The path to simplification of cloud microphysics.

    One of the main sources of uncertainty in climate modeling is microphysics. While Lagrangian approaches are promising, their computational cost is not yet suited to global climate simulations, leaving global simulations to bulk schemes. Starting from simple one-moment microphysics formulations that prognose only the specific mass of each hydrometeor 𝑞_x, physically richer approaches like double- and higher-moment bulk schemes were introduced to narrow model–observation gaps, but they deliver mixed, metric-dependent gains over simpler one-moment schemes [1,2]. This mixed record suggests that the link between microphysical processes and other atmospheric processes remains poorly understood.

    Few studies have probed this link directly [3], and the intricacy of bulk schemes has hindered clear attribution of changes in climate statistics to specific microphysical processes. Here we aim to make microphysics more transparent by simplifying it to a Kessler-like three-category scheme. We thus collapse Lin’s six-category scheme [4] to three prognosed variables: vapor 𝑞_v, condensates 𝑞_ci (cloud water 𝑞_c  + ice 𝑞_i ), and precipitates 𝑞_rgs  (rain 𝑞_r + graupel 𝑞_g + snow 𝑞_s), where their precise category is defined by local thermodynamics (temperature 𝑇 and relative humidity RH) and updraft velocity 𝑤 (Fig. 1). Using a global 5-km atmosphere-only ICON simulation with a Lin-like microphysics scheme, we ask whether accurate mappings 𝑓_x , 𝑓_y exist such that

    Condensates: 𝑞_x ≈ 𝑞_x* = 𝑓_x (𝑞_ci , 𝑇, RH, 𝑤),  for x ∈ {c, i} ,

    Precipitates:   𝑞_y ≈ 𝑞_y* = 𝑓_y (𝑞_rgs , 𝑇, RH, 𝑤), for y ∈ {r, g, s} ,

where 𝑞_x,y are the specific masses from our ICON simulation, and 𝑞^*_x,y  are the predicted specific masses from the partitioning functions 𝑓_x,y , given 𝑞_ci and 𝑞_rgs (see Fig. 1). We construct histograms in the (𝑇, 𝑤, RH)-space and fit simple partition functions, like sigmoids, to build our partitioning functions.

    We present here results from this mapping, as well as an evaluation of its performance. We measure the R², and we compare global instantaneous outputs from our ICON simulation to the predictions provided by our partitioning functions, such as LWP and IWP comparisons.

 

Fig.1: Collapse state of microphysics and its link to the predicted partitioning.

 

[1] Seiki, Kodama, Noda, Satoh, J. Climate, 28, 2405–2419 (2015).

[2] Song, Sunny Lim, Weather and Climate Extremes, 37, 2212-0947 (2022).

[3] Proske, Ferrachat, Neubauer, Staab, Lohmann, Atmos. Chem. Phys., 22, 4737–4762 (2022).

[4] Lin, Farley, Orville, J. Appl. Meteorol. Climatol., 22, 1065–1092 (1983).