6.4.2. TEM Model

The input variables used to drive DVMDOSTEM include: drainage classification (upland or lowland), CMT classification, topography (slope, aspect, elevation), soil texture (percent sand, silt, and clay), climate (air temperature, precipitation, vapor pressure, incoming shortwave radiation), atmospheric CO2 concentration, and fire occurrence (date and severity). All input datasets are spatially explicit, except the time series of atmospheric CO2.

When running TEM, these inputs must be provided and can be used to simulate different scenarios to answer specific questions about how the model system responds to changes in climate and disturbance regimes.

In order to create different scenarios with TEM that allow us to investigate how changes in temperature and precipitation impact snow, vegetation, and permafrost, we can modify the input climate data files used by TEM through the combination of two different functions:

• modify_air_temperature.py, which we used in the previous example and which allows us to modify air temperature by a specified offset (in °C) for specific months (e.g., summer or winter months).

• modify_precipitation_percent.py, which allows us to modify precipitation by a specified percentage for specific months.

In addition to the changes to the input climate data files, we can also alter the community type that we look at, which determines the plant functional types (PFTs) that are present in the model simulation. For the model site that we are looking at in this example (Imnavait creek), the three community types that we can select from are:

• Tussock tundra (contains the PFTs betula shrubs, other deciduous shrubs, evergreen shrubs, sedges, forbs, lichen, feathermoss, sphagnum moss)

• Wet sedge tundra (contains the PFTs deciduous shrubs, sedges, grasses, forbs, lichen, feathermoss, sphagnum moss)

• Heath tundra (contains the PFTs deciduous shrubs, evergreen shrubs, forbs, lichen, grasses, moss)

Within each community type, the PFTs have different characteristics that influence how they interact with snow and permafrost processes, as well as capability for dynamic changes over time including competition for light and nutrients. For more information, see the TEM background for Snow, Vegetation, and Permafrost section at the end of this document.

Running TEM Cases

General model setup

If you have not already done so, follow the following instructions to set up your environment for running TEM:

In the shell, start a model container:

docker run -it --rm \
   --name models-container \
   -v $(pwd):/home/modex_user \
   -v inputdata:/mnt/inputdata \
   -v output:/mnt/output \
   yuanfornl/ngee-arctic-modex26:models-main-latest /bin/bash

Look at the available input data and check that the Imnavait data is available:

modex_user@40bc0d780707:~$ ls -1 /mnt/inputdata/TEM/
cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10
cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Kougarok_10x10
cru-ts40_ar5_rcp85_ncar-ccsm4_EML_study_area_10x10
cru-ts40_ar5_rcp85_ncar-ccsm4_KUPARUK_10x10
cru-ts40_ar5_rcp85_ncar-ccsm4_toolik_field_station_10x10
modex26_1x1_AK-UTQ

Set up the run folders

With the available functions, you can create different TEM scenarios by modifying the input climate data files. Below are the instructions to set up the identical scenarios to the ELM examples shown previously.

• Baseline run

• Temperature scaled by +3 °C

• Precipitation scaling (rain +40%, snow +60%)

• Combined temperature and precipitation scaling

You will need to create a separate run folder for each scenario you want to run:

mkdir -p /mnt/output/tem/tem_ee2_breakout
cd /mnt/output/tem/tem_ee2_breakout

pyddt-swd \
   --input-data-path /mnt/inputdata/TEM/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10 \
   --copy-inputs \
   --force \
   baseline_imnavait_tussock

pyddt-swd \
   --input-data-path /mnt/inputdata/TEM/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10 \
   --copy-inputs \
   --force \
   warming_imnavait_tussock

 pyddt-swd \
   --input-data-path /mnt/inputdata/TEM/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10 \
   --copy-inputs \
   --force \
   precip_imnavait_tussock

 pyddt-swd \
   --input-data-path /mnt/inputdata/TEM/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10 \
   --copy-inputs \
   --force \
   warming_and_precip_imnavait_tussock

You can run each of the scenarios for the three different community types available for the Imnavait site described above, by replacing “tussock” in the run folder names with “wetsedge” or “heath”. We run through the examples using tussock tundra but will note where to change for the other community types.

Running the model

Running the different run folders involves several steps, many of which are identical across the scenarios. However, some steps to adjusting the input climate data files differ. Below are the instructions for each scenario.

Baseline run:

1. Change into the run folder:

   cd /mnt/output/tem/tem_ee2_breakout/baseline_imnavait_tussock
   

2. Adjust the run mask:

   pyddt-runmask --reset --yx 0 0 inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/run-mask.nc
   

3. Setup the output specification file

   pyddt-outspec config/output_spec.csv --on GPP m p
   pyddt-outspec config/output_spec.csv --on LAYERDZ m l
   pyddt-outspec config/output_spec.csv --on TLAYER m l
   pyddt-outspec config/output_spec.csv --on SWE m
   pyddt-outspec config/output_spec.csv --on SNOWTHICK m
   pyddt-outspec config/output_spec.csv --on ALD y
   pyddt-outspec config/output_spec.csv --on CMTNUM y
   

4. Start the run.

   dvmdostem -f config/config.js --force-cmt 5 -p 100 -e 1000 -s 250 -t 115 -n 0 -l monitor
   

   Note

   Inputs, length of data

   If you are using different input data, you might need to adjust the number of transient years to run (the -t flag). For the older generation of input data (cru-ts40_ar5_*_10x10/) there are 115 years of transient data, ending in 2015. For the newer files (modex26_1x1_*/), there are 123 years of transient data to run, ending in 2023.

   Check size of input files.

   There are many ways to check on the size and shape of a NetCDF file, here we will use the command line tool ncdump.

   ncdump -h path/to/your/input_file.nc
   

   This will print out the header to the NetCDF file. If you look at the printed output, you should be able to find the length of the time axis. For valid TEM input files, this will be the number of months in the file.

   Using Python’s xarray is also a good option:

   python -c "import xarray as xr; print(xr.open_dataset('path/to/your/historic-climate.nc').sizes)"
   

   Note that the command line options for running TEM require the number of years!

   Note

   Forcing the community type

   If you would like to ignore the input vegetation map (vegetation.nc) and use a particular community type (CMT) for the run, you can use the --force-cmt flag. --force-cmt 5 is for tussock tundra. For wet sedge tundra, use --force-cmt 6, and for heath tundra, use --force-cmt 7.

   Listing available CMTs

   If you would like to see a list of all CMTs that are available in your run’s parameter directory, you can run this command:

   pyddt-pyddt-param --report-all-cmts parameters/
   

Temperature scaled by +3 °C:

1. Change into the run folder:

   cd /mnt/output/tem/tem_ee2_breakout/warming_imnavait_tussock
   

2. Adjust the input climate data:

   python /home/modex_user/model_examples/TEM/modify_air_temperature.py \
     --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
     --months 6 7 8 9 \
     --years 2010 2011 2012 \
     --deviation 3
   

3. Replace the old input climate data file with the modified one:

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

4. Adjust the run mask:

   pyddt-runmask --reset --yx 0 0 inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/run-mask.nc
   

5. Setup the output specification file

   pyddt-outspec config/output_spec.csv --on GPP m p
   pyddt-outspec config/output_spec.csv --on LAYERDZ m l
   pyddt-outspec config/output_spec.csv --on TLAYER m l
   pyddt-outspec config/output_spec.csv --on SWE m
   pyddt-outspec config/output_spec.csv --on SNOWTHICK m
   pyddt-outspec config/output_spec.csv --on ALD y
   pyddt-outspec config/output_spec.csv --on CMTNUM y
   

6. Start the run.

   dvmdostem -f config/config.js --force-cmt 5 -p 100 -e 1000 -s 250 -t 115 -n 0 -l monitor
   

Precipitation scaling (rain +40%, snow +60%):

1. Change into the run folder:

   cd /mnt/output/tem/tem_ee2_breakout/precip_imnavait_tussock
   

2. Adjust the input climate data with the modify_precipitation_percent.py script. Since in TEM, precipitation falls as rain or snow depending on the average monthly air temperature, we will modify rain and snow separately assuming that rain occurs in the summer months (June, July, August, September) and snow in the winter months (October, November, December, January, February, March, April, May).

   python /home/modex_user/model_examples/TEM/modify_precipitation.py \
      --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
      --months 6 7 8 9 \
      --years 2010 2011 2012 \
      --deviation 0.4
   

   Move the modified into the original file.

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

   And run the second modification, followed by the mv command again.

   python /home/modex_user/model_examples/TEM/modify_precipitation.py \
      --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
      --months 10 11 12 1 2 3 4 5 \
      --years 2010 2011 2012 \
      --deviation 0.6
   

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

3. Adjust the run mask:

   pyddt-runmask --reset --yx 0 0 inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/run-mask.nc
   

4. Setup the output specification file

   pyddt-outspec config/output_spec.csv --on GPP m p
   pyddt-outspec config/output_spec.csv --on LAYERDZ m l
   pyddt-outspec config/output_spec.csv --on TLAYER m l
   pyddt-outspec config/output_spec.csv --on SWE m
   pyddt-outspec config/output_spec.csv --on SNOWTHICK m
   pyddt-outspec config/output_spec.csv --on ALD y
   pyddt-outspec config/output_spec.csv --on CMTNUM y
   

5. Start the run.

   dvmdostem -f config/config.js --force-cmt 5 -p 100 -e 1000 -s 250 -t 115 -n 0 -l monitor
   

Combined temperature and precipitation scaling:

1. Change into the run folder:

   cd /mnt/output/tem/tem_ee2_breakout/warming_and_precip_imnavait_tussock
   

2. Adjust the input climate data

   python /home/modex_user/model_examples/TEM/modify_precipitation.py \
      --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
      --months 6 7 8 9 \
      --years 2010 2011 2012 \
      --deviation 0.4
   

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

   python /home/modex_user/model_examples/TEM/modify_precipitation.py \
      --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
      --months 10 11 12 1 2 3 4 5 \
      --years 2010 2011 2012 \
      --deviation 0.6
   

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

   python /home/modex_user/model_examples/TEM/modify_air_temperature.py \
      --input-file inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc \
      --months 6 7 8 9 \
      --years 2010 2011 2012 \
      --deviation 3
   

   mv inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/modified_historic-climate.nc \
      inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/historic-climate.nc
   

3. Adjust the runmask:

   pyddt-runmask --reset --yx 0 0 inputs/cru-ts40_ar5_rcp85_ncar-ccsm4_CALM_Imnavait_Creek_MAT_10x10/run-mask.nc
   

4. Setup the output specification file

   pyddt-outspec config/output_spec.csv --on GPP m p
   pyddt-outspec config/output_spec.csv --on LAYERDZ m l
   pyddt-outspec config/output_spec.csv --on TLAYER m l
   pyddt-outspec config/output_spec.csv --on SWE m
   pyddt-outspec config/output_spec.csv --on SNOWTHICK m
   pyddt-outspec config/output_spec.csv --on ALD y
   pyddt-outspec config/output_spec.csv --on CMTNUM y
   

5. Start the run.

   dvmdostem -f config/config.js --force-cmt 5 -p 100 -e 1000 -s 250 -t 115 -n 0 -l monitor
   

Looking at TEM Results

For the analysis portion of this experiment, we will use a Jupyter Notebook to visualize and compare the outputs from the control and warming runs. This allows us to interactively explore the data and create plots to see how the warming treatment affected the soil temperatures.

To start this notebook, use another terminal to launch a Jupyter Notebook server in a new container:

docker run -it --rm \
   --name vis-container \
   -p 8888:8888 \
   -v $(pwd):/home/jovyan \
   -v inputdata:/mnt/inputdata \
   -v output:/mnt/output \
   yuanfornl/ngee-arctic-modex26:vis-main-latest

You should see output like this:

[I 2025-12-12 21:31:22.299 ServerApp] jupyter_lsp | extension was successfully linked.
[I 2025-12-12 21:31:22.305 ServerApp] jupyter_server_terminals | extension was successfully linked.
[I 2025-12-12 21:31:22.311 ServerApp] jupyterlab | extension was successfully linked.
[I 2025-12-12 21:31:22.317 ServerApp] notebook | extension was successfully linked.
...
    To access the server, open this file in a browser:
     file:///home/jovyan/.local/share/jupyter/runtime/jpserver-360-open.html
 Or copy and paste one of these URLs:
     http://2aecc7b15434:8888/tree?token=e66b3be47f2b3d7721adeb88992b8b1818901c5d76281678
     http://127.0.0.1:8888/tree?token=e66b3be47f2b3d7721adeb88992b8b1818901c5d76281678

Once the server is running, you should see a URL printed out in the terminal that you can open in your web browser on your host computer to access the Jupyter interface. Navigate to the vis_notebooks/TEM/ directory and open the precipitation_plotting.ipynb notebook.

Note

TODO: plots: 2005-15 (capture pre and post warming)

Note

TODO: GPP might have to shorten…to see right

Advanced TEM Processing and Information

TEM background for Snow, Vegetation, and Permafrost

Each DVMDOSTEM grid cell can be assigned one “community type” (CMT). A community type is essentially a parameterization that specifies many properties for vegetation, and soil.

Each vegetation CMT (e.g. “wet-sedge tundra”, “white spruce forest”, etc.), is modeled with up to ten PFTs (e.g., “deciduous shrubs”, “sedges”, “mosses”), each of which may have up to three compartments: leaf, stem, and root. Vegetation carbon and nitrogen fluxes are calculated at each time step based on environmental factors and soil properties. Assimilation of atmospheric CO2 by the vegetation is estimated by computing gross primary productivity (GPP) for each PFT. GPP is a function of foliage development (seasonal and successional patterns), air and soil temperature, water and nutrient availability, photosynthetically active radiation, and maximum assimilation rate (a calibrated parameter) (McGuire et al., 1992; Euskirchen et al., 2009). Changes in vegetation carbon stocks are calculated using GPP, autotrophic respiration (Ra), and litter-fall (transfer from vegetation to soil). Vegetation nitrogen stocks are calculated using plant nitrogen uptake and litter-fall. Vegetation carbon and nitrogen soil stocks may also be modified as a result of wildfire burn.

The soil column is structured as a sequence of layers organized by soil horizons (i.e. fibric, humic, mineral, and parent material). The number and physical properties of layers may change throughout the simulation based on vegetation, thermal, hydrologic, and seasonal properties that are calculated at each time step (Zhuang et al., 2003; Euskirchen et al., 2014; Yi et al., 2009; McGuire et al., 2018). The model uses the two-directional Stefan algorithm to predict freezing/thawing fronts and the Richards equation to predict soil moisture dynamics in the unfrozen layers (Yi et al., 2009; Yi et al., 2010; Zhuang et al., 2003). Snow is also represented with a dynamic stack of layers. The physical properties of the snowpack (density, thickness, and temperature) are calculated from snowfall, sublimation and snowmelt. Snow cover influences soil-thermal and hydrological seasonal dynamics. Changes in soil carbon stocks are a result of litter-fall from the vegetation and decomposition of soil carbon stocks by microbes (heterotrophic respiration or Rh). Changes in soil organic and available nitrogen stocks are a result of litter-fall, net mineralization of organic nitrogen, and plant nitrogen uptake. Soil organic layers and soil carbon and nitrogen stocks may also be modified due to wildfire.

A.D. McGuire, J. M. Melillo, L. A. Joyce, D. W. Kicklighter, A. L. Grace, B. Moore, and C. J. Vorosmarty. Interactions between carbon and nitrogen dynamics in estimating net primary productivity for potential vegetation in North America. Global Biogeochemical Cycles, 6:101–124, 1992. doi:10.1029/92GB00219.

E.S. Euskirchen, A. D. Mcguire, F. S. Chapin, S. Yi, and C. C. Thompson. Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: implications for climate feedbacks. Ecological Applications, 19:1022–1043, 7 2009. doi:10.1890/08-0806.1.

Q.Zhuang, A. D. McGuire, K. P. O’Neill, J. W. Harden, V. E. Romanovsky, and J. Yarie. Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska. Journal of Geophysical Research: Atmospheres, 1 2003. doi:10.1029/2001JD001244.

E.S. Euskirchen, C. W. Edgar, M. R. Turetsky, M. P. Waldrop, and J. W. Harden. Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost. Journal of Geophysical Research: Biogeosciences, 119:1576–1595, 8 2014. doi:10.1002/2014JG002683.

Shuhua Yi, A. David McGuire, Jennifer Harden, Eric Kasischke, Kristen Manies, Larry Hinzman, Anna Liljedahl, Jim Randerson, Heping Liu, Vladimir Romanovsky, Sergei Marchenko, and Yongwon Kim. Interactions between soil thermal and hydrological dynamics in the response of Alaska ecosystems to fire disturbance. Journal of Geophysical Research: Biogeosciences, 6 2009. doi:10.1029/2008JG000841.

A.D. McGuire, Hélène Genet, Zhou Lyu, Neal Pastick, Sarah Stackpoole, Richard Birdsey, David D’Amore, Yujie He, T. Scott Rupp, Robert Striegl, Bruce K. Wylie, Xiaoping Zhou, Qianlai Zhuang, and Zhiliang Zhu. Assessing historical and projected carbon balance of Alaska: a synthesis of results and policy/management implications. Ecological Applications, 28:1396–1412, 9 2018. doi:10.1002/EAP.1768.

Shuhua Yi, A. David McGuire, Eric Kasischke, Jennifer Harden, Kristen Manies, Michelle MacK, and Merritt Turetsky. A dynamic organic soil biogeochemical model for simulating the effects of wildfire on soil environmental conditions and carbon dynamics of black spruce forests. Journal of Geophysical Research: Biogeosciences, 12 2010. doi:10.1029/2010JG001302.