Data, data, data

ENVS 202 On-line Access


Important:

  • If you believe that global warming is a real problem then take a GREEN strip from the Sacred Box of Sampling.

  • If you believe that global warming is a hoax or a myth or not all that alarming or important take a RED strip




    Looking More at Data

    First a return to the Salmon issue:


    White Men Luv Fish

    The above documents and data strongly suggest that a significant reason for Columbia river salmon decline is due to increased harvests in Alaska and Canada.

    This is likely a reflection of where the Salmon are and this is strongly coupled to decadel climate changes in the Alaska-BC-PNW region.

    This implies, that at any particular point in this region, over timescales of 50 years, salmon counts/catches will be cyclical in nature.

    So, be careful what you are told without being allowed to see the larger picture:

    Graph shows total salmon catch off the coast of North America (including Alaska).

    Decline of In-River catches which is frequently used as the "best" indicator for overall salmon decline in the Columbia River System:

    Implication is that increases in ocean catch are directly responsible for decreases of in-river catches also shown.

    Looking at more data to find significance or not:

    Another Case: US Tornado Frequency:

    This is the distribution of verified tornadoes in the American Midwest from the period of 1953--1993:

    Is the apparent recent increase significant?

    Another Case: Urbanization and the Weather

    Does Urbanization produce a microclimate in the sense that urban areas are getting warmer.

    Where the data is

    Here are some results for Washington DC average Tempearture:

    • 1960-1990: 14.78 +/- 8.5 N=360
    • 1960-1970: 14.26 +/- 8.8 N=120
    • 1970-1980: 15.25 +/- 8.3 N=120
    • 1980-1990: 15.01 +/- 8.5 N=120

    Note that the dispersions are all very similar in these periods. The square root of 120 (number of data samples) = 11.

  • Error in the mean for 1960-1970 = 8.8/11 = 0.80
  • Error in the mean for 1970-1980 = 8.3/11 = 0.75

  • (M1-M2)/1.5E1 = 15.25-14.26/(1.5*.8) = 1/1.2 = 0.83 --> no significant increase. A significant increase would be hard to detect in this data because the instrinsic dispersion is large.

    Let's look at the case for Berkeley CA:

    • 1960-1970: 13.67 +/- 3.1 N=120
    • 1980-1990: 14.32 +/- 3.0 N=120
  • The error in the mean is a lot less because the disperion is less. The mean error is approximately 3/11 = .27

    So for Berkeley we have: 14.32-13.67/1.5(.27) = .65/.40 = 1.65 --> still not significant but a lot more significant than the case for Washington DC.

    This indicates that Berkeley is a better site for this study than Washington DC since the natural climate variations in DC are more severe and hence the signature of a small effect will be much more difficult to get out of the data.

    Now, what do you make of this data?

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