ATM 623: Climate Modeling

Brian E. J. Rose, University at Albany

Climate sensivity and the energy budget in CESM

About these notes:

This document uses the interactive IPython notebook format (now also called Jupyter). The notes can be accessed in several different ways:

Many of these notes make use of the climlab package, available at https://github.com/brian-rose/climlab

In this assignment you will investigate how the CESM slab ocean model responds to a doubling of atmospheric CO2.

Your assigment

  1. Answer all questions listed in this notebook.
  2. As before, write up your answers (including text, code and figures) in a new IPython notebook. Try to make sure that your notebook runs cleanly from start to finish, and explicitly imports every package that it uses.
  3. Save your notebook as [your last name].ipynb, e.g. my notebook should be called Rose.ipynb.
  4. Submit your answers by email before class on Thursday February 19.

Homework questions: part A

Here we investigate differences between the control simulation and the 2xCO2 simulation (after it has reached its new, warmer equilibrium).

  1. Calculate Equilibrium Climate Sensitivity (ECS) for the CESM slab ocean model.
  2. Calculate the net TOA energy flux in the control run and in the equilibrated 2xCO2 run (time and global averages). Are they both close to zero?
  3. What is the change in ASR and the change in OLR after doubling CO2?

  4. What are the clear-sky and cloudy-sky components of those changes?

  5. Make well-labeled maps of the change in the annual mean of these five quantities:

    1. Surface temperature
    2. ASR (total)
    3. ASR (clear sky)
    4. ASR (cloudy sky)
    5. OLR (total)
    6. OLR (clear sky)
    7. OLR (cloud sky)

  6. Comment on what you found in your maps.

    • Which regions warm more than others?
    • Are there any discernible spatial patterns in ASR and OLR changes?
    • What about the clear and cloudy sky components?
    • Comment on anything you find striking, interesting, or unexpected in these results.

Homework questions: part B

Here we investigate the transient adjustment to equilibrium.

For this, we will use the file

som_2xCO2.cam.h0.global.nc

This file contains a monthly timeseries of the CESM model output from the 2xCO2 model run, which was initialized from the control run. Every variable in this file has already been averaged globally. We can use the timeseries to look at the adjustment of the global average temperature and energy budget to the new equilibrium.

  1. Make a well-labeled graph of the timeseries of global mean surface temperature.
  2. You will find that there is a well-defined annual cycle in this temperature. Offer a reasonable hypothesis to explain why such a cycle exists in the simulation.
  3. Implement some kind of running average filter to smooth out the data. (There are many ways to do this... do whatever makes sense to you, but make sure your code is self-explanatory).
  4. Make another graph of the smooth timeseries. Does it look anything like the exponential relaxation curves we found in the zero-dimensional EBM?
  5. In another graph, plot smoothed verions of the timeseries of ASR and OLR.
  6. Comment on anything interesting you learned from these figures.

Verifying the annual cycle in global mean surface temperature against observations

Here we still study the annual cycle in global mean surface temperature and verify it against observations. For observations, we will use the NCEP Reanalysis data.

Reanalysis data is really a blend of observations and output from numerical weather prediction models. It represents our “best guess” at conditions over the whole globe, including regions where observations are very sparse.

The necessary data are all served up over the internet. We will look at monthly climatologies averaged over the 30 year period 1981 - 2010.

The data catalog is here, please feel free to browse: http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/catalog.html

Surface air temperature is contained in a file called air.2m.mon.1981-2010.ltm.nc, which is found in the directory surface_gauss.

Here's a link directly to the catalog page for this data file: http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface_gauss/catalog.html?dataset=Datasets/ncep.reanalysis.derived/surface_gauss/air.2m.mon.1981-2010.ltm.nc

Now click on the OPeNDAP link. A page opens up with lots of information about the contents of the file. The Data URL is what we need to read the data into our Python session. For example, this code opens the file and displays a list of the variables it contains:

In [1]:
import netCDF4 as nc
ncep_air2m = nc.Dataset("http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface_gauss/air.2m.mon.1981-2010.ltm.nc")

for v in ncep_air2m.variables: print v

climatology_bounds
lat
lon
time
air
valid_yr_count

The temperature data is called air. Take a look at the details:

In [2]:
print ncep_air2m.variables['air']

<type 'netCDF4.Variable'>
float32 air(time, lat, lon)
    long_name: Monthly Long Term Mean of Air Temperature
    valid_range: [ 150.  400.]
    units: degK
    add_offset: 0.0
    scale_factor: 1.0
    missing_value: -9.96921e+36
    precision: 2
    least_significant_digit: 1
    GRIB_id: 11
    GRIB_name: TMP
    var_desc: Air temperature
    dataset: CDC Derived NCEP Reanalysis Products
    level_desc: 2 m
    statistic: Long Term Mean
    parent_stat: Mean
    actual_range: [ 198.33992004  311.89520264]
    _ChunkSize: [  1  94 192]
unlimited dimensions: 
current shape = (12, 94, 192)
filling off

Notice that the dimensions are (12, 94, 192) -- meaning 12 months, 94 latitude points, 192 longitude points. Not the same grid as our model output!

Homework questions: part C

  1. Offer a reasonable hypothesis to explain why you found an annual cycle in global mean surface temperature in the simulation.
  2. Verify that such a cycle is also found in the NCEP Reanalysis data.
[Back to ATM 623 notebook home](../index.html)

Credits

The author of this notebook is Brian E. J. Rose, University at Albany.

It was developed in support of ATM 623: Climate Modeling, a graduate-level course in the Department of Atmospheric and Envionmental Sciences, offered in Spring 2015.