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OVERALL PERFORMANCE MEASURES

1st QUARTER METRIC COMPLETED: 

IMPROVING SIMULATIONS OF ATMOSPHERIC RIVERS AND HEAT WAVES IN THE COUPLED E3SM 

Product Definition

The Western US is subject to many natural hazards, such as floods, droughts, heat waves, and fires.  Continued investments in modeling efforts from the Department of Energy (DOE) have enabled examination of some of these hazards with the Energy Exascale Earth System Model (E3SM) at high spatial resolution – 25 km grid spacing compared to the standard, or low resolution, 100 km grid spacing. 

Atmospheric Rivers (ARs) are filamentary bands of increased atmospheric moisture transport.  Nearly all poleward moisture transport out of the subtropics occurs through ARs (Zhu & Newell, 1998).  ARs have been shown to be a valuable source of freshwater supplies, while also having the capacity to bring about flooding, landslides, and wind damage, making them a key component of the hydrologic cycle to understand.  Models like E3SM are a critical tool for predicting variabilities and changes in ARs, but uncertainties remain regarding which aspects of modeling, such as horizontal resolution, may improve the representation of ARs.

Heat waves (HWs) are periods of extreme near-surface temperatures that last on the order of days.  As the global mean temperature has increased, so too have the frequency and intensity of HWs (Seneviratne, 2021).  HWs are especially concerning, owing to their links to increased morbidity (Ye et al., 2012), only to be exacerbated by increasing average temperatures under a warming climate (Huang et al., 2011).  Like ARs, models have biases in representing these events.

The DOE-sponsored TempestExtremes software product (Ullrich & Zarzycki, 2017; Ullrich et al., 2021) allows for tracking of ARs and HWs as objects.  TempestExtremes has been used to track ARs repeatedly in the past (e.g., Liu et al., 2022; Rhoades et al., 2021).  HWs, to date, have primarily been examined using timeseries analysis performed at individual locations.  Here, we leverage TempestExtremes recently developed object-tracking capability within a new methodological framework to examine HWs as widespread events instead of using a traditional point-wise evaluation.

In this document, we evaluate the performance of E3SM in simulating ARs impacting the US West Coast and HWs in the Western US.  As part of the Phase 1 water cycle simulation campaign, E3SMv1 was used to perform simulations at high resolution (~25 km grid spacing) and low resolution (~100 km grid spacing) to evaluate the impacts of model resolution on representing water cycle processes. Both ARs and HWs are thus examined at high and low resolutions and the differences are compared and summarized herein.  Results show the impact of increased resolution on ARs is mixed, with some features showing improvements (capturing wind and precipitation extremes) while others showing degradation (frequency, position, and shape).  Increased resolution improves HW intensity and duration, while HW frequency is rather insensitive to the model resolutions examined.

Product Documentation

This report analyzes fully-coupled simulations from the E3SMv1 high-resolution (E3SM-HR) and low-resolution (E3SM-LR) experiments, as detailed in Caldwell et al. (2019) following a similar protocol of the Coupled Model Intercomparison Project Phase 6 (CMIP6) HighResMIP experiments (Haarsma et al. 2016).  Both E3SM-HR and E3SM-LR have active atmosphere, land, ocean, sea-ice, and river components.  The atmosphere model is described by Rasch et al. (2019) and makes use of a 72-level spectral element dynamical core to solve the primitive equations (Dennis et al., 2012).  Parameterized processes include deep convection (Neale et al., 2008; Richter & Rasch, 2008; Zhang & McFarlane, 1995); macrophysics, turbulence, and shallow convection (Golaz et al., 2002; Larson, 2017; Larson & Golaz, 2005); microphysics (Gettelman & Morrison, 2015; Gettelman et al., 2015); aerosol treatment (Liu et al., 2016; Wang et al., 2020); and radiative transfer (Iacono et al., 2008; Mlawer et al., 1997).  The ocean and sea-ice models use the Model for Prediction Across Scales (Petersen et al., 2019; Ringler et al., 2013), and a mesoscale eddy parameterization (Gent & Mcwilliams, 1990) is used for E3SM-LR only.  The land model is based on the Community Land Model version 4.5 (Oleson et al., 2013), run with satellite phenology.  The river model is the Model for Scale Adaptive River Transport (Li et al., 2013; Li et al., 2015), using runoff simulated by the land model to compute channel velocity, channel water depth, and water surface area and simulate streamflow.

E3SM-HR and E3SM-LR use an identical set of physical parameters but differ in the horizontal grid-spacing of each model component.  Each component has approximately a factor of four grid refinement.  The atmosphere and land components share a grid with spacing roughly equal to 25 km for E3SM-HR and 110 km for E3SM-LR.  The ocean and sea-ice models also share a grid.  The ocean/sea-ice grid varies in resolution globally, spanning 8-16 km for E3SM-HR and 30-60 km for E3SM-LR.

To track ARs and HWs, we use the TempestExtremes software (Ullrich & Zarzycki, 2017).  ARs are tracked as features with large (area exceeding 850000 km2), localized vertically-integrated water vapor transport, that do not also fit the criteria for Tropical Cyclones (warm core low pressure systems).  HWs are tracked as features whose daily maximum temperature exceeds the 95th percentile temperature for that location and during the May through September period for at least two consecutive days.  In both cases, metrics like AR and HW frequency are sensitive to the specific feature definition chosen (e.g., Shields et al., 2018), which has implications for evaluating model performance in simulating these extreme events (Leung et al., 2022). Investigating the impacts of such sensitivity on our model evaluation results is beyond the scope of this study but should be explored in the future.

After tracking of ARs and HWs in the E3SM HR and LR simulations and in observations, different aspects of these extreme events are analyzed and compared between the HR and LR simulations and the observations.