Publications

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The June 15, 1991 Mt. Pinatubo eruption is simulated in E3SM by injecting 10 Tg of SO₂ gas in the stratosphere, turning off prescribed volcanic aerosols, and enabling E3SM to treat stratospheric volcanic aerosols prognostically. This experimental prognostic treatment of volcanic aerosols in the stratosphere results in some realistic behaviors (SO₂ evolves into H₂SO₄ which heats the lower stratosphere), and some expected biases (H₂SO₄ aerosols sediment out of the stratosphere too quickly). Climate fingerprinting techniques are used to establish a Mt. Pinatubo fingerprint based on the vertical profile of temperature from the E3SMv1 DECK ensemble. By projecting reanalysis data and preindustrial simulations onto the fingerprint, the Mt. Pinatubo stratospheric heating anomaly is detected. Projecting the experimental prognostic aerosol simulation onto the fingerprint also results in a detectable heating anomaly, but, as expected, the duration is too short relative to reanalysis data.

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Results from the new DOE super-parameterized (SP) Energy Exascale Earth System Model (SP-E3SM) are analyzed and compared to the traditionally parameterized E3SMv1 and previous studies using SP models. SP-E3SM is unique in that it utilizes GPU hardware acceleration, CRM mean-state acceleration, and reduced radiation to dramatically increase the model throughput and allow decadal experiments at 100-km external resolution. It also differs from other SP models by using a spectral element dynamical core on a cubed sphere grid and a finer vertical grid with a higher model top. Despite these differences, SP-E3SM generally reproduces the behavior of other super-parameterized models. Tropical wave variability is improved relative to E3SM, including the emergence of a Madden-Julian Oscillation and a realistic slowdown of Moist Kelvin Waves. However, the distribution of precipitation exhibits an unrealistically large variance, and while the timing of diurnal rainfall shows modest improvements the signal is not as coherent as observations. A notable grid imprinting bias is identified in the precipitation field and attributed to a unique feedback associated with the interactions between explicit convection and the spectral element grid structure. Spurious zonal mean column water tendencies due to grid imprinting are quantified – while negligible for the conventionally parameterized E3SM, they become large with super-parameterization, approaching 10% of the physical tendencies. The implication is that finding a remedy to grid imprinting will become especially important as spectral element dynamical cores begin to be combined with explicitly resolved convection.

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Satellite simulators are often used to account for limitations in satellite retrievals of cloud properties in comparisons between models and satellite observations. The purpose of this framework is to enable more robust evaluation of model cloud properties, so that differences between models and observations can more confidently be attributed to model errors. A critical step in this process is accounting for the difference between the spatial scales at which cloud properties are retrieved with those at which clouds are simulated in global models. In this study, we create a series of sensitivity tests using 4-km global model output from the multiscale modeling framework to evaluate the sensitivity of simulated satellite retrievals to common assumptions about cloud and precipitation overlap and condensate variability used in climate models whose grid spacing is many tens to hundreds of kilometers. We find the simulated retrievals are sensitive to these assumptions. Using maximum-random overlap with homogeneous cloud and precipitation condensate leads to errors in Multiangle Imaging Spectroradiometer and International Satellite Cloud Climatology Project-simulated cloud cover and in CloudSat-simulated radar reflectivity that are significant compared to typical differences between the model simulations and observations. A more realistic treatment of unresolved clouds and precipitation is shown to substantially reduce these errors. The sensitivity to these assumptions underscores the need for the adoption of more realistic subcolumn treatments in models and the need for consistency among subcolumn assumptions between models and simulators to ensure that simulator-diagnosed errors are consistent with the model formulation.

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