Journal of Advances in Modeling Earth Systems最新文献

Different turbulent entrainment-mixing mechanisms between clouds and environment are essential to cloud-related processes; however, accurate representation of entrainment-mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment-mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment-mixing. Specifically, number concentration after entrainment-mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment-mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment-mixing is negatively correlated with liquid water content affected by entrainment-mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment-mixing in weather/climate models.

Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature-based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long-standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer-scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate.

Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations. ML-based parameterizations within hybrid ESMs have successfully learned subgrid-scale processes from short high-resolution simulations. However, most studies used a particular ML method to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small-scale processes (e.g., radiation, convection, gravity waves) in mostly idealized settings or from superparameterizations. Here, we use a filtering technique to explicitly separate convection from these processes in simulations with the Icosahedral Non-hydrostatic modeling framework (ICON) in a realistic setting and benchmark various ML algorithms against each other offline. We discover that an unablated U-Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes. While we were able to connect the learned relations of the U-Net to physical processes this was not possible for the non-deep learning-based Gradient Boosted Trees. The ML algorithms are then coupled online to the host ICON model. Our best online performing model, an ablated U-Net excluding precipitating tracer species, indicates higher agreement for simulated precipitation extremes and mean with the high-resolution simulation compared to the traditional scheme. However, a smoothing bias is introduced both in water vapor path and mean precipitation. Online, the ablated U-Net significantly improves stability compared to the non-ablated U-Net and runs stable for the full simulation period of 180 days. Our results hint to the potential to significantly reduce systematic errors with hybrid ESMs.

The atmosphere-ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub-diurnal scale processes at the air-sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub-diurnal scale air-sea interaction processes are important for ocean mixing. Here, by designing an integrated sub-diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub-diurnal air-sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high-frequency air-sea interactions more realistically.

Climate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed-scale approaches to understand water source, storage, and exit flux connections using a dynamic-flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow-derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt-derived streamflow preferentially discharges older water from snow-derived storage. Dynamic-flux particle tracking and new approaches presented here enable novel model-tracer comparisons in large-scale watersheds to better understand watershed behavior in a changing climate.

We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-hr time resolution for up to 1-year lead times on a 110-km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state-of-the-art (SOTA) machine learning (ML) weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet, at 1-week lead times, its skill is only about 1 day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Center for Medium-Range Weather Forecasts. We report several improvements in model design, including switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net, and introducing gated recurrent units (GRU) on each level of the U-Net hierarchy. The consistent east-west orientation of all cells on the HEALPix mesh facilitates the development of location-invariant convolution kernels that successfully propagate weather patterns across the globe without requiring separate kernels for the polar and equatorial faces of the cube sphere. Without any loss of spectral power after the first 2 days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in 1-year simulations.

The representation of subgrid-scale orography is a challenge in the physical parameterization of orographic gravity-wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non-quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale-aware spectrum of subgrid-scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well-known least-squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least-squares spectral analysis in representing the physical energy of a spectrum. Studies involving real-world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work.

Climate models have long-standing difficulties simulating the South Pacific Convergence Zone (SPCZ) and its variability. For example, the default Zhang-McFarlane (ZM) convection scheme in the Community Atmosphere Model version 5 (CAM5) produces too much light precipitation and too little heavy precipitation in the SPCZ, with this bias toward light precipitation even more pronounced in the SPCZ than in the tropics as a whole. Here, we show that implementing a recently developed convection scheme in the CAM5 yields significant improvements in the simulated SPCZ during austral summer and discuss the reasons behind these improvements. In addition to intensifying both mean rainfall and its variability in the SPCZ, the new scheme produces a larger heavy rainfall fraction that is more consistent with observations and state-of-the-art reanalyses. This shift toward heavier, more variable rainfall increases both the magnitude and altitude of diabatic heating associated with convective precipitation, intensifying lower tropospheric convergence and increasing the influence of convection on the upper-level circulation. Increased diabatic production of potential vorticity in the upper troposphere intensifies the distortion effect exerted by convection on transient Rossby waves that pass through the SPCZ. Weaker distortion effects in simulations using the ZM scheme allow waves to propagate continuously through the region rather than dissipating locally, further reducing updrafts and weakening convection in the SPCZ. Our results outline a dynamical framework for evaluating model representations of tropical–extratropical interactions within the SPCZ and clarify why convective parameterizations that produce “top-heavy” profiles of deep convective heating better represent the SPCZ and its variability.

The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography-based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.

Machine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super-particle model McSnow are used as training data. The ML performs a coarse-graining of the particle-resolved microphysics to multi-category two-moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML-based bulk model. The ML-based scheme is tested with simulations of increasing complexity. As a box model, the ML-based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML-based P3-like scheme provides a more realistic extended stratiform region when compared to the standard two-moment bulk scheme in ICON. In a realistic case study, the ML-based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse-grain super-particle simulations to a bulk scheme of arbitrary complexity.