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Tutorial 3: Identifying the Basic Ingredients#

Good Research Practices

Content creators: Marguerite Brown, Yuxin Zhou, Natalie Steinemann, Zane Mitrevica

Content reviewers: Katrina Dobson, Sloane Garelick, Maria Gonzalez, Nahid Hasan, Paul Heubel, Beatriz Cosenza Muralles, Sherry Mi, Cheng Zhang

Content editors: Jenna Pearson, Chi Zhang, Ohad Zivan

Production editors: Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan

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Tutorial Objectives#

In Tutorials 1-4, you will learn about the process of research design. This includes how to

  1. Identify a phenomenon and formulate a research question surrounding it

  2. Efficiently review existing literature and knowledge about the phenomenon

  3. Identify what is needed to study the phenomenon

  4. Formulate a testable hypothesis regarding the phenomenon

By the end of these tutorials you will be able to:

  • Understand the principles of good research practices

  • Learn to view a scientific data set or question through the lens of equity: Who is represented by this data and who is not? Who has access to this information? Who is in a position to use it?

Video 1: Basic Ingredients#

Activity: Identifying Basic Ingredients#

Take 10 minutes to discuss the advantages and disadvantages of utilizing the following basic ingredients to explore the research question discussed in Video 1:

  • Ice core data for CO2

  • Deep sea sediment data for sea surface temperature

Can you think of alternative approaches that might work well?

Choosing Your Data#

Click here for some pointers on how to choose your data

Here are some questions to ask yourself when choosing the data to use:

What physical processes must be included?

  • You don't want an approach that contains less than the bare minimum. For some phenomena, we know what the bare minimum is. For others, more research is needed...
  • If you are unsure about what physical processes are needed, check the literature!

What spatial and temporal resolution is necessary to capture the phenomenon?

  • GCMs can typically have a spatial resolution around 100km and time resolution of several hours.
  • For phenomena that require higher resolution, you can either
    • Use a more idealized model that resolves smaller scales
    • Implement a parameterization of the sub-gridscale features within the GCM.

What restrictions do I have for computational resources?

  • If you do not have access to large computational resources, you can still do research using smaller datasets or idealized models

Am I interested in looking at a particular time period or a specific physical location?

  • Reanalysis can be used for time periods after roughly the 1940s
  • Proxy data can be used for a wider historical and prehistorical data
  • Both reanalysis and proxy data can provide specific location information
  • Models can be designed to mimic the conditions of the location or time, for example:
    • GCMs (General Circulation Models or Global Climate Models) can be set according to parameters that resemble the time period
    • Energy balance models can capture some aspects of average temperature in other time periods
    • Radiative-convective equilibrium models can capture some phenomena in the tropics
    • Quasi-geostrophic models can capture some phenomena in the mid-latitudes (between ~30-60 degrees)
    • And many more!

Am I interested in studying a feature of the phenomenon in isolation or interactions between multiple features?

  • If you want to isolate a single aspect of the phenomenon, an idealized model may be more appropriate
  • If you want to study interactions between multiple features, either observational data or a more complex model may be appropriate

Am I trying to…

  • explain the theory behind the phenomenon? An idealized model may be appropriate
  • provide evidence to support or challenge a pre-existing hypothesis? Observational data or a more complex model may be appropriate
  • document the features of the phenomenon? Observational data may be appropriate

For more information on observational data:

For more information on numerical modeling:

  • Atmospheric Model Hierarchies: Maher, P., Gerber, E. P., Medeiros, B., Merlis, T. M., Sherwood, S., Sheshadri, A., et al. (2019). Model hierarchies for understanding atmospheric circulation, Reviews of Geophysics, 57, 250– 280. https://doi.org/10.1029/2018RG000607

  • Ocean Model Hierarchies: Hsu, T.-Y., Primeau, F., & Magnusdottir, G. (2022). A hierarchy of global ocean models coupled to CESM1. Journal of Advances in Modeling Earth Systems, 14, e2021MS002979. https://doi.org/10.1029/2021MS002979