Superparameterized Model Research Articles

Potential Predictability of the Madden‐Julian Oscillation in a Superparameterized Model

AbstractThe Madden‐Julian Oscillation (MJO) is a promising target for improving sub‐seasonal weather forecasts. Current forecast models struggle to simulate the MJO due to imperfect convective parameterizations and mean state biases, degrading their forecast skill. Previous studies have estimated a potential MJO predictability 5–15 days higher than current forecast skill, but these estimates also use models with parameterized convection. We perform a perfect‐model predictability experiment using a superparameterized global model in which the convective parameterization is replaced by a cloud resolving model. We add a second “silent” cloud resolving component to the control simulation that independently calculates convective‐scale processes using the same large‐scale forcings. The second set of convective states are used to initialize forecasts, representing uncertainty on the convective scale. We find a potential predictability of the MJO of 35–40 days in boreal winter using a single‐member ensemble forecast.

Mesoscale Convective Systems in a Superparameterized E3SM Simulation at High Resolution

AbstractAccurately representing mesoscale convective systems (MCSs) is crucial to simulating the energy and water cycles in global climate models. Using a novel MCS identification and tracking algorithm applied to observations and model simulations, we evaluate how well the Energy Exascale Earth System Model (E3SM) simulates MCSs over the central US. Simulations performed by E3SM at 25 km grid spacing with and without superparameterization (SP) are compared and evaluated against observations using multiple metrics highlighting important MCS characteristics. Compared to E3SM, the superparameterized model (SP‐E3SM) better simulates MCS number and MCS precipitation amount, diurnal cycle, propagation, and the probability distribution of precipitation rate in both spring and summer. The improvement from SP is partly contributed by improvement in simulating the large‐scale environments, featuring enhanced atmospheric low‐level moisture and larger moisture transport to the central US relative to E3SM. However, SP‐E3SM still underestimates MCS precipitation amount, particularly in summer. This underestimation is closely related to the negative bias in MCS precipitation intensity, although the drier environments simulated during summer also contributes to underestimation of MCS frequency. Without SP, the larger bias in MCS precipitation amount is closely related to the negative bias in MCS frequency, while the substantial dry bias in the large‐scale environments also contributes to underestimation of MCS intensity. Our results suggest that SP improves MCS simulation by improving modeling of the large‐scale environments and convection initiation, which are both major limiting factors in E3SM even at 25 km grid spacing where deep convection is represented by a cumulus parameterization.

Predictive Proxies of Present and Future Lightning in a Superparameterized Model

AbstractA superparameterized climate model is used to assess the global performance of several previously proposed proxies for lightning. In particular, predictors incorporating hydrometeor (ice, graupel) profiles and convective vertical velocities are compared to observations, then used to estimate changes in flash rates with global warming. The choice of microphysics parameterization is also investigated, with all predictors showing higher correlations with Lightning Imaging Sensor/Optical Transient Detector observations when using a 2‐moment scheme compared to a 1‐moment representation. All proxies generally agree in their response to warming over tropical land, with notable decreases in Africa, the Middle East, and northern South America, but disagree over oceans and the midlatitudes. The product of convective available potential energy and precipitation predicts increases over these latter areas, as do the 2‐moment ice‐based proxies, while those of the 1‐moment model tend to show decreases, highlighting the importance of cloud microphysics when using climate models to simulate lightning.

Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales

AbstractSome studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.

Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1

AbstractSix month coupled hindcasts show the central equatorial Pacific cold tongue bias development in a GCM to be sensitive to the atmospheric convective parameterization employed. Simulations using the standard configuration of the Community Earth System Model version 1 (CESM1) develop a cold bias in equatorial Pacific sea surface temperatures (SSTs) within the first two months of integration due to anomalous ocean advection driven by overly strong easterly surface wind stress along the equator. Disabling the deep convection parameterization enhances the zonal pressure gradient leading to stronger zonal wind stress and a stronger equatorial SST bias, highlighting the role of pressure gradients in determining the strength of the cold bias. Superparameterized hindcasts show reduced SST bias in the cold tongue region due to a reduction in surface easterlies despite simulating an excessively strong low‐level jet at 1‐1.5 km elevation. This reflects inadequate vertical mixing of zonal momentum from the absence of convective momentum transport in the superparameterized model. Standard CESM1simulations modified to omit shallow convective momentum transport reproduce the superparameterized low‐level wind bias and associated equatorial SST pattern. Further superparameterized simulations using a three‐dimensional cloud resolving model capable of producing realistic momentum transport simulate a cold tongue similar to the default CESM1. These findings imply convective momentum fluxes may be an underappreciated mechanism for controlling the strength of the equatorial cold tongue. Despite the sensitivity of equatorial SST to these changes in convective parameterization, the east Pacific double‐Intertropical Convergence Zone rainfall bias persists in all simulations presented in this study.

Projection of Summer Precipitation over the Southeastern United States in the Superparameterized CCSM4

Abstract Projections of the hydrological cycle over the southeastern United States are compared between CCSM4 and the superparameterized model (SP-CCSM4). Under the extreme forcing of the representative concentration pathway 8.5 (RCP8.5) climate change scenario, in Virginia, North Carolina, South Carolina, and Kentucky, SP-CCSM4 projects a decrease in summer precipitation, whereas the conventionally parameterized CCSM4 projects an increase in summer rainfall. The projected reduction in summer precipitation in SP-CCSM4 is due to the remote influence from the northwest intrusion of the North Atlantic subtropical high, as well as the local decrease of soil moisture content. Both models show that summer precipitation over the southern United States is characterized by a positive feedback with soil moisture. However, in CCSM4 rainfall increases with increasing soil moisture and in SP-CCSM4 rainfall decreases with decreasing soil moisture. The different representation of cloud processes in the two models yields different responses of precipitation to the latent heat flux changes over the southeastern United States. Moreover, multivariate EOF analyses in the two models suggest that the local land–atmosphere interactions have a stronger influence on the projected changes of precipitation over the southeastern United States than does the North Atlantic subtropical high.

Convection in a Parameterized and Superparameterized Model and Its Role in the Representation of the MJO

Abstract The behavior of convection and the Madden–Julian oscillation (MJO) is compared in two simulations from the same global climate model but with two very different treatments of convection: one has a conventional parameterization of moist processes and the other replaces the parameterization with a two-dimensional cloud-resolving model, the so-called superparameterization. The different behavior of local convection and the MJO in the two model simulations reveals that the accurate representation of the following characteristics in the modes of convection might contribute to the improvement of the MJO simulations: (i) precipitation should be an exponentially increasing function of the column saturation fraction, (ii) heavy precipitation should be associated with a stratiform diabatic heating profile, and (iii) there should be a positive relationship between precipitation and surface latent heat flux.