B31. Influence of Climate Bias on Extreme Events
- Holly Davis (Unlicensed)
- Renata McCoy
| Poster Title | Isolating the Influence of Climate Bias on Extreme Events Using Initialized Ensembles |
|---|---|
| First Author | Ramalingam Saravanan |
| Topic | atmospheric model development, applied E3SM |
| Affiliation | RGMA |
| Link to document |
Title
Isolating the Influence of Climate Bias on Extreme Events Using Initialized Ensembles
Authors
Ramalingam Saravanan, Texas A&M
Abstract
One of the important applications of global climate models is to predict anticipated changes in the statistical properties of extreme events. Tropical cyclones (TCs) are among the extreme events with the greatest socioeconomic impacts in the United States and other regions of the world. Although coarse-resolution global climate models are not capable of simulating individual TCs accurately, they do exhibit significant skill in simulating the interannual and decadal variations in the aggregate statistics of TCs. We propose to analyze and simulated extremes in E3SM, with a focus on TC activity on the Northern Hemisphere.
One of the challenges in simulating the spatial distribution of TCs and other extreme events is the impact of climate bias. Since these biases can develop within a few weeks from the start of a simulation, it becomes difficult to distinguish between the flow bias effect and other possible deficiencies in the climate model, such as errors in the representation of clouds or poor spatial resolution. To address this problem, we propose to use an initialized ensemble approach, where a series of short (14-day) weather forecasts is carried out using the high resolution (0.25-degree) EAM, the atmospheric component of E3SM, for the Northern Hemisphere TC season. The background flow in the ensemble-average of the forecasts will be close to the observed flow, by construction, whereas long EAM control runs will exhibit fully developed biases. The integrations will be performed for the 10-year period 2000-2009, initialized from atmospheric analyses and using observed sea-surface temperature and sea-ice during this period as the surface boundary condition. Comparing the statistics of TC simulations in the initialized forecast ensemble to those in the control run of EAM will allow us to isolate the impact of mean flow biases.