• Dhar@lemmy.caEnglish
    13·
    2 days ago

    “… dominate the headlines.” puts graph at bottom of article

    • collapse_already@lemmy.mlEnglish
      6·
      2 days ago

      You don’t know what the day of the year is? The chart shows what the standard deviation from the normal temperature is for each day of the year, so all June 30ths show up in the same column for instance. It’s worth noticing that the baseline that they are comparing to is probably already elevated because it starts well after green house gas emissions started because that is when we had the satellites to gather the data. So the 3+ standard deviations is probably under representing the temperature compared to the pre-hydrocarbon economy.

      • kryptonianCodeMonkey@lemmy.worldEnglish
        2·
        1 day ago

        Just noting that the “baseline”, I believe, is just the average temp for all recorded temps for that date, and the std. dev. Is likewise based on those recorded temps. They didn’t just some nominal values that they’re comparing to. The baseline changes with each new recorded temp. So, yes, they’re definitely higher than pre-hydrocarbon economy.

        • collapse_already@lemmy.mlEnglish
          2·
          1 day ago

          The baseline stopped in 2020. “Standard deviations from the 1991-2020 mean” The standard deviation is of course dependant upon how many samples (30 per day)are in the data set. The only reason I qualified my statement is we don’t have the actual historic data to say without qualification, and if you went back to the Jurassic or further, who knows with any certainty what to compare with (tectonics at some point mean that the area in question didn’t exist). The data certainly is indicative that the baseline data would be significantly lower, but we cannot say that with the authority that we can for the data collected by satellites which is definitive and pretty much unassailable.

    • kryptonianCodeMonkey@lemmy.worldEnglish
      2·
      22 hours ago

      Yes, the graph is meant to compare the deviation from average (mean) on a given day every year. Each line is a different year, the independent axis is the day of the year (e.g. July 12th), and the dependent axis is the number of standard deviations from average temp for said date.

      The expected temperature varies wildly depending on the day of the year which is why is makes sense to find the average and standard deviation for the same date year over year and compare the measured values to that statistic. If you are comparing different dates, say June 3rd to February 18th, what meaningful information do you gain by saying that June 3rd was hotter than the average between those two dates? But if this June 3rd was the hottest June 3rd on record by several standard deviations, that’s notable.

      Day of the year comparison is as close to apples to apples as you can get given the seasons. And it is much harder to compare year over year levels at a given date if they are graphed sequentially, especially for 44 years worth of data. So you overlay them aligning on date, set your average and deviations based on the measured temps for that date for all years, and you can see seasonal trends or outliers such as this one.