MooMooMilkParty

There is actually an energy cost to evaporation which takes some of that energy: https://en.wikipedia.org/wiki/Enthalpy_of_vaporization

So, this, as well as ocean mixing is how the surface temperature is "moderated", in addition to the heat capacity that others have brought up.

Not a dumb question at all! The main reason is because there is much more land mass in the northern hemisphere than in the southern, and land tends to heat up more than ocean. You can actually see how this effect balances out on this site: https://climatereanalyzer.org/clim/t2_daily/

If you look at the annual temperature range for the northern hemisphere (NH) it has a range >10C, while the southern hemisphere has a range ~5C.

You can read more about this here: https://www.carbonbrief.org/guest-post-why-does-land-warm-up...

Yes, absolutely. Most climate models run on supercomputers, same with molecular dynamics, large scale fluid dynamics, energy systems simulations and of course a whole lot of weapons research.

Related I also gotta recommend "Uncommon Carriers" by John McPhee. It's got a chapter that follows a barge operator on the Illinois river that's pretty great.

I love a good cynical take as much as the next HN reader, but as a researcher working in the environmental/climate space many of us are using these words because the are true. Sure there are some folks I consider full of buzzwords and light on substance but I think it's warranted for scientists to make "inflated" claims to get their research noticed. Yes, there are structural problems in academia and research funding that do need to be fixed, but I'm not sure this shift in language is completely a symptom of it. People are actually dying out there and we are pleading to get funding to try to help.

Just the last couple of weeks: - Flooding in Pakistan kills 1000+: https://www.vox.com/world/2022/8/30/23327725/pakistan-floodi...

- Ongoing slow disaster in the Colorado River Basin: https://www.npr.org/2022/08/27/1119550028/7-states-and-feder...

- Jackson, MS water crisis: https://www.cnn.com/2022/08/30/us/jackson-water-system-faili...

- Record breaking heat forecasted for CA this coming week: https://www.latimes.com/california/story/2022-08-31/the-heat...

- Spanish drought destroys harvests: https://www.bbc.com/news/world-europe-62707435

- China factories shut down because of heat wave: https://www.wsj.com/articles/chinas-factories-hurt-amid-covi...

- Drought dries up rivers in France: https://www.reuters.com/world/europe/frances-river-loire-set...

Also worth reading, and considerably more up to date, is Science Be Dammed.

https://uapress.arizona.edu/book/science-be-dammed

Just adding to this, not far south of Las Vegas the Central Arizona Project is a rather large canal (so not exactly a pipeline but close enough) which transports Colorado River water to Phoenix and Tucson.

https://en.wikipedia.org/wiki/Central_Arizona_Project

My guess is if you're posting here you're not necessarily the target market for this product. You might be better with just a Sawyer squeeze or other off the shelf filter. If you're looking for clarifying turbid water you could get a bottle of alum from the grocery store and use that as a flocculant. Or if you wanted heavier duty there's "Water Wizard for River Runners" which is made of stuff which is often used for wastewater treatment.

You can get a copy at the lead author's website: http://www.jasechko.com/papers.html

Edit: Or, here's the direct link: http://science.sciencemag.org/cgi/content/full/372/6540/418?...

Scientific computing is what I would be interested in for these types of hardware.

I'm a little bit curious here. My understanding is that the wave function has complex values and the "state" is described by the wave function times its complex conjugate. Couldn't they have meant the factor of -1 to be on the imaginary portion and then things still work out to have the same probability distribution?

Posting this verbatim from my reddit comment, but I ran a few quick fits and the (arbitrarily scaled) Planck distribution looks better than standard fits of some other distributions. Of course I ran absolutely no statistics on these or tried any sort of real analysis that should have been included in the paper in the first place, so take it with multiple grains of salt:

I fit a few distributions to the data and actually the Planck distribution at 9000K seems to be noticeably better. I did no further statistics and haven't even looked at the fitted parameters for this. Note: I am an earth scientist, not a physicist. https://imgur.com/Qg4ixF2

Edit: More distributions: https://imgur.com/IrF6lsV

Edit: Filtering out sodium and potassium to try to account for some of the low-wavelength counts doesn't seem to help fitting the distributions either: https://imgur.com/dHDUS9Z

You can get the data here: https://physics.nist.gov/PhysRefData/ASD/lines_form.html

And here's the (garbage) code to reproduce my plots:

    %pylab inline
    import pandas as pd
    import scipy
    import scipy.stats
    
    mpl.style.use('seaborn-muted')
    mpl.rcParams['figure.figsize'] = (18, 12)
    mpl.rcParams['font.size'] = 16
    df = pd.read_csv('nist.csv')
    
    def filter_string(x):
        x = str(x).replace('=', '').replace('"', '').replace('*', '').replace('+', '').replace('(', '').replace(')', '')
        if len(x) == 0:
            x = 'NaN'
        return x
    
    asl_comp = df['ritz_wl_vac(nm)'].apply(filter_string).astype(float)
    asl_ver  = df['obs_wl_vac(nm)'].apply(filter_string).astype(float)
    sp_num = df['sp_num'].astype(float)
    
    df_neutral = df[sp_num == 1]
    asl_comp = df_neutral['ritz_wl_vac(nm)'].apply(filter_string).astype(float)
    asl_ver  = df_neutral['obs_wl_vac(nm)'].apply(filter_string).astype(float)
    sp_num = df_neutral['sp_num'].astype(float)
    
    asl_ver = asl_ver[~asl_ver.isna()]
    
    def planck(wav, T=9000.0):
        h = 6.626e-34
        c = 3.0e+8
        k = 1.38e-23
        a = 2.0*h*c**2
        b = h*c/(wav*k*T)
        intensity = (a / ( (wav**5)) * (1 / (np.exp(b) - 1.0) ))
        return intensity
    
    
    dist_names = ['beta', 'lognorm', 'pearson3', 'gumbel_r']
    x = np.arange(0, 1500, 5)
    intensity = planck(x / 1e9)
    size = len(asl_ver)
    
    yvals, xvals = np.histogram(asl_ver, bins=300, normed=True)
    
    for dist_name in dist_names:
        dist = getattr(scipy.stats, dist_name)
        param = dist.fit(asl_ver)
        pdf_fitted = dist.pdf(x, *param[:-2], loc=param[-2], scale=param[-1])
        plt.plot(x, pdf_fitted, label=dist_name, linewidth=4,)
    
    plt.hist(asl_ver, bins=300, density=True, color='grey');
    plt.plot(x, intensity / 1.1e17, linewidth=4, color='red', linestyle='--', label='scaled planck (9000k)')
    plt.legend()
    plt.xlabel(r'$\lambda (nm)$')
    plt.ylabel('Density')

Aha! As far as persisting calculations, this makes sense. I guess in my head I was thinking "pure" data where everything opened was precomputed. Forgive my ignorance about database solutions, but do they not implement something similar?

But now you've got me thinking, it would be nice if libraries like Dask could allow for flagging of symbolic operations like this to be written to disk for quickly saving metadata where intermediate steps don't explicitly need to be saved.

You're blowing my mind here a little bit. I don't use MySQL but do use Excel and the R/Python data science stacks quite a bit. I regularly open data with tens or even hundreds of thousands of columns in the latter by using lazy computations. Is this not the case with database approaches? What kind of hardware are we talking? Presumably Excel has to load everything into memory at once since it's immediately viewable, right?

I hope this capability gets to maturity. It consistently seems like anything I want in a language is in progress in Julia; new scientists in 15 years are going to be very spoiled if Julia becomes mainstream.

I'm curious about your last statement. I guess I'd be considered a night owl by most people, and certainly have poor sleep hygiene in the eyes of early birds, but I find myself most motivated to do really "intellectual" work about 2-3 hours after dinner. For the record, when I say "intellectual" work I don't specifically mean academic, but more broadly creative. Do you specifically mean something like learning in the classroom setting?

Now I'm also curious about the differences in free-form learning (which I guess I'm talking about) vs the more traditional classroom learning. Do you have any insight on this?

Not really. The point of the Ng quote is essentially:

We don't have the technology to populate Mars -> We can't overpopulate Mars.

and

We don't have the technology to create sentient computations -> robots won't decide to kill us.

Your analog would have been:

We don't have the ability to affect the climate -> no anthropogenic disasters.

But, we know the antecedent in this case is false. Now I'm not asserting that anthropogenic disasters will occur (based on these arguments), but just pointing out the flaw in your logic.

I wish I could find the source for this, but I remember reading that Gauss idolized Newton for his style. From what Gauss (and a tiny bit of Newton) I have read, it seems like he didn't like to provide any sorts of motivation or exposition. So, FWIW there's a second anecdote in support of this idea.

> I don't know how anybody can take models like this seriously. It's about as valid as any of these economists models. Hell, it is partly an economic model.

There is a huge variety of models spanning the gamut of complexity. This analysis is for one. It's about adding to the literature.

> Reading the paper, they make a big deal about the out of sample testing - but given that there is a clear trend, is anybody surprised the model fits? Do you even need a fancy Bayesian hierarchical model or would a regression have predicted the same thing.

Again, they probably don't need the Bayesian model, and regression models have been explored thoroughly in the literature. This is simply another tool, and another data point.

> To top it off there's not mention of the CO2 cycle and the forces sucking CO2 out of the air they just assume the CO2 is cumulatively stasis in the air for perpetuity.

I feel like a broken record, but there have been studies on this as well. Further, cumulative emmisions are an important indicator. See: http://www.pnas.org/content/106/6/1704.abstract

> This is the reason why there is growing discontent for climate science. We need it now more than ever, but the academic community just publishes bullshit and yells at us if we doubt the veracity of their models.

Erm, this paper fully acknowledges the fact that it's a completely statistical model, and the many ways that it could be completely incorrect. If anything, blame Nature for publishing it, not the authors for doing the work.

How many climate modeling papers have you read? They are not all statistically based (in fact many of the big oness are completely process based if that makes you happy). Much of the literature is aimed directly at addressing both uncertainty and error, and if you actually speak to anyone doing the work they will most certainly drone you half to death about things they wish they could fix/take into account.

> We've had a period of cooling that none of their models have predicted, and instead of saying, hey we aren't that good at modelling, they and everyone else who went all-in politically on these models have started a hate campaign against skepticism.

Ugh, I'm not even going to address this one. Someone else did a good enough job already.

> It's a strange world where scientists use the word 'skeptic' as a derogatory term.

Let's be clear here - that term is a propaganda technique used to astroturf and whitewash. I agree, it's unfortunate terminology, but it's not the fault of the scientists.

I've used his differential-line repo to make some wall art for my apartment, it's some beautiful stuff:

http://imgur.com/a/BoKdy

Repo at: https://github.com/inconvergent/differential-line

I think one of the hard things about academia (by ways of "pure research"), is measuring impacts. In some positions there are metrics which are more representative of real progress than others. My belief is that measuring an academic's "impact" is a very fuzzy thing in the long term, especially when you bring in people who don't specialize in the same field. That's a reason this can't be generalized fully, though you can probably find corollaries.

But, your last sentence to me, seems like a good one. Presumably, that would be an excellent topic to pursue research in, considering we clearly don't have a working theory on it (but that's not my specialty so who knows, maybe there's a bunch of smart people being ignored about it). Thus, it's hard to measure progress in, and we institute perverse incentives per the article.

Good call on going pure python. To take this a bit further I made your changes and used numba with @jit(nopython=True, cache=True), for some interesting results. If I do include the sorting into the timing:

    Unsorted execution time: 0.2175428867340088 seconds
    Sorted execution time:   1.133354663848877 seconds

And if I don't:

    Unsorted execution time: 0.21171283721923828 seconds
    Sorted execution time:   0.08376479148864746 seconds

Masakage knives: http://masakageknives.com/

There are many other makers of kitchen knives that are comparable to Masakage knives, but if someone who was serious about cooking asked me for a single recommendation of which knife brand they should buy into this would probably be my response. Their knives are reasonably priced, feature beautiful aesthetics, have personality, and perform as well as anyone could reasonably need.

I wrote up a small script to test this out for python (at bottom of this post). The results on my laptop are about 8.0 seconds for unsorted and 7.6 seconds for the sorted version. I'm assuming that the discrepancy for python is much smaller due to the slow nature and high overhead of the language (or at least the way I've used it here), but I would be interested to know: how would one go about finding out what the python interpreter is doing beneath the surface?

Edit: After running with a wider range of parameters, it seems that the difference is always roughly the same order of magnitude. To investigate further, I included the sort into the second timing to double check and for 3276800 elements it's still a bit faster overall when you sort the array.

  #!/usr/bin/env python
  import time
  import numpy as np
  
  def main(n=32768):
      arr = np.random.randint(0, 256, n)
      t0 = time.time()
      sum1 = do_loop(arr)
      t1 = time.time()
  
      arr = np.sort(arr)
      t2 = time.time()
      sum2 = do_loop(arr)
      t3 = time.time()
  
      assert sum1 == sum2
      print(" Unsorted execution time: {} seconds".format(t1-t0))
      print(" Sorted execution time:   {} seconds".format(t3-t2))
  
  def run_many(func):
      def wrapper(arg):
          for t in range(1000):
             func(arg)
          return func(arg) 
      return wrapper
  
  @run_many
  def do_loop(arr):
      tot = 0
      for i in arr:
          if i >= 128:
              tot += i
      return tot
  
  if __name__ == '__main__':
      main()

The one thing I'd love to see, as a vim user and a scientist, is something like Matlab/RStudio/Spyder where you can explore variables that exist within a session. Maybe it could hook up to an ipython session and you could display it in a separate terminal and send commands with vim-slime to be updated in real time.

For the most part, when I need this, I revert to Rodeo (since it has vim bindings like RStudio's), but if I could ditch it that would be amazing. Nothing against Rodeo or other editors, but vim habits are hard to break.

You could always just use a bowl and a whisk - or even a fork if you're really a minimalist. Made a batch of bourbon vanilla whipped cream last night.

Lorenz's Deterministic Nonperiodic Flow is the earliest work on the topic that I can think of. It's relatively accessible too.

http://journals.ametsoc.org/doi/abs/10.1175/1520-0469(1963)0...

This is a great read. If I were closer to the area I'd definitely have spent some time trying to recreate the investigation and check out the site.

Also, got to plug the story of the search for the lost German tourists in Death Valley[1]. I feel like his writing in that series really gets you in the head of the lost, and is morbidly fascinating.

[1] http://www.otherhand.org/home-page/search-and-rescue/the-hun...

While you are correct that methane is quite potent, when discussing climatological effects it's not as important as CO2. Especially when discussing ice sheets and sea level rise, the long term (1000+ year) effects are what's likely to reduce Greenland and Antarctica to points where feedbacks will prohibit their regrowth (thus dooming them to complete melt and us to deal with the unfortunate side effects).

Again, not disagreeing in any way, just looking to provide some context in relation to the subject matter in the original post. In fact, I think climate change and environmental stewardship are issues that go hand in hand and should definitely be taken more seriously.