Journal of Advances in Modeling Earth Systems最新文献

Aquaplanet experiments are used to investigate the statistical convergence of the Global Storm-Resolving model (GSRM) ICOsahedral Nonhydrostatic (ICON) model, under successive, two-fold horizontal grid spacing refinements from 160 to 1.25 km. A methodology based on the Richardson extrapolation method is used with the aquaplanet hemispherical symmetry to quantify convergence. We use the symmetrical and anti-symmetrical solution components to estimate the asymptotic convergence pattern, the asymptotic estimate, and sampling uncertainty. Based on successive horizontal grid refinements, different climate statistics are explored to determine whether they enter a convergent regime and, if so, what their convergent value is. Our analysis focuses on global-mean statistics related to the general circulation and aspects that influence the climate: the meridional overturning circulation, the tropical structure (the Inter-Tropical Convergence Zone and the zonal mean thermodynamic state), and the energy and water budget. Our results show a kilometer and hectometer-scale horizontal grid spacing requirement for statistical convergence of the meridional overturning circulation structure and global mean statistics. Distinctively, the tropical structure is estimated to be very near its asymptotic values at km-scale grid spacing, but its intensity appears to converge more slowly, as do the storm-track and jet-stream. As we increase the horizontal grid spacing, cloud reduction and the zonal distribution of water vapor convergent pattern drive convergence in the energy and water budgets. We conclude that the ICON GSRM without convection parameterization exhibits statistical convergence at 10 km horizontal grid spacing in aquaplanet experiments across many of the metrics studied, specifically in the large-scale and tropical vertical structure.

Hot and moist “hothouse” climates occurred in Earth's past and are expected in Earth's far future climate, driven by increasing solar luminosity. In hothouse climate regimes, precipitation transitions from a quasi-steady state, as in present-day tropical convection, to an “episodic deluge” or relaxation-oscillator (RO) regime where precipitation occurs in intense bursts separated by multi-day dry spells. Recent studies suggest that the transition to RO convection regimes is radiatively driven. However, the transition from steady state to RO convection has only been studied with radiative convective equilibrium (RCE) simulations with constant insolation, excluding the diurnal cycle. Precipitation and convection are strongly linked to the diurnal cycle in Earth's present climate over both land and ocean. We explore the impact of the diurnal cycle on the transition from steady state to RO convection using two sets of small-domain RCE simulations with ocean and swamp-like surface boundary conditions. Our RCE simulations with ocean boundary conditions show convection transitions to an episodic deluge regime at 322 K and the diurnal cycle modulates precipitation to occur during late-night or near dawn, when convective inhibition is the weakest. Our RCE simulations with swamp-like boundary conditions, which allow for mean surface temperature variations, show that as RO states emerge, the diurnal cycle modulates precipitation to primarily occur during the late-afternoon to about dusk; but as the mean SST increases, precipitation occurs during the late-night to dawn. These results show that the diurnal cycle strongly influences the timing of convection and precipitation patterns in extreme climates.

Historically, large areas have been deforested, croplands and rangelands have expanded, and the irrigation area has grown substantially. These land use and land cover changes have altered land surface properties, driving changes in near-surface air temperature. From limited observations and mostly idealized simulations, we know that altering sufficiently large land surface areas can lead to systematic changes in temperature and precipitation outside altered areas. The advection of temperature anomalies, atmosphere, land, and ocean feedbacks are known to be potential drivers of such non-local responses. We show that regionally, non-local temperature signals driven by land-use change can be robustly found in fully coupled Community Earth System Model 2 (CESM2) simulations of the historical period (1850–2014) with all forcings versus all-but-land-use-change forcings. With regional-scale warming of up to more than 1 K and cooling of up to more than 0.5 K, the modeled effects are commensurate to historical temperature effects of all forcings. Regional non-local warming and cooling balance out in the global mean to an effect that is small compared to internal variability (IntV). We analyze how the signal-to-noise ratio of spatially averaged signals depends on the number of ensemble members included from the CESM2 large ensemble. Furthermore, we discuss the ability of our and other signal separation techniques to distinguish different parts of the signal from each other and from IntV. Finally, we discuss future research needs for reliable conclusions on non-local biogeophysical effects of land use change to inform future land-based climate change mitigation strategies.

Historically, large areas have been deforested, croplands and rangelands have expanded, and the irrigation area has grown substantially. These land use and land cover changes have altered land surface properties, driving changes in near-surface air temperature. From limited observations and mostly idealized simulations, we know that altering sufficiently large land surface areas can lead to systematic changes in temperature and precipitation outside altered areas. The advection of temperature anomalies, atmosphere, land, and ocean feedbacks are known to be potential drivers of such non-local responses. We show that regionally, non-local temperature signals driven by land-use change can be robustly found in fully coupled Community Earth System Model 2 (CESM2) simulations of the historical period (1850–2014) with all forcings versus all-but-land-use-change forcings. With regional-scale warming of up to more than 1 K and cooling of up to more than 0.5 K, the modeled effects are commensurate to historical temperature effects of all forcings. Regional non-local warming and cooling balance out in the global mean to an effect that is small compared to internal variability (IntV). We analyze how the signal-to-noise ratio of spatially averaged signals depends on the number of ensemble members included from the CESM2 large ensemble. Furthermore, we discuss the ability of our and other signal separation techniques to distinguish different parts of the signal from each other and from IntV. Finally, we discuss future research needs for reliable conclusions on non-local biogeophysical effects of land use change to inform future land-based climate change mitigation strategies.

Machine learning for the parameterization of subgrid-scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data-driven parameterization schemes is how to properly represent rare, but important events that occur in geoscience data sets. We investigate and develop strategies to reduce errors caused by insufficient sampling in the rare data regime, under constraints of no new data and no further expansion of model complexity. Resampling and importance weighting strategies are constructed with user defined parameters that systematically vary the sampling/weighting rates in a linear fashion and curb too much oversampling. Applying this new method to a case study of gravity wave momentum transport reveals that the resampling strategy can successfully improve errors in the rare regime at little to no loss in accuracy overall in the data set. The success of the strategy, however, depends on the complexity of the model. More complex models can overfit the tails of the distribution when using non-optimal parameters of the resampling strategy.

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning-based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM-MOSART-DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM-MOSART-DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM-MOSART-DOC to accurately capture long-term average DOC concentrations, with Kling-Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long-term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM-MOSART-DOC integrates process-based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning-based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM-MOSART-DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM-MOSART-DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM-MOSART-DOC to accurately capture long-term average DOC concentrations, with Kling-Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long-term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM-MOSART-DOC integrates process-based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Extensive offshore renewable energy installations have raised concerns about their environmental impacts. These concerns highlight the need for high fidelity modeling of conditions within wind-farm regions, where wave–structure interactions through reflection, diffraction, and dissipation reshape local and regional wave dynamics, thereby influencing energy conversion efficiency and altering surrounding hydrodynamic conditions. However, accurately representing these wave–structure interactions remains a major challenge for spectral wave models, which often oversimplify turbines as energy sinks and thus introduce nonphysical dissipation. This study develops a new parameterization to represent distinct regimes of wave-structure interactions according to the ratio of wavelength to structural size. When wave and structure scales are comparable, wave scattering dominates and is represented as an energy-conserving source term based on diffraction theory, allowing for directional redistribution of wave energy. Drag-induced dissipation dominates for cases where the wavelength greatly exceeds the structural scale and is parameterized by a dissipative source term. Both regimes are formulated within a unified framework and implemented in the wave spectral model, WAVEWATCH III. Numerical simulations demonstrate that the proposed parameterization improves the physical realism of wave–structure interactions. The modeled wave field exhibits a strong dependence on wave–structure scale ratio and a distinct spatial pattern in significant wave height, with amplification upstream of the farm and attenuation downstream. These findings offer a physics-based solution, supporting future offshore renewable energy development and improving the understanding of its impacts on the marine environment.

Understanding the transport pathways of floating material at the ocean surface is important to improve our knowledge on surface circulation and assessing its environmental impacts. Numerical experiments through Lagrangian particle simulations are widely used to investigate the dispersion of floating material, typically relying on velocity fields from ocean circulation models. However, the contribution of different ocean dynamics (at different temporal and spatial scales) to the net Lagrangian transport remains unclear. Here we focus on tidal forcing, only included in recent ocean models, to explore its effect on particle dispersion at the ocean surface. By comparing a twin simulation with and without tidal forcing, we conclude that tide-induced dynamics play an important role in horizontal Lagrangian pathways. We focus on the Azores Islands region and find that surface particles travel a longer cumulative distance and a lower total distance with than without tidal forcing. Additionally, tidal forcing leads to higher variability in surface particle accumulation patterns. The differences found in the surface particle accumulation patterns can be greater than 40%. These findings have important implications for virtual particle simulations, suggesting that considering tidal currents alone may not capture the full range of tide-induced effects. A deeper understanding of the underlying dynamics is essential for accurately analyzing transport properties. Our outcomes can already help improve Lagrangian simulations made to understand the connectivity of marine species and for marine pollution applications, for example, ocean clean-up strategies for plastics or oil spills, in the Azores Islands and regions with similar dynamics.

Understanding the transport pathways of floating material at the ocean surface is important to improve our knowledge on surface circulation and assessing its environmental impacts. Numerical experiments through Lagrangian particle simulations are widely used to investigate the dispersion of floating material, typically relying on velocity fields from ocean circulation models. However, the contribution of different ocean dynamics (at different temporal and spatial scales) to the net Lagrangian transport remains unclear. Here we focus on tidal forcing, only included in recent ocean models, to explore its effect on particle dispersion at the ocean surface. By comparing a twin simulation with and without tidal forcing, we conclude that tide-induced dynamics play an important role in horizontal Lagrangian pathways. We focus on the Azores Islands region and find that surface particles travel a longer cumulative distance and a lower total distance with than without tidal forcing. Additionally, tidal forcing leads to higher variability in surface particle accumulation patterns. The differences found in the surface particle accumulation patterns can be greater than 40%. These findings have important implications for virtual particle simulations, suggesting that considering tidal currents alone may not capture the full range of tide-induced effects. A deeper understanding of the underlying dynamics is essential for accurately analyzing transport properties. Our outcomes can already help improve Lagrangian simulations made to understand the connectivity of marine species and for marine pollution applications, for example, ocean clean-up strategies for plastics or oil spills, in the Azores Islands and regions with similar dynamics.