Journal of Advances in Modeling Earth Systems最新文献

The large variation in net ecosystem productivity (NEP) with forest age was dominated by the dynamics of net primary productivity (NPP)–which in turn was determined by the different response slopes of gross primary productivity (GPP) and autotrophic respiration (Ra) with forest age. However, only few models can comprehensively represent the impacts of forest age and global changes including land-use change, climate change, nitrogen deposition, and atmospheric CO₂ from the perspective of ecological processes. Based on a process-based model (CEVSA-ES) that included these global changes, we developed an ecosystem carbon sink assessment model considering forest age dynamics (CEVSA-AgeD) using satellite-based relationships between GPP (or Ra) and forest age to constrain photosynthesis and autotrophic respiration processes. Subsequently, we used a model data-fusion framework combined with carbon flux observations to calibrate the model. The calibrated CEVSA-AgeD model performed well in simulating seasonal (R² values for GPP, ecosystem respiration, and NEP were 0.86, 0.79, and 0.66, respectively) and annual carbon flux changes (R² of GPP, ecosystem respiration, and NEP were 0.83, 0.77, and 0.67, respectively). The magnitude of average NEP in China estimated using this model was 0.35 ± 0.005 TgC/yr from 2001 to 2021, which was close to previous estimates, and the dynamics of forests age increased NEP by 87–92 TgC/yr. These results indicate that the CEVSA-AgeD model performed well in simulating carbon fluxes at the site and regional scales and that it was necessary to incorporate the effect of forest age dynamics on carbon cycling processes into process-based models.

In numerical weather prediction and climate models, boundary-layer clouds are controlled by a wide range of subgrid-scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large-eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka-Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale-aware parameterizations of shallow-convective clouds in atmospheric models.

One of the major challenges in large-domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large-scale streamflow projections to inform water resource management and long-term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

Cloud responses to surface-based sources of aerosol perturbation partially depend on how turbulent transport of the aerosol to cloud base affects the spatial and temporal distribution of aerosol. Here, scenarios of plume injection below a marine stratocumulus cloud are modeled using large eddy simulations coupled to a prognostic bulk aerosol and cloud microphysics scheme. Both passive plumes, consisting of an inert tracer, and active plumes are investigated, where the latter are representative of saltwater droplet plumes such as have been proposed for marine cloud brightening. Passive plume scenarios show higher in-plume cloud brightness (relative to out-of-plume) due to the predominant transport of the passive plume tracer from the near-surface to the cloud layer within updrafts. These updrafts rise into brighter areas within the cloud deck, even in the absence of an aerosol perturbation associated with an active plume. Comparing albedo at in-plume to out-of-plume locations associates the inert plume with the brightest cloud locations, without any causal effect of the plume on the cloud. Numerical sensitivities are first assessed to establish a suitable model configuration. Then sensitivity to particle injection rate is investigated. Trade-offs are identified between the number of injected particles and the suppressive effect of droplet evaporation on plume loft and spread. Furthermore, as the near-field in-plume brightening effect does not depend significantly on injection rate given a suitable definition of perturbed versus unperturbed regions of the flow, plume area is a key controlling factor on the overall cloud brightening effect of an aerosol perturbation.

Experimental soil heating experiments have found a consistent increase in soil-surface CO₂ emissions (F_s), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO₂ fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F_s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F_s (∼17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

Wetlands are the largest emitters of biogenic methane (CH₄) and represent the highest source of uncertainty in global CH₄ budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH₄ flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO₂ and CH₄ fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO₂ and CH₄ fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH₄ concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH₄ emission root mean squared error by up to 33% and is able to represent inter-daily CO₂ and CH₄ flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

Lagrangian trajectories are widely used as observations for recovering the underlying flow field via Lagrangian data assimilation (DA). However, the strong nonlinearity in the observational process and the high dimensionality of the problems often cause challenges in applying standard Lagrangian DA. In this paper, a Lagrangian-Eulerian multiscale DA (LEMDA) framework is developed. It starts with exploiting the Boltzmann kinetic description of the particle dynamics to derive a set of continuum equations, which characterize the statistical quantities of particle motions at fixed grids and serve as Eulerian observations. Despite the nonlinearity in the continuum equations and the processes of Lagrangian observations, the time evolution of the posterior distribution from LEMDA can be written down using closed analytic formulas after applying the stochastic surrogate model to describe the flow field. This offers an exact and efficient way of carrying out DA, which avoids using ensemble approximations and the associated tunings. The analytically solvable properties also facilitate the derivation of an effective reduced-order Lagrangian DA scheme that further enhances computational efficiency. The Lagrangian DA part within the framework has advantages when a moderate number of particles is used, while the Eulerian DA part can effectively save computational costs when the number of particle observations becomes large. The Eulerian DA part is also valuable when particles collide, such as using sea ice floe trajectories as observations. LEMDA naturally applies to multiscale turbulent flow fields, where the Eulerian DA part recovers the large-scale structures, and the Lagrangian DA part efficiently resolves the small-scale features in each grid cell via parallel computing. Numerical experiments demonstrate the skillful results of LEMDA and its two components.

This paper presents a self-contained derivation, from first principles, of a convective vertical mixing scheme based on the Eddy-Diffusivity Mass-Flux (EDMF) approach. This type of closure separates vertical turbulent fluxes into two components: an eddy-diffusivity (ED) which accounts for local small-scale mixing in a nearly isotropic environment, and a mass-flux (MF) transport term, which represents the non-local transport driven by vertically coherent plumes. Using the multi-fluid averaging underlying the MF concept, we review consistent energy budgets between resolved and subgrid scales for seawater and dry atmosphere, in anelastic and Boussinesq frameworks. We demonstrate that when using an EDMF scheme, closed energy budgets can be recovered if: (a) bulk production terms of turbulent kinetic energy (TKE) by shear buoyancy include MF contributions; (b) boundary conditions are consistent with EDMF, to avoid spurious energy fluxes at the boundary. Furthermore, we show that lateral mixing, due to either entrainment or detrainment induces a net production of TKE via the shear term, with enhanced production under increased horizontal drag. We also provide constraints on boundary conditions to ensure mathematical consistency. Throughout the theoretical development, we maintain transparency regarding underlying assumptions. In a companion paper (Perrot and Lemarié (2024, https://hal.science/hal-04666049); hereafter Part II) we assess the validity of these hypotheses, and analyze the sensitivity of the scheme to modeling choices against Large Eddy Simulations (LES) and observational data on oceanic convection. Part II also details an energy-conserving discretization and quantifies energy biases in inconsistent formulations.

Effective assessment of potential climate impacts requires the ability to rapidly predict the time-varying response of climate variables. This prediction must be able to consider different combinations of forcing agents at high resolution. Full-scale ESMs are too computationally intensive to run large scenario ensembles due to their long lead times and high costs. Faster approaches such as intermediate complexity modeling and pattern scaling are limited by low resolution and invariant response patterns, respectively. We propose a generalizable framework for emulating climate variables to overcome these issues, representing the climate system through spatially resolved impulse response functions. We derive impulse response functions by directly deconvolving effective radiative forcing and near-surface air temperature time series. This enables rapid emulation of new scenarios through convolution and derivation of other impulse response functions from any forcing to its response. We present results from an application to near-surface air temperature based on CMIP6 data. We evaluate emulator performance across 5 CMIP6 experiments including the SSPs, demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to CMIP6 ensemble outputs. Global mean relative error in emulated temperature averages 1.49% in mid-century and 1.25% by end-of-century. These errors are likely driven by state-dependent climate feedbacks, such as the non-linear effects of Arctic sea ice melt. We additionally show an illustrative example of our emulator for policy evaluation and impact analysis, emulating spatially resolved temperature change for a 1,000 member scenario ensemble in less than a second.