Comparison of UAH and GISS Time Series with Common Baseline

In this post I set both UAH and GISS global temperature anomaly series to a common baseline period (1981-2010)  and compare them. Even though the UAH series is satellite based and GISS series is station based, the series exhibit striking similarities.

Common Baseline

In this previous post, I showed how to convert temperature anomaly time series from one baseline period to another period.  I use this technique in this post to directly compare UAH (baseline 1981-2010) and GISS (baseline 1951-1980) series.

The offsets are as follows:

• UAH:  -0.000978
• GISS: 0.34958

Since the UAH TLT 5.4 series is based on a 1981-2010 baseline, the offset is nearly zero (-0.00098 versus 0.0).

Users can reproduce my analysis on their own by downloading my CTS.csv file and applying the offsets to the UAH and GISS series.

Comparison of 1981-2010 Baseline Series

Here is a plot of UAH and GISS 12 month moving averages for 1979 to current: Click to Enlarge

UAH Temperature Anomalies Following Predictable Pattern

In this post I show one simple  and 2 multiple regression models to assess the role of time, El Nino – La Nina SSTA and volcanic activity (SATO) on UAH global temperature anomaly trends. The 3rd model provides a reasonable  approximation of the actual UAH oscillations over the 1979 – Feb, 2011 period.

Click Image to Enlarge

This analysis is similar to previous temperature anomaly regressions (here, here, here) that I have done.

The simple trend line regression shows the overall trend is upward, however, there are several oscillations that the linear trend misses.  The yr_frac and Nino34 regression improves on the linear model, however, it undershoots in the early 1980s,  overshoots in the 1992-1994 period, periods following significant volcanic activity.

The yr_frac, Nino34 and SATO model improves the fit in the early 1980s and 1992-1994 period and is slightly better in the 1998 and 2010 El Nino periods.

The 3rd model matches the observed 2010 El Nino – La Nina oscillation pretty well so far, indicating that the 2010 – 2011 UAH anomalies are following a predictable pattern.

Comparison of UAH Anomalies During 1998 & 2010 El Nino – La Nina Oscillations

In this post, I show the UAH global temperature anomaly traces during the 1998 and 2010 El Nino – La Nina oscillations.

Click Image to Enlarge

The 1998 UAH anomalies were higher in the peak El Nino period than the comparable 2010 El Nino peak period.  The anomaly drop off from the peaks have been comparable so far.  It will be interesting to see how the current La Nina progresses compared to the  12 month depressed anomaly period following the 1998 El Nino.

Comparison of GISS LOTAs During 5 El Nino – La Nina Cycles

In this post I compare GISS LOTAs during 5 El Nino – La Nina cycles (2010, 1998, 19883, 1973 and 1970).

El Nino – La Nina Cycles

In a previous post I showed the Nino 34 SSTA cycles for 2010, 1998, 1983, 1973 and 1970 here. In this post, I want to see how GISS Land Ocean Temperature Anomalies (LOTA) vary over El Nino – La Nina cycles.  Here is my RClimate chart showing GISS anomalies for 6 months prior to cycle year,the cycle year and the 12 months after cycle year (30 month period).

Click chart to enlarge

While the 2010 cycle is only partially complete, there are a number of interesting aspects in this chart. The average temperatures during the cycles have clearly risen with the latest cycle showing the highest maximum anomaly. All 5 cycles all have similar patterns, with a buildup in 6 months prior to cycle year. The maximum – minimum range for the 5 cycles are comparable, ranging from 0.45 (2010) to 0.60 (1998).

Here is a data summary of the 5 cycles.

RClimate Links

• RClimate script:   link
• Data: link

September 2011 Arctic Sea Ice Extent Forecast

In this post, I use a quadratic regression model to forecast the  September, 2011  Arctic Sea Ice Extent. The model was developed with  1980 – 2010 data. Links to the R script, source data and  how-to article on polynomial regression are provided.

Arctic Sea Ice Extent Forecast for September, 2011

First, here is my forecast: (Click image to enlarge)

Based on the 1980 – 2010 downward Arctic Sea Ice trend,  my forecast is that September, 2011 SIE will decline  0.36 below 2010 levels, to 4.54 million km^2, with a confidence band of +- 0.59.

How Did I Develop My Forecast?

I have written a number of posts on Arctic Sea Ice Extent (here, here, here). In this post, I used the NSDIC‘s monthly data file (link)  to construct a quadratic regression model of September sea ice extent for the 1980 – 2010 period. I then used this model to predict the September, 2011  Arctic Sea Ice Extent.

I have 2 main learning curve sources for this model:

• Tamino‘s post on Arctic Sea Ice decline provided the basic idea of using a quadratic model to fit Arctic SIE decline.

• John Quick’s tutorial on polynomial regression provided the how-to instructions I needed to implement Tamino’s approach in R.

RClimate Script and Links

Here is the link to my RClimate script.

Assessing Climate Trends: Eyeball versus Regression

In a previous post, I showed the Lower Stratospheric Temperature Anomaly (TLS) Trends (link).  A reader submitted the following comment:

“The lower stratosphere temperature profile is essentially flat from ca. 1995 to the present. This approximately mirrors the temperature trend for the surface temperature. From 1980 to about 1995, the surface temperature increased while the lower stratospheric temperature decreased. After that both went flat.” tony

In the words of Edwards Deming:

In God we Trust, All Others Must  Bring Data”

Since tony didn’t bring any data to back up his  claims, I’ll do the analysis for him.

Atmospheric Temperature Structure : 2 – Stratospheric Cooling

In this  post I review the temperature structure of the atmosphere and lower stratosphere temperature (TLS) anomaly trends.

Temperature Structure in the Atmosphere

In post 1 of this series, I developed this RClimate chart of temperature soundings which I update daily: (Click to enlarge)