Home > Uncategorized > Cooling stations. A UHI Hint

Cooling stations. A UHI Hint

Update: google earth files in the box: Personally I like to look at things backwards. Why are cool sites cool? So download the kml or kmz file and you can tour 62 sites: All with 90 years of data or more. All with a cooling trend. And all “supposedly” urban. what do you see at the meso scale. Anyway’s The next drop will have the animation code, the kml code, file download diagnostics, and a script to replicate the cool urban stations.

Let’s recap where we are. I went in search of the the biggest warming trend and biggest cooling trend in the data.( for an entirely different reason ) And after restricting our view to stations with long records, stations with 90 years of data in the period 1900 to 2009, we landed on this distribution of trends. The trend per decade.

Again, just looking at the distribution gives us some information. We’re seeing what appears “somewhat” normal, Just a quick look at the density,ECDF and QQ.

Density

ecdf

QQplot

By eye I was wondering if the trends were a gamma –err prolly not–  or log normal. A gamma sorta makes sense if one considers that a warming rate is going to take a while to appear. At some locations the “wait” time will be shorter while at other places the “wait” time will be longer. However, I didnt do any formal testing on this other than looking at the QQ– shrugs– just an observation to maybe come back to.

As we see we have 1492 stations. Looking at the metadata for urban/small town/ rural we have this:

table(Yr90Inv$Rural)
R   S   U
757 320 415

Which shows us that  for stations with long records over half of them are “rural” by the designation in the inventory. Now, of course, that designation has it flaws, but this is just exploratory data analysis. The next step I took what to isolate just the stations that had negative warming. otherwise known as cooling. And  we pull up the most extreme case:

Id                                              Name    Lat     Lon Altitude   Rural

4682 42572681004                  KETCHUM RS  43.68 -114.35          R

I noted a couple things. It’s a rural site in a Mountain valley. But seeing that drop in the later years and what may be a discontinuity ( undocumented station change) , I moved on to next station

42572438001      OOLITIC PURDUE EXP FM   38.88  -86.55      198      175     R

A couple things: What we know from the distribution of all stations is that rural stations constitute half of the sample. Now, on the supposition that  there is no UHI, that rural and urban see the same warming over 110 years of data one can expect this. One can expect that the sample of cooling stations drawn from the whole sample will have the same distribution of urban/small town/rural: roughly 50:25:25. So, when the second station I drew from the far end of the distribution was also rural, well thats like two heads in a row. Nothing special, but how long a streak would I get? Well, you have to look at 32 stations ( sorted from coolest to warmest) before you get to an urban station. hmm. And the other thing that was striking was this. That station ALSO happens to be the first NON US station. go figure: On one hand the US stations tend to have longer records so I can expect a lot of US stations. Still the number of us stations in the “cool” distribution seemed worthy of investigation ( later work if somebody wants to play)

61111518000                PRAHA RUZYNE  50.10   14.25

A couple points that I have discussed about UHI. UHI  results from a few things.  Chief among them is the disruption of the boundary layer that results from building tall buildings. And of course changes in the surface properties and lastly waste heat, probably the least important. Population is not a precise measure of any of these. So here we have a site probably at the airport with a clear fetch all around. We do not have an urban environment with tall buildings. We also see that airports are not categorically bad.  There is another factor as well that Oke mentions that few have picked up on. That is the difference in wetness between the urban landscape and the surrounding rural environment. More on that later. lets hit the next “Urban site”

42572216003                  ALBANY 3SE  31.53  -84.13

Map

And the next: 12567083000                ANTANANARIVO -18.80   47.48

Map

So in the top 50 cooling sites, here is what we have: 3  “urban” sites. 48 sites from the US.  At those “urban” sites we have two airports with what appear to be long  fetches. If you have a long fetch, your UHI is going to be minimized. If you have buildings destroying that fetch, you have some of the preconditions to generate UHI. That’s why, for example, I think some of the concerns about waste heat at airports are potentially flawed. And one final note. Note the lake. More on that at a future date.

So sum up the little exploration of the data, I’ll leave you with something to chew on: recall that the 1500 or so stations with long records were split  50:25:25:  Rural:small town: urban.

When we  segregate the data by trend and look at cooling stations, is this structure preserved:

> table(cold90Inv$Rural)

R   S   U

308 143  62

Nope:  Is it significant? Does it actually indicate anything? but it is interesting that when we look at the long records, and the cooling stations within those long records that urban sites are very few in number. Are they Actually urban? Nobody seems to ask questions like that. Partially that’s because people don’t understand everything that goes into UHI. Also because they tend to be mesmirized by the close up shot which focuses on waste heat or surface material. They often forget the bigger meso scale picture. That tarmac, sitting on the coast of an ocean has a long clean fetch and most students of Oke or Parker know what wind does to Tmin. Next up, I will need to re integrate some KML code and we can take a tour of these 62 ‘urban’ stations and all the ‘cooling’ stations. On the ground what do they have in common. Not in the “metadata”. on the ground.

Categories: Uncategorized
  1. cce
    September 30, 2010 at 11:16 AM

    The predominance of US stations is probably due to uncorrected TOBS, combined with the fact that the US hasn’t warmed all that much. Rural vs. Urban could be caused by moving urban stations to the country. You might compare the “raw” data to the their adjusted counterparts. I bet a lot of the “cooling” drops out, plus some step changes will show up.

  2. Steven Mosher
    September 30, 2010 at 11:35 AM

    Hi cce.

    Not sure what “uncorrected TOBS means” or how you determined that. A likely explanation is this:

    out of 1492 stations with long records ( warming and cooling) 1122 of them are from the US

    so TOBS, the US not warming alot, etc etc are not on my first line of explanations.

    Also not sure what you mean by moving “urban” to “rural” not even sure anymore what those terms mean. I know what people think they mean but after spending a bunch of time reading Oke and others I’m in serious doubt over a metadata approach to identifying them. still fishing about

  3. cce
    September 30, 2010 at 7:41 PM

    75% of the long records are from the US, but 96% of the top 50 cooling records are from the US. Since you’re using the “raw” data, many US stations have very large uncorrected time of observation bias which will make the past look much warmer than it really was. That has to skew “cooling” in favor of US stations.

    Regarding rural vs urban, whether the moves are identified or not, out of 1000+ stations, you are going to see some stations moved from urban areas to rural areas, and they will be considered “rural’ today. This will make recent observations cooler relative to the past.

    For places as densely covered as the US, I would think all you need to identify moves (or other changes) is three closely sited stations. If there is a step in one relative to the other two, then we know that the first was moved or otherwise changed. I’d imagine there are software packages designed to find such things. I recall McIntyre using something like this for a post on the MSU step changes.

    • Steven Mosher
      September 30, 2010 at 8:34 PM

      Thanks cce. I was kinda focused on something else. ( looking for stuff on urban surfaces everything was behind the paywall

      1 . Ya we know the US ( GHCN ) has uncorrected TOBS. what we dont know is that the rest of the world has corrected tobs.
      I’m sure that the TOBS corrections ( which corrected largely rural records) will start to shift things, but with no records of whether
      other countries did or did not perform TOBS corrections, one is a bit at a loss. However, on the programming side, this could
      be a nice spot to introduce USHCN into the picture,

      2 The urban to rural moves. Probably a part of the explanation. not sure how you establish it as definitive. hmm

      3. I was kinda headed toward a comparison of these sites with the surrounding sites. if you havent guessed I’m more headed down
      the spatial variation fork of discussion and less of the UHI discussion. I was looking for
      cooling trends” to start to get a handle on the some of the spatial variation work ( and some arima.sim ) Carrick was suggesting and I wanted to find the
      longest most extreme records I could ( the one that warmed the most and the one that warmed the least ) So the whole UHI thing is a bit of a distraction from my main thing. Also looking for some ideas on how to show the stuff graphically.

  4. Carrick
    October 12, 2010 at 6:10 PM

    cce:

    Since you’re using the “raw” data, many US stations have very large uncorrected time of observation bias which will make the past look much warmer than it really was.

    How many is “many”?

    How big is the TOBS correction compared natural temperature trends in the US?

    Do you have numbers for either of these, or are you just speculating?

    • Steven Mosher
      October 13, 2010 at 12:22 AM

      long ago USHCn used to post their stepwise changes and TOBS was a big mover of data.
      primarily rural stations (cce to correct me) The issue being moving the standard time back to midnight. The size of the correction ( all told) was a good portion of the warming. I want to say half, but I’m not certain. It was something we all screamed about ( till I understood).

      I think the charts showing it have been posted several times. Anthony was big on this. Might be posted on CA somewhere. NOAA has taken the chart down ( last I looked)

      The biggest issue with TOBS is the SE of prediction. Its an empirical model that does a nice unbiased correction, but with hefty SE.

  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: