Tutorial 3: Identifying the Basic Ingredients
Contents
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
Content editors: Jenna Pearson, Chi Zhang, Ohad Zivan
Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan
Our 2023 Sponsors: NASA TOPS
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¶
####### 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
############ Video 3: Identifying the Basic Ingredients
# @title Video 3: Identifying the Basic Ingredients
#Tech team will add code to format and display the video
Tutorial Objectives¶
In Tutorials 1-4, you will learn about the process of research design. This includes how to
Identify a phenomenon and formulate a research question surrounding it
Efficiently review existing literature and knowledge about the phenomenon
Identify what is needed to study the phenomenon
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:
Pangeo hosts a few open-access datasets
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