Evaluating clouds in the AM3 model using atmospheric classification

Poster PDF

Description

Parameterizations in models attempt to statistically relate large-scale atmospheric variables such as temperature, humidity, and winds with variables that change on scales too small to resolve, such as cloud properties. We study the observed relationships between such large- and small-scale variables through creation of a set of atmospheric states and associated distributions of small-scale variables. We compare the occurrence of these states and their observed relationships to cloud properties with those that exist in the GFDL’s AM3 model. In doing so, we demonstrate that the model struggles to create cold fronts, does not generate enough deep convection in conditions with large-scale resolved ascent, and creates too much deep convection from parameterized convection. We use a clustering technique to define a set of atmospheric states for a region surrounding the ARM SGP site. Large-scale variables from the ERA-Interim reanalysis are the input to the clustering algorithm to define the states, and cloud occurrence data from the cloud radar at the SGP site is used to validate the statistical significance of each state. Once the states are defined, we classify the state of the atmosphere every 6 hours for the duration of the study, creating a time series of atmospheric state. This time series is used as a basis for compositing simultaneous observations of interest and creating distributions of small-scale variables associated with each of the states. Distributions of both ground-based observations from the SGP site such as cloud occurrence, precipitation, and energy fluxes as well as satellite-derived equivalents are created in this fashion. Snapshots from the AM3 model are sorted into the observed atmospheric states, and their associated distributions of cloud, precipitation, and radiative properties are composited to produce modeled distributions of parameterized variables for each atmospheric state. Comparison of the observed and modeled distributions of small-scale variables within an individual state tests the model parameterization under a particular set of physical conditions. In contrast, comparing the observed frequency of occurrence of the atmospheric states with their occurrence within AM3 tests how well the model reproduces the large-scale conditions of the region. In doing so, we parse the model bias for variables such as cloud occurrence into contributions from parameterization performance, and contributions from the large-scale conditions.