Christoph A. Keller

Development of a grid-independent GEOS-Chem chemical transport model (v9-02) as an atmospheric chemistry module for Earth system models

The GEOS-Chem global chemical transport model (CTM), used by a large atmospheric chemistry research community, has been re-engineered to also serve as an atmospheric chemistry module for Earth system models (ESMs). This was done using an Earth System Modeling Framework (ESMF) interface that operates independently of the GEOSChem scientific code, permitting the exact same GEOSChem code to be used as an ESM module or as a standalone CTM. In this manner, the continual stream of updates contributed by the CTM user community is automatically passed on to the ESM module, which remains state of science and referenced to the latest version of the standard GEOS-Chem CTM. A major step in this re-engineering was to make GEOS-Chem grid independent, i.e., capable of using any geophysical grid specified at run time. GEOS-Chem data sockets were also created for communication between modules and with external ESM code. The grid-independent, ESMF-compatible GEOS-Chem is now the standard version of the GEOS-Chem CTM. It has been implemented as an atmospheric chemistry module into the NASA GEOS5 ESM. The coupled GEOS-5–GEOS-Chem system was tested for scalability and performance with a tropospheric oxidant-aerosol simulation (120 coupled species, 66 transported tracers) using 48–240 cores and message-passing interface (MPI) distributed-memory parallelization. Numerical experiments demonstrate that the GEOS-Chem chemistry module scales efficiently for the number of cores tested, with no degradation as the number of cores increases. Although inclusion of atmospheric chemistry in ESMs is computationally expensive, the excellent scalability of the chemistry module means that the relative cost goes down with increasing number of cores in a massively parallel environment.

Revisiting global fossil fuel and biofuel emissions of ethane

Recent measurements over the Northern Hemisphere indicate that the long-term decline in the atmospheric burden of ethane (C2H6) has ended and the abundance increased dramatically between 2010 and 2014. The rise in C2H6 atmospheric abundances has been attributed to oil and natural gas extraction in North America. Existing global C2H6 emission inventories are based on outdated activity maps that do not account for current oil and natural gas exploitation regions. We present an updated global C2H6 emission inventory based on 2010 satellite-derived CH4 fluxes with adjusted C2H6 emissions over the U.S. from the National Emission Inventory (NEI 2011). We contrast our global 2010 C2H6 emission inventory with one developed for 2001. The C2H6 difference between global anthropogenic emissions is subtle (7.9 versus 7.2u2009Tgu2009yr−1), but the spatial distribution of the emissions is distinct. In the 2010 C2H6 inventory, fossil fuel sources in the Northern Hemisphere represent half of global C2H6 emissions and 95% of global fossil fuel emissions. Over the U.S., unadjusted NEI 2011 C2H6 emissions produce mixing ratios that are 14–50% of those observed by aircraft observations (2008–2014). When the NEI 2011 C2H6 emission totals are scaled by a factor of 1.4, the Goddard Earth Observing System Chem model largely reproduces a regional suite of observations, with the exception of the central U.S., where it continues to underpredict observed mixing ratios in the lower troposphere. We estimate monthly mean contributions of fossil fuel C2H6 emissions to ozone and peroxyacetyl nitrate surface mixing ratios over North America of ~1% and ~8%, respectively.

Obstructions and lines of marginal stability from the world-sheet

The behaviour of supersymmetric D-branes under deformations of the closed string background is studied using world-sheet methods. We explain how lines of marginal stability and obstructions arise from this point of view. We also show why = 2 B-type branes may be obstructed against (cc) perturbations, but why such obstructions do not occur for = 4 superconformal branes at c = 6, i.e. for half-supersymmetric D-branes on K3. Our analysis is based on a field theory approach in superspace, as well as on techniques from perturbed conformal field theory.

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

Smoke aerosols have been observed in Southwest China as a result of long-range transport from surrounding areas in March and April. The processes driving this transport and the resultant impact on regional aerosol optical properties are studied here through a combined use of the Goddard Earth Observing System (GEOS)-Chem chemistry transport model in conjunction with satellite and the first-ever ground-based observations in the Southwest China. The potential biomass burning source regions as well as their respective contributions to aerosol loading in Southwest China are quantified. Compared to Sun photometer observations of aerosol optical depth (AOD) at 550u2009nm at eight stations in the study region (10–28°N, 90–115°E, comprising Northeast India, Indo-China Peninsula, and Southwest and South China), the AOD simulated by GEOS-Chem (nested grid with 0.5°u2009×u20090.667° resolution) by using the Fire Inventory from National Center for Atmospheric Research shows an average bias of −0.17 during January 2012 to May 2013. However, during the biomass burning months (March–April), the simulated AOD is much improved with a bias of −0.04. Model sensitivity experiments show that biomass burning in Burma and Northeast India is the largest contributor to smoke AOD (~88%) and total AOD (~57%) over Kunming, an urban site in Southwest China. Case studies on 21–23 March 2013 show that the smoke layer in Northeast India and North Burma can extend from the surface to 4u2009km and then be transported to Southwest China by prevailing westerly airflow. Model-simulated AOD and vertical distribution of aerosols are respectively in good agreement with satellite measurements from Moderate Resolution Imaging Spectroradiometer and Cloud-Aerosol Lidar with Orthogonal Polarization.

A sensitivity analysis of key natural factors in the modeled global acetone budget

Acetone is one of the most abundant carbonyl compounds in the atmosphere, and it serves as an important source of HOx (OHu2009+u2009HO2) radicals in the upper troposphere and a precursor for peroxyacetyl nitrate (PAN). We present a global sensitivity analysis targeted at several major natural source and sink terms in the global acetone budget to find the input factor or factors to which the simulated acetone mixing ratio was most sensitive. The ranges of input factors were taken from literature. We calculated the influence of these factors in terms of their Elementary Effects on model output. Of the six factors tested here, the four factors with the highest contribution to total global annual model sensitivity are direct emissions of acetone from the terrestrial biosphere, acetone loss to photolysis, the concentration of acetone in the ocean mixed layer, and the dry deposition of acetone to ice-free land. The direct emissions of acetone from the terrestrial biosphere are globally important in determining acetone mixing ratios but their importance varies seasonally outside the tropics. Photolysis is most influential in the upper troposphere. Additionally, the influence of the oceanic mixed layer concentrations are relatively invariant between seasons, compared to the other factors tested. Monoterpene oxidation in the troposphere, despite the significant uncertainties in acetone yield in this process, is responsible for only a small amount of model uncertainty in the budget analysis.

Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology

Global simulations of atmospheric chemistry are commonly conducted with off-line chemical transport models (CTMs) driven by archived meteorological data from general circulation models (GCMs). The off-line approach has advantages of simplicity and expediency, but incurs errors due to temporal averaging in the meteorological archive and the inability to reproduce the GCM transport algorithms exactly. The CTM simulation is also often conducted at coarser grid resolution than the parent GCM. Here we investigate this cascade of CTM errors by using 222Rn-210Pb-7Be chemical tracer simulations offline in the GEOS-Chem CTM at rectilinear 0.25° ×0.3125° (≈25 km) and 2° ×2.5° (≈200 km) resolutions, and on-line in the parent GEOS-5 GCM at cubed-sphere c360 (≈25 km) and c48 (≈200 km) horizontal resolutions. The c360 GEOS-5 GCM meteorological archive, updated every 3 hours and remapped to 0.25° ×0.3125°, is the standard operational product generated by the NASA Global Modeling and Assimilation Office (GMAO) and used as input by GEOS-Chem. We find that the GEOS-Chem 222Rn simulation at native 0.25° ×0.3125° resolution is affected by vertical transport errors of up to 20% relative to the GEOS-5 c360 on-line simulation, in part due to loss of transient organized vertical motions in the GCM (resolved convection) that are temporally averaged out in the 3-hour meteorological archive. There is also significant error caused by operational remapping of the meteorological archive from cubed-sphere to rectilinear grid. Decreasing the GEOS-Chem resolution from 0.25°×0.3125° to 2°×2.5° induces further weakening of vertical transport as transient vertical motions are averaged out spatially as well as temporally. The resulting 222Rn concentrations simulated by the coarse-resolution GEOS-Chem are overestimated by up to 40% in surface air relative to the on-line c360 simulations, and underestimated by up to 40% in the upper troposphere, while the tropospheric lifetimes of 210Pb and 7Be against aerosol deposition are affected by 5-10%. The lost vertical transport in the coarse-resolution GEOS-Chem simulation can be partly restored by re-computing the convective mass fluxes at the appropriate resolution to replace the archived convective mass fluxes, and by correcting for bias 20 in spatial averaging of boundary layer mixing depths.

Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model

Exposure to ambient fine particulate matter (PM2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM2.5 to interpret globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM2.5 composition varies substantially for secondary inorganic aerosols (2.4-19.7 μg/m3), mineral dust (1.9-14.7 μg/m3), residual/organic matter (2.1-40.2 μg/m3), and black carbon (1.0-7.3 μg/m3). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m3), industry (6.5 μg/m3), and power generation (5.6 μg/m3) are leading sources of outdoor global population-weighted PM2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM2.5 provides insight into sources and processes that influence the global spatial variation in PM2.5 composition.

A new method (M 3 Fusion-v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world’s largest collection of surface ozone metrics. Output from six models was provided by the participants of 5 the Chemistry-Climate Model Initiative (CCMI) and NASA’s Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly-spaced observations onto a fine resolution grid by using integrated nested Laplace approximations, and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in 10 each of 8 world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias-corrected within two degrees of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean. 1 Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-183 Manuscript under review for journal Geosci. Model Dev. Discussion started: 5 September 2018 c

GEOS-Chem High Performance (GCHP): A next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications

Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ∼ 100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here, we develop such a highperformance capability for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to operate in a distributed-memory framework for massive parallelization. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere– troposphere oxidant–aerosol chemistry at C180 spatial resolution (∼ 0.5× 0.625) or finer become routinely feasible.

Application of random forest regression to the calculation ofgas-phase chemistry within the GEOS-Chem chemistry model v10

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consists of one month (July 2013) of output of chemical 5 conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families 10 (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine learning driven GEOS-Chem model compares well to the standard simulation. For O3, errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalised mean bias (NMB), root mean square error (RMSE) and R are 4.2%, 35%, and 0.9, respectively; after 30 days the errors increase to 13%, 67%, and 0.75, 15 respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10% and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short lived nitrogen species errors become large, with NMB, RMSE and R reaching >2100% >400%, and <0.1, respectively. 20 This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations but optimisation and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations and its applicability to operational air quality activities.