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Abstract Sensitivity analysis with atmospheric chemical transport models may be used to quantify influences of specific emissions on pollutant concentrations. This information facilitates efficient environmental decision‐making regarding emissions control strategies for pollutants that affect human health and public welfare. The multicomplex step method (MCX) is a sensitivity analysis approach that enables calculation of first‐ and higher‐order sensitivities of a nonlinear algorithm with analytical accuracy. Compared to the well‐known finite difference method, the MCX method is also straight‐forward to compute yet does not suffer from precision errors due to subtracting numbers with common leading digits and eliminates the requirement of tuning the step size. The aerosol inorganic equilibrium thermodynamic model, ISORROPIA, which treats ammonium, chloride, nitrate, sodium, sulfate, calcium, potassium, and magnesium, was augmented to leverage the multicomplex step method (ISORROPIA‐MCX) to analyze the influence that the total amount of a pollutant has on concentrations partitioned into different phases. This enables simultaneous calculation of the first‐order, second‐order, and cross‐sensitivity terms in the Taylor Series expansion when evaluating the impact of changes in input parameters on an output variable, increasing the accuracy of the estimated effect when the functions are nonlinear. ISORROPIA encodes highly nonlinear processes which showcases the computational advantages of the multicomplex step method as well as the limitations of the approach for fractured solution surfaces. With ISORROPIA‐MCX, the influence of total concentrations of aerosol precursors on aerosol acidity are evaluated with cross‐sensitivity terms for the first time. 

Abstract. Sensitivity analysis in chemical transport models quantifies the response of output variables to changes in input parameters. This information is valuable for researchers engaged in data assimilation and model development. Additionally, environmental decision-makers depend upon these expected responses of concentrations to emissions when designing and justifying air pollution control strategies. Existing sensitivity analysis methods include the finite-difference method, the direct decoupled method (DDM), the complex variable method, and the adjoint method. These methods are either prone to significant numerical errors when applied to nonlinear models with complex components (e.g. finite difference and complex step methods) or difficult to maintain when the original model is updated (e.g. direct decoupled and adjoint methods). Here, we present the implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 as CMAQ-hyd. CMAQ-hyd can be applied to compute numerically exact first- and second-order sensitivities of species concentrations with respect to emissions or concentrations. Compared to CMAQ-DDM and CMAQ-adjoint, CMAQ-hyd is more straightforward to update and maintain, while it remains free of subtractive cancellation and truncation errors, just as those augmented models do. To evaluate the accuracy of the implementation, the sensitivities computed by CMAQ-hyd are compared with those calculated with other traditional methods or a hybrid of the traditional and advanced methods. We demonstrate the capability of CMAQ-hyd with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism in CMAQ. We also explore the cross-sensitivity of monoterpene nitrate aerosol formation to its anthropogenic and biogenic precursors to show the additional sensitivity information computed by CMAQ-hyd. Compared with the traditional finite difference method, CMAQ-hyd consumes fewer computational resources when the same sensitivity coefficients are calculated. This novel method implemented in CMAQ is also computationally competitive with other existing methods and could be further optimized to reduce memory and computational time overheads.