Journal of Advances in Modeling Earth Systems最新文献

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low-cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low-cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

The impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi-angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro-convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one-dimensional plume-rise model (1-D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp-10-585-2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1-D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1-D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM-chem). The coupled CAM-chem-PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1-D PRM while saw a mere 15% increase of computation time.

This study presents the first attempt to simulate a full cycle of the quasi-biennial oscillation (QBO) in a global storm-resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about $��5��k�m�$ and $��400��m�$, respectively, we show that an untuned state-of-the-art GSRM is already on the verge of simulating a QBO-like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary-scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary-scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary-scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio-temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution.

Distribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well-simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE-derived TWSA and streamflow observations, leads to a trade-off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions.

This study presents the development of a so-called Turbulent Kinetic Energy (TKE)-l, or TKE-l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large-scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single-column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.