Seeing the environmental forest

Christopher Booker wrote a highly deceptive piece in The Telegraph on temperature adjustments in Paraguay and elsewhere around the world.  His implication is that scientists have fraudulently adjusted temperature records to show warming when there really is none.

This is an update to my previous post on this topic, which was based largely on January-November data.  The full-year data is rolling out and no matter who is measuring, 2014 was a hot year for the Earth.

Back in August, I wrote a post that found the January-June period was the third hottest on record ( based on the Cowtan-Way data set which corrects the coverage bias in HadCRUT4 data).  This post will revise and update that earlier article, incorporating GISS, UAH, NCDC, HadCRUT4, and Cowtan-Way data sets.  I am not including RSS, as that data set has shown false cooling since 2000.

Claims that 95% of climate models are wrong have been making the rounds since Spencer published it on his blog in February.  Here's the graph he created: Take a good look.  Not only does his graph appear to show that most models are higher than both HadCRUT4 and UAH satellite temperature record but it shows that HadCRUT4 is higher than UAH as well.  That is...strange, to say the least.  IPCC AR5 (aka CMIP5) models were calibrated against 20th century temperatures (1900-1999) and have only been actually predicting temperatures since 2000.  However, Spencer's graph appears to show that their output is higher than the observed temperature records for 1983-1999—during the calibration period.  That makes no sense at all.

Much of the debate over the possible pause in surface temperatures since 1998 really hinges on 1998 being an outlier.  And not only an outlier but an influential data point, which means that its very presence changes the overall regression trend.  In this post, I'll show how to identify outliers, high-leverage data points, and influential data points. First, some basic definitions.  An outlier is any data point that falls outside the normal range for that data set, usually set as being 2 standard deviations from the average.  In regression analyses, an outlier is any data point where its residual falls outside the normal range.  High leverage data points are made at extreme values for the independent variables such that there are few other data points around, regardless of whether or not those data points change the overall trend.  An influential data point is an extreme outlier with high leverage that alters the overall trend. Now for the analysis, sta...

One common misunderstanding about how the current global warming differs from past episodes of warming is the rate of warming.  In this post, I'll show how the rate over the past 30 years stacks up with two of the better-known rates from geologic history. Past 30 years (1983-2013) rate ± standard error: UAH: +0.015379 ± 0.003783ºC per year GISS: +0.015505 ± 0.002491ºC per year NCDC: +0.014454 ± 0.002489ºC per year HadCRUT4: +0.014896 ± 0.002824ºC per year Depending on the data set, the rate of the last 30 years ranges from 0.014454ºC per year up to 0.015505ºC per year.  When I average the four data sets together then calculate the rate, the result is +0.014692 ± 0.003070ºC per year for the last 30 years. For the geologic rates, let's start with the most recent and work backwards in time. Over the 5,000 years since the end of the Holocene Climatic Optimum, the Earth slowly cooled by 0.7ºC ( Marcott et al. 2013 ).  That's an average rate of  -0.00014ºC...

Let me be blunt: There is little evidence that global warming stopped in 1998 or any year thereafter.  Most of the evidence we have, from the energy imbalance to total heat content to ocean heat content, show that global warming continues, as I previously explained here , here , and here .  The only piece of evidence that appears to show that global warming has stopped is that the trend in surface temperature data is not statistically significant in recent years.  However, that is at best ambiguous.  No significant trend could mean that warming continues but short-term variation in the data masks the trend, that there's no warming or that there's a cooling trend but not enough data for that to be significant.  There's no real way to tell unless you either a) add enough data for short-term variation to cancel out or b) use statistical techniques to factor out the known natural variation. In this article, I expand on my previous analyses of surface temperature, ...

As I've already covered in the past ( here , here , and here ), the claim that the Earth hasn't warmed since 1998 is pure bunk.  Today, I re-ran a linear regression analysis and discovered that UAH satellite temperature data now shows statistically significant warming since 1998:

A couple days ago, a fellow user on a different forum challenged me with the following information while disputing whether or not the Earth is still warming:

First, an introduction.  This blog will cover topics in ecology and environmental science.  That is, after all, my background.  I hold Bachelor's and Master's degrees in Biology, with emphasis on ecology, and am completing a Ph.D. in Environmental Science.  My research focus covers forest ecology but my training runs the gamut from calculating the spread and fate of pollutants in groundwater to creating watershed models to population ecology to ecosystem cycles.  My teaching experience is on the college level, having taught courses ranging from general biology to physiology to ecology, environmental science, and evolution. One of the first questions I'm usually asked when people find out my background is "Is global warming real?"  My answer is "Yes, it's real–and it's caused by humans."  In this post, I'll explore the data that shows that the Earth is warming.  I'll get into the data why we're the cause in subsequent posts.