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Tutorial 3: Identifying the Basic Ingredients

Good Research Practices

Content creators: Marguerite Brown, Yuxin Zhou

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

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Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan

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Video 3: Identifying the Basic Ingredients

Video 3: Identifying the Basic Ingredients

Video 3: Identifying the Basic Ingredients

Video 3: Identifying the Basic Ingredients
Video 3: Identifying the Basic Ingredients

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# @title Video 3: Identifying the Basic Ingredients
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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?

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