Seminar: Joowan Kim

Please join us tomorrow, Wednesday at 14:35 in Burnside 934 for a student seminar by Joowan Kim. Abstract is as follows:

 

Climatology of ERA-Interim and ensemble of CMIP5 models.

Annual-mean climatology (1979-2005) of 100-hPa temperature from a) ERA-Interim and b) ensemble of CMIP5 models. White contours denote OLR from observation and model ensemble respectively. c) Taylor diagram of the temperature field within 15S-15N for individual models (open and closed circles) and their ensemble (cross).

Thermal characteristics of the tropical tropopause layer in CMIP5 models: historical simulations

The climatology and variability of temperatures in the tropical tropopause layer are investigated in 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for historical simulations. The climatology of 100-hPa temperatures compare well with ERA-Interim reanalysis. The models possess reasonable temperature minima in the deep tropics, but some models also have a warm bias or a bias in the location of the temperature minima. The CMIP5 models generally capture the phase of the seasonal cycle in 100-hPa temperatures, but the amplitude of the seasonal cycle varies greatly among models. The interannual variability in 100-hPa temperature is associated with the El Niño-Southern Oscillation (ENSO) and volcanic forcing in observation and CMIP5 models. Most of models successfully capture the ENSO-related large scale response, but the response to volcanic forcing is overestimated in many models. On intraseasonal timescales, observed and modeled variability is dominated by equatorial waves (Kelvin, inertio-gravity, and mixed Rossby-gravity waves) and the Madden-Julian Oscillation (MJO). Most models show variability related to the equatorial waves, but significant biases are found in the phase speeds of the waves when compared to ERA-Interim. The MJO signature is weak and non-distinguishable from the Kelvin wave power in most CMIP5 models.

Student Seminar: Melissa Gervais

Please join us tomorrow in Burnside 934 at 14:35 for a student seminar by Melissa Gervais. Abstract follows.

How Well is the Distribution of Precipitation Represented? Part I: Impacts of Station Density and Resolution Changes on Gridded Station Data

Precipitation is one of the most important variables to predict in future climate change owing to the socio-economic implications for water resources. However, it has historically been a very challenging variable for climate models to predict. Newer versions of Community Climate System Model (CCSM) from the National Center for Atmospheric Research (NCAR) have seen great improvements in their representation of the distribution of precipitation, with results now very close to observations (Gent 2011). The accuracy of precipitation observations used to validate the GCM output is thus becoming increasingly important. Results will be presented from the first of two studies on examining the ability of observations, reanalysis, the CCSM4 fully coupled model, and the NCAR Community Atmosphere Model (CAM5), to represent the distribution of precipitation. Here, we focus on the accuracy of interpolating station data in terms of the method of interpolation and the station density.

Station data from the Global Historical Climatology Network – Daily Version 1.0, within the United States, will be used to create and test gridded precipitation products. The goal is firstly to examine what the impact of gridding station data is on the precipitation statistics and whether the gridding method used is important. Secondly, an experiment will be conducted to determine how dense an observation network needs to be, in different climatic regions, in order to produce an accurate distribution of precipitation. This allows us to identify regions where station density is not high enough to trust the gridded precipitation data for validating GCMs.

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