The Blackboard

In discussions of the application of the Hansen/Lebedeff gridding method to “toy planet” data, a few people noted that the “toy” data used did not hightlight a known way bias can be introduced into a computed temperature trend as a result of dropping out a whole bunch of stations. This can give the false impression that I’m suggesting the anomaly method takes care of any and all data problems associated with adding or dropping thermometers used to measure the trend: It does not. In fact, even when using the anomaly method, bias can be introduced when we add or drop the number of thermometers used to measure temperatures and the thermometers used each measure temperatures at locations that exhibit different trends. For example: Climate scientists predict that the northern latitudes will warm at a faster rate than southern latitutes. If so, then dropping all the northern thermometers could result in a bias in trend. To show how, I’ve modified the spreadsheet and created synthetic data to create graphs that highlight how trends can be biased when thermometer are biased in a particular way.

The Urban Heat Island (UHI) effect is something of a charged subject on climate science blogs. Depending on who you ask, you might hear that it either accounts for the majority of modern warming or that it doesn’t exist at all. While it is undeniable (and fairly easily shown) that both the site characteristics and surrounding area have a measurable impact on the absolute temperature of the station, teasing out the actual effects of changes in urban form over time on temperature is notoriously difficult. Its hampered both by the lack of good historical metadata for many stations and basic disagreements on the best methods to use to determine the marginal contribution of UHI to the trend.

The article will provide a novel examination of UHI impacts on global temperature trends from the Global Historical Climatological Network (GHCN) using three different proxies for urbanity: subjective classifications of stations as urban or rural in the station metadata, classifications of stations as “dark” or “bright” based on nighttime satellite observations, and the year 2000 population density at the station location via the Gridded Population of the World Version 3 database. All of these represent a single snapshot in time, and don’t show changes in urban characteristics over time. However, if we make the reasonably conservative assumption that areas rarely de-urbanize, the vast majority of UHI effects over the past few decades should appear in those stations classified as urban/bright/high density if these classifications are reasonably accurate.

Read more »

The “toy planet” discussion of the “basic” anomaly method in its most idealized form was such a hit, I’ve decided to discuss how the ‘bias’ method used in section (3) of Hansen and Lebedeff 1987 — the method actually used by GISTemp– might be affected by deal with “the march of the thermometers”. One reason I am discussing GISTemp specifically is that Chiefio says:

Roy Spencer posted two versions of the UAH temperature of the lower troposphere! According to version 5.3, the temperature of the lower troposphere was 0.63C; according to version 5.2, it was 0.74C. Despite the fact that I my bet of 0.64 would have been closer to correct using the splendid new version 5.3, we will be using version 5.2 to distributed quatloos.

This makes PEHarvey this month’s top winner: he was who was bang on correct! Congratulations. Spend those quatloos wisely.

I am still able to access the detailed values for version 5.2 here; as usual, I tacked the reading Roy posted for version 5.2 onto the record and plotted temperatures and trends from the inception of the record, since 2000 and since 2001. I’ve also highlighed all Januaries and ran a trace so you can see how many months had temperatures exceeding the most recent one:

Figure 1: UHA TLT with OLD system.

As some of my readers know, when someone raises a criticism of a particular widely used result in climate science, I often like to try to understand the main issue and discuss it using a toy problem or “cartoon”. That is: I like to create an artificial problem that highlights the issue that someone claims is causing a problem, and then explore whether or not that issue could either a) never-ever-ever possibly cause a problem, b) could cause a problem in some specific circumstances and c) if (b) figure out how close to that circumstance we might be. These sorts of abstract cartoon problems are actually very important to engineering– but I digress.

This post builds on a simple spatial gridding model outlined here. As always, the latest source code can be found at http://drop.io/0yhqyon, and I welcome folks helping improve it.

If we are looking at the effect of adjustments and station quality, we should really be looking at maximum and minimum temperature data rather than mean data, because mean data simply averages the max/min temps and a fair bit of information is lost in the process.

Read more »

Many in comments have suggested that some of the rebuttals to the claim that the march of the thermometers actually biases temperature anomalies reported by NOAA, GISSTemp and CRU may be unnecessary. How so, it’s possible no one has really made that claim. If so, the ‘rebuttals’ would be countering strawmen (i.e. arguments that have never been made by anyone).

This is a Sponsored Post written by me on behalf of AccuWeather. All opinions are 100% mine.

As many climate-blog addicts are aware, weather is not climate. While we are all busy arguing whether this years DC snowstorm was caused by climate change, those in DC were probably just thankful for online services like AccuWeather which could give them advance warming about impending snowmaggedon.

Today, I was lucky enough to learn of Accuweather’s new site, which is still in beta.

Visiting, I entered my zip code to learn it’s a sunny 38F in Lisle; temperatures will drop to 24F tonight. More importantly to me, the monthly forecast predicts we’ll hit the 50s late in March. This means I may finally be able to erect my portable green-house and start some of the less tender annuals soon. I’m planning to buy seeds and potting soil this week. I start these in the window, but I do like to have some advance indications of weather conditions plant schedule actually starting the seeds accordingly. (Lucky for me I’m not planning to go to Florida; an Accuweather story also warns “Breaking Weather: Chilly for Spring Breakers”.)

The site also has some useful forecast maps for the lower 48, including forecasts for rain, snow, UV, windchill, heat index and a number of other indicators as well as slick radar and satellite weather displays of current weather equipped with both “zoom” and “play” features.

But enough about the weather! Accuweather now also links to their a global-warming area, which provided links to Brett Anderson’s blog. Guess what? The first comment on the most recent blog was placed by Patrick AKA Cyclonebuster! If you visit, you can have a chat with our old friend.

Read more »

I have to admit I initially missed the whole “march of the thermometers results in overwhelming bias” and now I’m trying to put together a time-line. Mind you, I knew this meme was out there, but since there has never been any convincing evidence the march of the thermometers actually caused any large bias in the reported surface temperatures, I never expected it to get quite as much play as it did. I guess if subscribed to cable TV, I would have noticed this meme had hit the big time sooner.