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Chemistry in CESM-SE: Evaluation, Performance, and Optimization

Funding Program Area(s)
Project Type
University Grant
Project Team

Principal Investigator

Project Status

Chemistry is an essential component of climate as it defines the distribution of radiatively active chemical species, black carbon and nitrogen depositions and cloud‐aerosol interactions. The uncertainty in the aerosol indirect effect(s) from anthropogenic emissions since pre‐industrial times is quite large, and a significant fraction of this uncertainty is related to our incomplete knowledge and understanding of secondary‐organic aerosol formation processes. In particular, recent research has indicated a potentially strong interaction between pollution plumes and formation of secondary organic aerosols during the complex oxidation of biogenic non‐methane hydrocarbons. All this points to the specific need for a well‐tested and highly computationally‐efficient chemistry in Earth System models. We are therefore proposing to systematically test and evaluate the representation of chemistry and tracer transport in the Community Earth System Model (CESM) in which the new Spectral Elements (SE) dynamical core is implemented. Indeed, recent experiments have shown extremely good scalability of CESM‐SE on many processors (up to 180,000), making it an ideal tool for the upcoming exascale computing platforms. We propose to compare CESM‐SE with the standard Finite Volume dynamical core CESM using simulations with increasing flow and chemistry complexities. We will document and measure differences between simulations using tracers and newly designed mixing diagnostics. This will provide the University and DOE communities with a fully tested and evaluated chemistry in CESM‐SE.

Secondly, we propose to develop and evaluate chemistry optimization using Graphical Processing Units (GPUs) to expand its computational efficiency. In particular, we will evaluate if this additional computational capability could lead to implementing faster and more accurate chemistry solvers. Thirdly, we propose to use the subgrid‐scale information available from the polynomial representation in CESM‐SE. Such information can indeed be used to go beyond the present modeling of chemistry using grid‐averaged quantities only and include sub‐grid spatial correlation (or lack thereof) between chemical species. This representation of segregation could significantly affect chemistry simulations in regions of strong gradient and mixing. This proposal will provide the CESM user community with 1) the full knowledge of the computational and scientific performance of CESM‐SE in terms of tracer transport and chemistry, 2) the ability to take advantage of GPU acceleration, 3) the availability of improved chemistry solvers and 4) the representation of subgrid‐scale variability and its impact on chemical reaction rates. Such fully evaluated CESM‐SE with chemistry will be the ideal tool to expand our understanding of chemistry‐climate interactions through increase in resolution and/or complexity in chemistry. Our proposal is strongly aligned with the BER ESM program goals as it will "improve the accuracy and skill of climate models by implementing enhanced ESM components, such as improved parameterizations for clouds, aerosols and chemistry…". Overall, the proposed topics require the continuous collaboration between atmospheric scientists, numerical methods experts and computational scientists. Finally, our proposal strongly leverages existing research programs funded by the Department of Energy on atmospheric chemistry in CESM (National Center for Atmospheric Research ), on SE (Sandia National Laboratory and Oak Ridge National Laboratory), on GPU optimization (Oak Ridge National Laboratory and Cray Research) and on observational field campaigns (Pacific Northwest National Laboratory). The proposed team is ideal to successfully perform the tasks necessary to provide the DOE and University communities with a thoroughly tested and highly‐optimized chemistry in CESM‐SE. This tool will be ready for efficiently using the upcoming exascale computing platforms to advance the fundamental science of climate change.