Journal of Advances in Modeling Earth Systems最新文献

Lakes are important sentinels of climate change and may contribute over 30% of natural methane (CH₄) emissions; however, no earth system model (ESM) has represented lake CH₄ dynamics. To fill this gap, we refined a process-based lake biogeochemical model to simulate global lake CH₄ emissions, including representation of lake bathymetry, oxic methane production (OMP), the effect of water level on ebullition, new non-linear CH₄ oxidation kinetics, and the coupling of sediment carbon pools with in-lake primary production and terrigenous carbon loadings. We compiled a lake CH₄ data set for model validation. The model shows promising performance in capturing the seasonal and inter-annual variabilities of CH₄ emissions at 10 representative lakes for different lake types and the variations in mean annual CH₄ emissions among 106 lakes across the globe. The model reproduces the variations of the observed surface CH₄ diffusion and ebullition along the gradients of lake latitude, depth, and surface area. The results suggest that OMP could play an important role in surface CH₄ diffusion, and its relative importance is higher in less productive and/or deeper lakes. The model performance is improved for capturing CH₄ outgassing events in non-floodplain lakes and the seasonal variability of CH₄ ebullition in floodplain lakes by representing the effect of water level on ebullition. The model can be integrated into ESMs to constrain global lake CH₄ emissions and climate-CH₄ feedback.

We introduce deep generative diffusion for multivariate and regional surrogate modeling learned from sea-ice simulations. Given initial conditions and atmospheric forcings, the model is trained to generate forecasts for a 12-hr lead time from simulations by the state-of-the-art sea-ice model neXtSIM. For our regional model setup, the diffusion model outperforms as ensemble forecast all other tested models, including a free-drift model and a stochastic extension of a deterministic data-driven surrogate model. The diffusion model additionally retains information at all scales, resolving smoothing issues of deterministic models. Furthermore, by generating physically consistent forecasts, previously unseen for such kind of completely data-driven surrogates, the model can almost match the scaling properties of neXtSIM, as similarly deduced from sea-ice observations. With these results, we provide a strong indication that diffusion models can achieve similar results as traditional geophysical models with the significant advantage of being orders of magnitude faster and solely learned from data.

High-resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah-MP land surface model with dynamic vegetation growth and river routing, in combination with an irrigation module, Sentinel-1 backscatter and snow depth retrievals, we produce a set of 0.7-km² water budget estimates of the Po river basin (Italy) for 2015–2023. The results demonstrate that irrigation modeling improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow in postprocessing (12% error reduction relative to observed low summer streamflow), even if the basin-wide irrigation amount is underestimated. Sentinel-1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel-1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel-1 backscatter DA cannot significantly improve short-term or interannual variations in soil moisture, irrigation modeling causes a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. This study helps advancing the design of digital water budget replicas for river basins.

One key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof-air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2-m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2-m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation-discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity-gradient tensor. In this work, we implement the ZB20 parameterization into the primitive-equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large-scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state-of-the-art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale-aware and, consequently, can be used with a single value of the non-dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long-standing biases in global ocean simulations in future studies.

Polar-orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub-daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5-min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES-R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES-R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree-based ensemble regressions showed promising performance for predicting GPP (R² of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m⁻² s⁻¹) and RECO (R² of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m⁻² s⁻¹) on a half-hourly time step using GOES-R surface products and top-of-atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near-real time.

The parameterization of subgrid-scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one-at-a-time (MOAT) parameter sensitivity analysis using short-term (3-day), initialized hindcasts of CAM6-CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally-averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid-latitude storm tracks). We next evaluate several experimental 20-year simulations of CAM6-CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short-term and long-term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long-term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study.