Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)

Abstract

Abstract. We describe a new generation of the high-performance GEOS-Chem (GCHP) global model of atmospheric composition developed as part of the GEOS-Chem version 13 series. GEOS-Chem is an open-source grid-independent model that can be used online within a meteorological simulation or offline using archived meteorological data. GCHP is an offline implementation of GEOS-Chem driven by NASA Goddard Earth Observing System (GEOS) meteorological data for massively parallel simulations. Version 13 offers major advances in GCHP for ease of use, computational performance, versatility, resolution, and accuracy. Specific improvements include (i) stretched-grid capability for higher resolution in user-selected regions, (ii) more accurate transport with new native cubed-sphere GEOS meteorological archives including air mass fluxes at hourly temporal resolution with spatial resolution up to C720 (∼ 12 km), (iii) easier build with a build system generator (CMake) and a package manager (Spack), (iv) software containers to enable immediate model download and configuration on local computing clusters, (v) better parallelization to enable simulation on thousands of cores, and (vi) multi-node cloud capability. The C720 data are now part of the operational GEOS forward processing (GEOS-FP) output stream, and a C180 (∼ 50 km) consistent archive for 1998–present is now being generated as part of a new GEOS-IT data stream. Both of these data streams are continuously being archived by the GEOS-Chem Support Team for access by GCHP users. Directly using horizontal air mass fluxes rather than inferring from wind data significantly reduces global mean error in calculated surface pressure and vertical advection. A technical performance demonstration at C720 illustrates an attribute of high resolution with population-weighted tropospheric NO2 columns nearly twice those at a common resolution of 2∘ × 2.5∘.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

“…55,56 GCHP shares the same source code for physical and chemical mechanisms with the traditional implementation of GEOS-Chem (GEOS-Chem Classic or GCC) except for the FV3 advective algorithm in GCHP which is optimized for gnomonic cubed-sphere projection with higher scalability and efficiency. 29,57 In addition, GCHP simulations can be conducted with efficient parallelization over thousands of physical computing cores 30 including on the cloud, 33 thus enabling high-resolution simulations. In this study, we contrast the highest available cubed-sphere resolution at the start of this work of C360 (∼25 km) with a commonly used low resolution of C48 (∼200 km).…”

“…Prior global high-resolution studies have primarily focused on reactive trace gases ,, with fewer analyses on PM 2.5 . , Most global air quality simulations over the past two decades have been at 100 km or lower resolutions. ,, This study examines the resolution dependence of population exposure to PM 2.5 and its components with additional emphasis on NO 2 given its pronounced heterogeneity, focusing on the understudied Global South. Enabled by recent developments to a global open-source community model (GEOS-Chem) in its high-performance implementation (GCHP) including improved performance and ease of use, we conduct global simulations using its complex aerosol–oxidant mechanism for a full year at a cubed-sphere resolution of C360 (∼25 × 25 km 2 ), 64 times finer than a reference simulation at C48 (∼200 × 200 km 2 ). Discrepancies in population exposure to surface PM 2.5 , its components, and NO 2 are assessed between C48 and C360 simulations.…”

Large impact of coarse-resolution atmospheric transport model error on land-ocean and tropic-extratropic partitioning and seasonal cycle in CO2 inversion

“…Our finding indicates that the current satellite retrieval algorithm may not be as biased as previously argued, and increasing the resolution of ATM or improving the parameterization schemes of ATM should be placed at a high priority in order to derive a robust country-level carbon budget and reasonable ocean carbon cycle estimates. Recent efforts of speeding up ATMs using Graphics Processing Units (GPU) and Message Passing Interface (MPI) (Martin et al, 2022) Parallelization are ongoing that native resolution inversion is computationally possible in the coming years.…”

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

“…The most common approach to assess spatially resolved source contributions at national scales has been through the use of chemical transport models which offer the ability to represent both direct emissions and secondary formation of PM 2.5 from reactions between gaseous precursors. ,, Some studies have additionally employed satellite-derived estimates of ambient PM 2.5 concentrations to represent ambient concentrations and fine scale variations more accurately. , Prior studies have also applied positive matrix factorization (PMF) methods to evaluate local source contributions to PM 2.5. , Direct emissions from residential combustion, industry, power generation, agricultural waste burning, and windblown dust are major sources of PM 2.5 in India. ,, Recent progress has been made in applying chemical transport models to simulate secondary formation of PM 2.5 from reactions between primary gas-phase precursors and to represent the relation of sources with ambient PM 2.5 concentrations. ,,, We use the recently developed stretched grid capability of the GEOS-Chem chemical transport model in its high performance implementation , (GCHP), high-resolution hybrid satellite-derived PM 2.5 exposure estimates, and disease-specific concentration response functions from the Global Burden of Disease (GBD) to assess sector- and fuel-based contributions to PM 2.5 concentrations and attributable mortality in South Asia. We combine sensitivity simulations from GCHP with the high resolution satellite-derived PM 2.5 exposure estimates along with country and state-specific disease burden data for South Asia and India to provide a detailed assessment of the sector- and fuel-based sources of PM 2.5 mass, composition, and the resulting contributions to the attributable PM 2.5 disease burden for 6 regions (described by bold lines within India in Figure ), 29 states, and 1 Union Territory for India, and six surrounding South Asian countries.…”