Journal of Advances in Modeling Earth Systems最新文献

Agrivoltaics, combining agriculture with photovoltaic systems, offers a promising solution to address land-use conflict between food and energy production. However, the complexities of agrivoltaics and its effects on the water-energy-carbon interactions remain poorly understood. In this study, we developed a process-based agrivoltaic model within the Community Land model 5 to assess the impacts of agrivoltaics on water, energy, and carbon cycles. The model was validated using data from agrivoltaic sites in Illinois and Colorado, generally capturing spatiotemporal variations in light conditions, soil moisture, and biomass carbon. Simulation results suggest that agrivoltaics significantly impact water, energy, and carbon budgets at the patch and system levels for maize and soybean in Illinois and grass in Colorado (2000–2014). Our findings show that the impacts of agrivoltaics vary by climate conditions and plant types. In dry climates, rainfall redistribution and shading from agrivoltaics conserve soil moisture and enhance evapotranspiration, promoting greater carbon assimilation and soil carbon storage for C₃ grass. Conversely, in wetter regions, reduced solar radiation from shading becomes the dominant factor, lowering carbon assimilation and sequestration for maize and soybean. These results suggest that agrivoltaics can help mitigate drought impacts in arid environments. Our analysis of land equivalent ratios across different photovoltaic ground coverage ratios (PV GCR) shows that a medium PV GCR (60%) under “AgPV” deployment, where PV and plants share the same land, maximizes land-use efficiency at the study sites. Our modeling study supports informed decision-making to promote sustainable management of water, energy, and food resources amid environmental change.

A new signal simulator for Doppler velocity, derived for W-band cloud profiling radar onboard EarthCARE, is developed and implemented into the Cloud Feedback Model Intercomparison Project Observation Simulator Package version 2 (COSP2). The simulator converts the vertical motion of hydrometeors and cumulus mass flux in global climate models (GCMs) into Doppler velocity signals, providing statistics on Doppler velocity and its spectrum width in the form of Contoured Frequency by tEmperature Diagram (CFED) or by Altitude Diagram (CFAD). To account for the different treatments of vertical air motion in stratiform and convective clouds within GCMs, their statistics are processed separately. The simulator was tested on the MIROC6 GCM and compared with ground-based radar measurements. The results showed consistency in ice particle growth and melting between the model and the observations. However, the droplet fall speed in the model was quantitatively underestimated, revealing a bias in the cloud microphysics of MIROC6. The combined use of this simulator with calculation of cloud optical depth in COSP2 also allows for the investigation of Doppler velocity characteristics as a function of cloud type. The developed simulator enabled COSP2 to generate model diagnostics of cloud particles and cumulus vertical air motions, facilitating future global comparisons with EarthCARE data. The enhanced capabilities of COSP2 thus will add value to model evaluation through the combined use of multiple simulators and multi-sensor synergistic observations provided by EarthCARE.

Motivated by the need to improve the representation of small-scale surface heterogeneity in Earth System Models (ESMs), new algorithms have been introduced to discretize ESM computational units (CUs) into a variable number of subgrid topographic units for improving model simulations with minimal increase in computational demand. The algorithms can be applied to structured (regular grid) and unstructured (e.g., watersheds) CUs to derive topography-based subgrid units (TGUs). This study evaluates the capability of the TGUs to capture surface heterogeneity within grid- versus watershed-based CUs. For this purpose, TGUs are derived for the grid- and watershed-based CUs at four equivalent spatial scales (1°, 0.5°, 0.25°, and 0.125° for grid-based and Hydrologic Unit Code levels HUC07, HUC08, HUC09, and HUC10 for watershed-based) over the CONUS domain. Statistical metrics are computed at the CU and TGU levels at each spatial scale for comparison. Results show that compared to the grid-based TGUs, the watershed-based TGUs are superior in capturing spatial heterogeneity associated with topographic slope, land cover, and surface hydrometeorology, despite their similar capability in capturing topographic elevation. This improved capability of the watershed-based TGUs resulting from the combined effects of the CU and TGU level discretization is consistently found across all spatial scales examined. At the finest spatial scales (0.125° and HUC10), the watershed-based TGUs better capture the observed precipitation, temperature, and snow water equivalent than the grid-based TGUs at 94%, 84%, and 72% of the SNOwpack TELemetry sites, respectively, highlighting the potential advantage of the watershed-based TGUs for improving accuracy and realism in ESM simulations.

Improving cloud cover prediction is one of the fundamental challenges for large-scale climate simulations due to the stochastic nature of clouds and their complex interactions with the atmospheric circulation. This study explores various machine-learning (machine learning) approaches and utilizes Lagrangian air mass history to improve prediction accuracy. Here, we use 169,824 isobaric boundary layer trajectories over the eastern subtropical oceans with colocated meteorological data at 12-hr intervals over 4 days. Satellite cloud cover data from MODIS are colocated with the trajectory points where available, resulting in 43,830 trajectories (26%) that are fully filled. All models received seven cloud-controlling factors (CCF) at each timestamp to predict total cloud cover simultaneously. Several statistical models applied here are found to predict cloud cover with similar or better performance than the leading meteorological reanalysis. The best model using recurrent neural networks and cloud cover feedback achieves a correlation coefficient of 0.72 between predictions and MODIS measurements, compared to 0.65 for the reanalysis. Applications of these models are investigated. We determine sensitivities of cloud cover to cloud-controlling parameters by adding different perturbations to CCFs and recording consequent changes. This sensitivity study reveals a nonlinear relationship between cloud cover and numerous CCFs.

Nutrient resorption from senescing leaves can significantly affect plant nutrient status and growth, making it an important process for carbon-cycle predictions for land surface models (LSMs). Based on a recent analysis of global nutrient resorption patterns from trait data, we develop a dynamic scheme of nitrogen (N) resorption driven by leaf structural and environmental factors, and test its effect on present-day global simulations for woody plant functional types (PFTs) using the QUINCY biosphere model. Consistent with observations, we predict higher N resorption for the deciduous PFT compared to the evergreen PFTs, while at the same time reproducing the global gradient of decrease in resorption with key environmental drivers such as air temperature within each PFT. As a result, the novel scheme increases N resorption in N-limited plants, enhancing stored N for the subsequent year and reducing internal N limitation. This has cascading implications for ecosystem nutrient pools, plant productivity and, to a limited extent, the response of carbon and N cycling to elevated CO₂. The new scheme contributes to the development of an ecologically realistic representation of nutrient resorption in an LSM, with implications for both present day and future N limitation of the terrestrial biosphere.

Process-based hydrologic simulations in large domains generally require intensive computing resources. In this study, we implement various parallelization approaches within a process-based hydrologic solver, SUMMA, including the Message Passing Interface (MPI), Open Multi-Processing (OMP), and the Actor Model, to enable high-performance computing for large-domain hydrologic simulations. We provide detailed guidelines on these implementations to assist hydrologists in parallelizing their models effectively. Using a hydrologic simulation over North America as a case study, we compare the scalability, computational cost, input/output performance, and coupling capabilities of these parallel approaches with the original sequential approach. Our results show that the SUMMA-MPI exhibits linear scaling up to 1,024 cores, whereas SUMMA-OMP is only recommended for smaller numbers of cores. The MPI approach exhibited a straggler effect, resulting in core utilization of only 80%. To address this, we introduced a load-balancing calibration based on historical runs, which increases SUMMA-MPI core usage to 95% and thereby mitigates the straggler effect. With regard to coupling capabilities, MPI is the most effective for large-scale simulations involving multiple nodes and extensive core counts, supporting strong coupling and synchronization. The Actor Model reveals its excellent fault tolerance that enables automatic modification and recommencement of specific Grouped Response Units (GRUs) rather than restarting the entire simulation in the event of a failure within the simulation. Through this study, the implementation details of multiple parallelization schemes are documented and their advantages and limitations are discussed, which provides parallel computing insights for advancing computational hydrology in the Earth System Science community.

Carbon–nitrogen coupling is a critical constraint for improving carbon cycle and climate simulations in Earth system models (ESMs), yet large uncertainties hinder inter-model comparisons. Here, we present CNit v2.0, an updated representation of the carbon–nitrogen cycle in MAGICC—a widely used reduced-complexity model (RCM). CNit v2.0 is calibrated to emulate carbon–nitrogen cycle dynamics in various ESMs across historical, idealized (1pctCO2, 1pctCO2-bgc), and multiple Shared Socioeconomic Pathway (SSP) experiments, demonstrating strong emulation performance. The global annual-mean emulation from historical to SSP5-8.5 (1850–2100) reveals increasing nitrogen limitation on net primary production (NPP), with a multi-model mean inhibition of 10.2 ± 5.6% by 2100 due to nitrogen deficits limiting plant uptake. The stronger CO₂ fertilization effect in carbon-only (C-only) ESMs exceeds the mitigating influence of nitrogen limitation in CN-coupled ESMs, implying a risk of continued NPP overestimation in C-only ESMs—even if a nitrogen cycle is later added—due to insufficient constraints on CO₂ sensitivity. The climate response of litter production is sign-changing between C-only (inhibition) and CN-coupled (enhancement) ESMs, suggesting nitrogen effects may be misattributed as climate effects in C-only ESMs. Divergent climate responses and nitrogen effects on litter decomposition—particularly litter respiration and labile soil organic matter decomposition—are the primary drivers of total heterotrophic respiration differences between C-only and CN-coupled ESMs. Alongside NPP, these factors shape distinct carbon cycle dynamics. While nitrogen pools and fluxes generally follow carbon trends, they exhibit greater inter-model spread. In light of the calibration updates, we propose practical strategies to improve carbon cycle calibration in future RCMs.

Estimating and modeling background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertia-gravity modes—an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neural-network autoencoder (AE). The AE was trained using 40 years of ERA5 reanalysis data, enabling it to capture flow-dependent atmospheric balances from a diverse set of weather states. We demonstrate that performing DA in the latent space yields analysis increments that preserve multivariate horizontal and vertical physical balances in both tropical and midlatitude atmosphere. Assimilating a single 500 hPa geopotential height observation in the midlatitudes produces increments consistent with geostrophic and thermal wind balance, while assimilating a total column water vapor observation with a positive departure in the nearly-saturated tropical atmosphere generates an increment resembling the tropical response to (latent) heat-induced perturbations. The resulting increments are localized and flow-dependent, and shaped by orography and land-sea contrasts. Forecasts initialized from these analyses exhibit realistic weather evolution, including the excitation of an eastward-propagating Kelvin wave in the tropics. Finally, we explore the transition from using synthetic ensembles and a climatology-based background error covariance matrix to an operational ensemble of data assimilations. Despite significant compression-induced variance loss in some variables, latent-space assimilation produces balanced, flow-dependent increments—highlighting its potential for ensemble-based latent-space 4D-Var.

Several different time periods of the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) are isolated for examining how the depiction of tradewind marine shallow cumuli in single-column models (SCMs) is affected by choices about model physics. The periods of interest are times when the NOAA Research Vessel Ronald H. Brown and research aircraft WP-3D Orion were collocated, enabling verification of initial conditions and large-scale forcing (advective) tendencies constructed using gridded data from the fifth generation ECMWF atmospheric reanalysis (ERA5). To demonstrate how this new ATOMIC test case can be used to guide model development, three parameterization suites of the NOAA Unified Forecast System are evaluated within the Common Community Physics Package Single Column Model (CCPP SCM). Calculations are also performed using a large-eddy simulation (LES) to further bridge the gap between observations and SCM output, all of which are separated into regimes of either relatively active (“cloudy”) or inactive (“clear”) marine shallow cumuli. In both regimes tested, the parameterization suites tend to: (a) generate an unrealistic skewed or bimodal distribution of cloud fraction, (b) overestimate light to moderate rain rates, (c) produce an erroneously cold and dry boundary layer, and (d) produce higher-than-observed cloud tops. Results show that modifying the treatment of cloud fraction as well as increasing spatial and temporal resolution help bring the SCM more in line with observations. In addition, evidence is found to suggest that some of the remaining model biases may stem from intrinsic differences in the spatio-temporal sampling properties of the observations versus SCM output.

We have addressed convective self-aggregation (CSA) in steady and oscillating sea surface temperature (SST) and solar radiation (SOLIN) cloud-resolving model simulations in a non-rotating radiative-convective equilibrium (RCE) framework. Our experiment designs are motivated by land-ocean heterogeneity of atmospheric convection. The steady and oscillating forcings are idealizations of ocean and land conditions, respectively, based on their differences in heat capacities. In both kinds of simulations, the diurnal mean SST and SOLIN are the same, and both SST and SOLIN are only varied in time (i.e., they are spatially homogeneous at any given time). We find that diurnally oscillating forcing accelerates CSA. Stronger long-wave cooling in dry regions at night and during the warm SST phase (late afternoon) both allow the long-wave feedback, known to favor aggregation, to intensify compared to steady forcing simulations. In addition to the long-wave, reduced short-wave warming in dry regions (during the day) further enhances radiative cooling there compared to moist regions. Overall, the radiative cooling is enhanced in dry regions compared to neighboring moist convective regions. A dry subsidence is driven by this net radiative (short-wave plus long-wave) cooling, consistent with earlier work on CSA. Stronger radiative cooling allows stronger subsidence which allows low-level circulation to more efficiently transport moisture and energy up-gradient, driving convection to aggregate faster. We also note a sensitivity of our experimental setup to initial conditions, more so at warmer SST. This stochastic behavior might be critical in reconciling the differences of opinion regarding the response of convection aggregation to oscillating SST forcing.