Browsing by Author "Saravanan, Ramalingam"

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Assessing the influence of background state and climate variability on tropical cyclones using initialized ensembles and mesh refinement in E3SM
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/227; DOE-Office Of Science
The scientific goal of this project is to assess the influence of the background state and horizontal resolution of climate models on the simulation of extreme events like intense precipitation, tropical cyclones, atmospheric rivers, and heatwaves. The main results are: - Initialized ensembles of 4-week forecasts using a climate model (E3SM) show that dynamic (flow) and thermodynamic (moisture) biases asymptote to their climatological values at different rates—dynamic bias grows rapidly and approaches its climatological value within about 2 weeks whereas the thermodynamic bias takes about 4 weeks. - Moisture bias has a bigger impact on extreme events like tropical cyclones and intense precipitation than flow bias, and this impact is non-monotonic over bias evolution time. - Climate models are capable of simulating the flow features associated with unprecedented extreme events like the Western North America heatwave of June 2021, but moisture biases can weaken the surface manifestation of extreme heatwaves. Reducing model bias may be more important than increasing model resolution in improving the fidelity of heatwave simulations in climate models. - Climate models are capable of simulating weather patterns associated with “power droughts”, i.e., periods with low solar and wind power generation, provided model biases in the simulation of the background state can be corrected. - Regional mesh refinement can improve the simulation of extreme weather events but model biases may still persist.
Assessing the influence of background state and climate variability on tropical cyclones using initialized ensembles and mesh refinement in E3SM
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/227; DOE-Office Of Science
One of the important applications of global climate models is to predict anticipated changes in the statistical properties of extreme events. Tropical cyclones are among the extreme events with the greatest socioeconomic impacts in the United States and other regions of the world. Landfalling hurricanes cause significant loss of property and life along the Atlantic and Pacific coasts of North America. Although coarse-resolution global climate models are incapable of simulating individual hurricanes accurately, they do exhibit significant skill in simulating the interannual and decadal variations in the aggregate statistics of hurricanes in the Atlantic basin, when provided the observed sea surface temperature as the boundary condition. We propose to analyze and validate simulated tropical cyclone activity in the Energy Exascale Earth System Model (E3SM), with a focus on tropical cyclones in the Northern Hemisphere. As global climate models approach horizontal spatial resolutions of 25km, their ability to simulate the statistical properties of tropical cyclones becomes an important validation metric. The U.S. CLIVAR Hurricane Working Group recently carried out an intercomparison of tropical cyclone characteristics as simulated by climate models and found that models are indeed able to reproduce the gross features of the geographical distribution of observed global tropical cyclone frequency. However, most models are not able to reproduce the detailed spatial structure of tropical cyclone tracks over the North Atlantic and other regions. In general, regionally-aggregated measures of tropical cyclone activity turn out to be much more predictable than local tropical cyclone occurrences. One of the challenges in simulating the spatial distribution of tropical cyclone track density in global climate models is the effect of climate bias. The genesis and evolution of tropical cyclones is quite sensitive to the large-scale background flow. For example, excessive vertical wind shear can inhibit the development of tropical cyclones. Since atmospheric flow 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 subgrid parameterizations or poor spatial resolution. To address this problem, we propose to use an initialized ensembles approach, where a series of 14-day hindcasts is carried out using the atmospheric component of E3SM. The integrations will be initialized from atmospheric reanalyses every 3 days over the decadal period 2000-2009. By construction, the background flow in these hindcasts will be close to observations. Comparing the statistics of tropical cyclone simulations in the initialized ensemble to that in the control runs will allow us to isolate the impact of mean flow biases. Errors in the representation of fine-scale orographic features in certain areas, such as the Central American Gap Wind region, can also lead to biases in the simulation of tropical cyclones. We propose to use a mesh-refinement approach to better represent orographic features in this region and study its impact on tropical cyclone activity.