The study presents a parameterization of (mostly) previously published detailed modeling of sulfuric-acid- dimethylamine (DMA) new particle formation (NPF) and a modeling study in which the parameterization is applied over Beijing and found to explain aerosol number concentrations there. The parameterization of the detailed models and the method of simulating DMA concentrations are interesting, and useful. The paper could be improved by making the statements in the text more quantitative. I recommend that the paper is within scope of ACP and will be suitable for publication if the following mainly minor comments can be addressed.

Major comments

1. Need to evaluate condensation sink (CS) timeseries in WRF-chem simulations to show that simulated 1nm aerosol concentrations aren’t right for the wrong reasons. Please discuss results with reference to how sensitive the NPF rates are to the CS (Figure 2). While some information might be gained from the banana plots, they are hard to interpret quantitatively, and the size distribution is averaged over the time-period, which might hide significant discrepancies and might also help explain issue in simulating 2-10nm-sized aerosols.

2. Authors need to follow ACP open data standards: https://www.atmospheric-chemistry-and-physics.net/policies/data_policy.html. It is no longer sufficient to say "data are available from the authors on request'. Post simulation output and all code and data needed to reproduce figures on a repository, at a minimum.

Minor comments

The introduction refers to much of the relevant literature on NPF parameterizations but does not review prior modeling work in Beijing. Also need to discuss complementary modeling of SA-DMA clustering by Liu et al 2021, https://www.pnas.org/doi/epdf/10.1073/pnas.2108384118

Is there any dependence of the SA-DMA NPF rates on relative humidity?

The evaporation rate of 3.33s-1 is different to the one in Kuerten et al (2018), 0.1s-1 and likely other studies. Why?

What about synergistic effects involving ammonia or nitric acid (Glasoe et al, 2015; Liu et al 2021) and what about the possible role of amines other than DMA or even malic acid (Liu et al, Phys Chem Chem Phys,  2022 10.1039/D2CP03551K).

Sections 3.1 and 3.2 are too brief and qualitative: need to quantify the biases in the parameterized J rates versus the KM and CDS models using normalized mean bias and R^2.

Section 3.3: what are the R^2 values between the measured and simulated SA and DMA concentrations? Daytime SA is usually simulated within a factor 2 of measurements, which looks good, and the measurement uncertainty is likely close to a factor of 2 if not higher, so you can’t be expected to do much better than this – but a factor of 2 in SA is a factor of 16 in SA-DMA nucleation if the power law is 4. Doesn’t this introduce an important uncertainty in the simulated aerosol number concentration?

Section 3.4: In Figure 6, it looks like only about half of the NPF events simulated actually happened. It would be good to put some more precise numbers on this in the text. And in Figure 4, there seem to be too many Aitken mode aerosols most of the time. Doesn’t this suggest the SA-DMA NPF mechanism is too strong? What could cause these biases?

How frequently were relevant diagnostic variables output from WRF-chem?

Figure 1: the dashed line represents a factor of four variation in a) and an order of magnitude in b). There is no grey dashed line for +/-50% variation.

Line 308/Figure S6: Dunne et al (2016) included SA-DMA nucleation in their model but did not present it as part of their main analysis. Was only SA-H2O and SA-NH3-H2O nucleation from CLOUD included in this comparison, or were all mechanisms included? Please clarify.

All the Supplementary figures should be explicitly referred to in the text, or removed – but it may be enough to change S9-S16 to S9-S18 on line 362.

Would be good to try to link sensitivity studies more closely to observed biases in the results – discuss how uncertainties in X could lead to biases in Y etc.

Numerous small grammatical mistakes, e.g. missing “the”, use of “largely” to mean “greatly”, new words such as “majorly”, consistency between singular and plural verb forms (e.g 'the clusters… is…') throughout should be fixed before publication.

Increasing evidences indicate that sulfuric acid (SA)-driven new particle formation (NPF) enhanced by dimethylamine (DMA) could be the key nucleation mechanism in the polluted urban atmosphere while it has not been well represented in chemical transport  models. This study provides a simplified dynamic DMA-SA nucleation parameterization with explicit consideration of the coagulation/condensation sink (CS). Combined with the comprehensive representation of sources and sinks of gaseous precursors, the updated WRF-Chem model well explains NPF events and particle number concentrations in wintertime for Beijing. The manuscript is well organized and the new nucleation modeling methodology has the potential to be widely used in other models. I recommend the publication of the manuscript in ACP if the following minor comments are addressed.

1 Both the nature of simplified parameterization and the results of 3-D modeling demonstrate a high dependence of nucleation rate and occurrence of NPF events on CS. Hence, the authors need to evaluate the model performance on CS simulation.

2 In section 3.1, “characteristic equilibrium time” and “data collection time interval” are used to test the reasonability of pseudo-steady-state assumptions in SA-DMA parameterization. A more specific description of these terms is suggested for better readability.

3 Various parameterizations of DMA-SA nucleation are reviewed in the introduction, however, previous 3-D modeling works concerning NPF in Beijing are not mentioned.

4 Some molecular modeling works show that the growth pathways from precursor molecules to ~1.4 nm particles may be altered with changing precursor concentration and atmospheric conditions (T and CS). The underlying uncertainty on growth pathways should be discussed since a fixed one is used under all conditions.