L) law to establish a relationship between the optical properties and leaf area index. However, the simplified constraint leads to an overestimation of land surface emissivity in the vegetated regions. In this study, we introduce a new scheme considering realistic leaf reflectivity values rather than assuming zero. This new scheme is based on the relationship derived from the B–L law, but it is statistically augmented to consider the effects of leaf reflections. It is designed to emulate a multi-vegetation-layer numerical model known as the Norman model, which is capable of numerical calculations of multi-reflections among leaves. This new method consists of only a couple of simple equations; despite its simplicity, it very closely mimics the Norman model; The discrepancy of the results between the new method and the Norman model is less than measurement uncertainties for any combination of input parameters. When the new scheme is implemented in the Community Land Model version 5 (CLM5), the land surface emissivity values are simulated much more consistently with global measurements, resulting in significant alterations of land surface energy budget. The enhanced realism through our new scheme is poised to contribute to more accurate numerical weather and climate simulations.

This study utilizes Deep Neural Networks (DNN) to improve the K-Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11-year turbulence-resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN-augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long-term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first-order and second-moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing.

The Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three-dimensional mesoscale-resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data-driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of $��\sim �$0.74 and $��\sim �$0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability.

The shallow-water analogue models for the tropical atmosphere are examined from a formulational point of view. The normal-mode approach provides a formal procedure to reduce the primitive equation system to a shallow-water analogue, although approaches based on vertical integrals of the primitive equation system may be more intuitively appealing. Under a general framework of the latter, classical models by Gill (1980, https://doi.org/10.1256/smsqj.44904) and Lindzen and Nigam (1987, 2.0.co;2>https://doi.org/10.1175/1520-0469(1987)044<2418:otross>2.0.co;2) are derived in a deductive manner, by elucidating their limitations, implications, as well physical processes assumed. Major advantage of shallow-water analogue models is that after a vertical integral, the determination of convective heating rate simply reduces to that of a precipitation rate. Consequently, the question of representing convection also almost reduces to that of precipitation. This fact leads to confusions in literature about distinction between large-scale precipitation and subgrid-scale convection. This framework further supports a popular notion of the moisture as a key variable for describing convection. By reviewing the existing formulations, it is shown that convection can be parameterized without moisture under the limit of the parcel-environment quasi-equilibrium.

While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter-annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter-annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS-VIC and between 0.11 and 0.42 in DE-NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state-wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite-derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences.

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.