Journal of Advances in Modeling Earth Systems最新文献

Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy, which is largely controlled by boundary-layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere-only and ocean-atmosphere coupled models. The diurnal cycles of convection in both simulations are significantly improved with small changes to their mean states. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at subdiurnal time scales. Relaxed quasi-equilibrium for convective available potential energy holds in significant measure as a result. Future model improvement will focus on the remaining biases in the diurnal cycle, which may be further reduced by including stochastic entrainment and cold pools.

On ensemble-based coupled data assimilation, cross-component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross-component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so-called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS-inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal-to-noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS-based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow-varying oceanic component, but not vice versa. In comparison with cross-component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.

The computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high-resolution aerosol assimilation system. A 2-dimensional variational method (2DVar) was implemented in a non-hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar-orbital satellites. The model results were evaluated against ground-based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM_2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite-estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM_2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite-retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground-based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.

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.