At temperatures between 0 deg C and -38 deg C clouds may contain both liquid drops and ice crystals. These clouds are referred to as mixed-phase clouds and are a significant source of uncertainty in weather and climate. Weather and climate models struggle to correctly simulate the number of ice crystals in them and the behaviour of the clouds strongly depends on getting this correct. More detailed modelling with bin-microphysics can help better understand the processes in detail. At mixed-phase cloud temperatures, ice crystals can form due to primary nucleation on so-called 'ice-nucleating particles' (INPs); or they can form due to 'secondary ice processes' (SIPs). At the University of Manchester, using laboratory measurements, we have recently identified a new SIP mechanism that has the potential to be of great importance in mixed-phase clouds. In this project you will use a cloud parcel model with bin-microphysics to study the effects of aerosols, primary nucleation and secondary ice processes in a range of mixed-phase cloud conditions to understand the importance of the different mechanisms on the formation and development of mixed-phase clouds.
The code repository for the Bin Microphysics Model is available at https://github.com/UoM-maul1609/bin-microphysics-model. This model is written in Fortran and has some associated Python scripts for analysis and plotting.
Prof. Paul Connolly will start up the VM, give you your username, SSH key (which is a file) and the IP address of the server (VM). To login you need to open CMD (on windows), terminal if on a mac or Chromebook. You would then type:
ssh -i id_virtual_students.key -X <username>@<IP-address>
The first time you log in you may need to answer 'yes' to a question about connecting.
Once logged in you will be interacting the VM in the same way as you would a linux server through the terminal. To download the bin microphysics model type:
git clone https://github.com/UoM-maul1609/bin-microphysics-model
This should download the code to the folder bin-microphysics-model
You need to be in the bin-microphysics-model folder to compile the code. Type
cd bin-microphysics-model
in the terminal / cmd window to enter the folder.
We need to then tell the computer to build the machine code executable that the computer CPU understands. This can be done by typing:
make NETCDFLIB=-L/usr/lib/x86_64-linux-gnu/ NETCDFMOD=/usr/include/ FFLAGS='-O2 -w -o' FFLAGS2='-O2 -w -o' FFLAGSOMP='-O2 -w -o'
The make command tells the computer to use a file called Makefile which is in the bin-microphysics-model folder you downloaded. In this file are rules for compiling the code. There are some additional variables that we pass into the Makefile. These are NETCDFLIB, NETCDFMOD, FFLAGS, FFLAGS2 and FFLAGSOMP, which overrides the values they are set to in the Makefile. They need to be altered for our environment.
You can run the model after it has been compiled by typing
./run.sh python/namelist-sip.in
Note that python/namelist-sip.in is the input file for the model, and includes initial values and processes that the model will consider.
This generates a file called /tmp/<username>/output1.nc, which is a so-called NetCDF formatted file containing the model output data.
We can use Python to look at the output. There is an example script at python/example_plot_bmm.py. This can be edited to output different variables from the NetCDF file. To plot the output file at /tmp/<username>/output1.nc you can type:
python3 python/example_plot_bmm.py
This will generate a file at /tmp/<username>/Test.png
How to we obtain the output file from inside the container, so that we can view it or insert it in a report?
We can open up another CMD, or terminal window and connect with SFTP (Secure File Transfer Protocol).
From another terminal or CMD window type
sftp -i id_virtual_students.key <username>@<IP-address>
This will log you into the VM. You can bring the file over to your local system by typing
get /tmp/<username>/Test.png
And then you will be able to view it in the usual way. By default it will be transferred to the folder that you were in before you logged in with SSH.
A neat way of understanding how different factors impact precipitation on the ground is the Factorial Method. See https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD008960
This has been coded up for you for certain factors, but we will discuss factors for you to look at in your project.
To run the factorial analysis code you can run the model using the batchRuns.py script, which is in the python folder inside the repository. There are 6 factors by default and each factor has two choices, so this is /tmp/<username>. To go into the python folder enter:
cd python
Then to run the code to batch process these factors type:
python3 batchRuns.py
In order to analyse these results we can use the factorialMethod.py script. From inside the python directory, enter this command:
python3 factorialMethod.py
The effect of each factor will be printed to the screen as
Effect of x,y,z
[[ 43738.74993496]
[-53935.79485368]
[ 53299.64164086]
[ -539.24981414]
[ 48381.34649661]
[ 58825.67145921]]
Interactions
t aer hm br m1 m2
0 NaN -11593.413864 52866.128636 600.707490 25598.785694 -8086.938432
1 -11593.413864 NaN 3021.430069 906.641322 15156.672129 -42487.089173
2 52866.128636 3021.430069 NaN -59.654391 34162.304535 -15403.547601
3 600.707490 906.641322 -59.654391 NaN -570.074529 -549.083881
4 25598.785694 15156.672129 34162.304535 -570.074529 NaN 12486.423234
5 -8086.938432 -42487.089173 -15403.547601 -549.083881 12486.423234 NaN
The 'effect' is just the effect of each factor we are investigating on the maximum ice crystal number concentration in the cloud.
Whereas the interactions are the 'non-linear' interactions between factors. For example turning on the HM process (the 3rd factor) actually increases the ice in the cloud in this case. Why would that be? Turning on the Mode-2 raindrop-freezing process (6th factor) gives more ice too. These are secondary ice mechanisms, so this is expected.
But making the turning on the HM process AND turning on the Mode-2 process has a negative interaction term (last row or the HM column in the table). This means that when both processes act together we get less ice than the sum of them individually. This is perhaps not too surprising, as there is a limit to the amount of ice we can have in the cloud.
These are defined in the runsDefine.py file. This file has a list called runToDo. Each element in the list is also a list of length 2. The first element (in each length-2 list) is the default case (in the base input file, python/namelist-sip.in) and the second element is the comparison that you are making for that factor.
Note, if you would like to look into different aerosol distributions you may wish to use James et al (2023). They reference a paper by Crooks et al. (2018) in their Table 3. This paper also uses the same model suggested in this project.
You could also look into incorporating aerosol from observations. For example, aerosol observations from the DC-Mex project have been put in the file data/Model_inputs_DCMEX.xlsx. Read more under the Aerosol properties Section.
The mode-2 process was investigated at the University of Manchester and a paper was written about it (James et al., 2021).
Note, a pre-print of a paper about a recent project is in the Earth System Science Data journal, Finney et al. (2023).
The aerosol in the file data/Model_inputs_DCMEX.xlsx are from the DCMEX campaign. It includes the size distributions (dN/dlnDp vs. Dp) from SMPS measurement, the calculated bulk kappa value and the density from AMS+SP2 measurements. It also includes the initial temp, pressure, and RH.
3-mode fits have been plotted in the file data/Model_inputs_DCMEX.xlsx and these can be used in the model. n_aer values need to be multiplied by 1e6 and d_aer values need to be multiplied by 1e-6 before putting into the namelist. The model fits were done by using the equation for
From the spreadsheet you can see that kappa values are given. This it a way of calculating the equililbrium vapour pressure of the aerosol particles following Petters and Kriedenweis (2007). To use this method in the model set kappa_flag=1 in the python/namelist-sip.in file and set the first element of kappa_core1 to the value in the spreadsheet. Density values are also given in the spreadsheet. These can be set in the python/namelist-sip.in file by changing the first element of density_core1.
- Teller and Levin, Factorial method as a tool for estimating the relative contribution to precipitation of cloud microphysical processes and environmental conditions: Method and application, 2008, https://doi.org/10.1029/2007JD008960
- James, R. L., Phillips, V. T. J., and Connolly, P. J.: Secondary ice production during the break-up of freezing water drops on impact with ice particles, Atmos. Chem. Phys., 21, 18519–18530, https://doi.org/10.5194/acp-21-18519-2021, 2021.
- James, R. L., Crosier, J., and Connolly, P. J.: A bin microphysics parcel model investigation of secondary ice formation in an idealised shallow convective cloud, Atmos. Chem. Phys., 23, 9099–9121, https://doi.org/10.5194/acp-23-9099-2023, 2023
- Finney, D. L., Blyth, A. M., Gallagher, M., Wu, H., Nott, G., Biggerstaff, M., Sonnenfeld, R. G., Daily, M., Walker, D., Dufton, D., Bower, K., Boeing, S., Choularton, T., Crosier, J., Groves, J., Field, P. R., Coe, H., Murray, B. J., Lloyd, G., Marsden, N. A., Flynn, M., Hu, K., Thamban, N. M., Williams, P. I., McQuaid, J. B., Robinson, J., Carrie, G., Moore, R., Aulich, G., Burton, R. R., and Connolly, P. J.: DCMEX coordinated aircraft and ground observations: Microphysics, aerosol and dynamics during cumulonimbus development, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-303, in review, 2023
- Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007
- Connolly, P. J., Topping, D. O., Malavelle, F., and McFiggans, G.: A parameterisation for the activation of cloud drops including the effects of semi-volatile organics, Atmos. Chem. Phys., 14, 2289–2302, https://doi.org/10.5194/acp-14-2289-2014, 2014.