16 research outputs found
Application of MJO Simulation Diagnostics to Climate Models
The ability of eight climate models to simulate the Madden-Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November-April) behavior. Initially, maps of the mean state and variance and equatorial space-time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space-time spectral peak in the zonal wind compared to that of precipitation. Using the phase-space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (~20- 29 days) than observations (~31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30-80-day band. Several key variables associated with the model's MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.open1149
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Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
During the Last Interglacial, approximately 129 to 116 ka (thousand years ago), the Arctic summer climate was warmer than the present, and the Greenland Ice Sheet retreated to a smaller extent than its current state. Previous model-derived and geological reconstruction estimates of the sea-level contribution of the Greenland Ice Sheet during the Last Interglacial vary widely. Here, we conduct a transient climate simulation from 127 to 119 ka using the Community Earth System Model (CESM2), which includes a dynamic ice sheet component (the Community Ice Sheet Model, CISM2) that is interactively coupled to the atmosphere, land, ocean, and sea ice components. Vegetation distribution is updated every 500 years based on biomes simulated using a monthly climatology to force the BIOME4 equilibrium vegetation model. Results show a substantial retreat of the Greenland Ice Sheet, reaching a minimum extent at 121.9 ka, equivalent to a 3.0 m rise in sea level relative to the present day, followed by gradual regrowth. In contrast, a companion simulation employing static vegetation based on pre-industrial conditions shows a much smaller ice-sheet retreat, highlighting the importance of the changes in high-latitude vegetation distribution for amplifying the ice-sheet response. © 2021. The Authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model
Most global climate models parameterize separate cloud types using separate
parameterizations. This approach has several disadvantages, including obscure
interactions between parameterizations and inaccurate triggering of cumulus
parameterizations.
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Alternatively, a unified cloud parameterization uses one equation set to
represent all cloud types. Such cloud types include stratiform liquid and ice
cloud, shallow convective cloud, and deep convective cloud. Vital to the
success of a unified parameterization is a general interface between clouds
and microphysics. One such interface involves drawing Monte Carlo samples of
subgrid variability of temperature, water vapor, cloud liquid, and cloud ice,
and feeding the sample points into a microphysics scheme.
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This study evaluates a unified cloud parameterization and a Monte Carlo
microphysics interface that has been implemented in the Community Atmosphere
Model (CAM) version 5.3. Model computational expense is estimated, and
sensitivity to the number of subcolumns is investigated. Results describing
the mean climate and tropical variability from global simulations are
presented. The new model shows a degradation in precipitation skill but
improvements in shortwave cloud forcing, liquid water path, long-wave cloud
forcing, precipitable water, and tropical wave simulation