One of the stunning coincidences is that Andrew Bolt and I are born within a few hours of each other. While similar in many ways (e.g. born on 26th Sep 1959, both have European born fathers), we differ in our understanding of climate change.
I thought I might help him with a little science-based information on his favourite subject (I am a scientist afterall) by putting together the following birthday card:
So, happy 50th birthday Andrew!
I am still waiting for the copy of the synthesis report of the 4th assessment of the IPCC that I have ordered for Andrew’s birthday to arrive. I hope he reads and learns from it, but after all, this is the same Andrew Bolt who said “I am not a scientist, and cannot have an informed opinion on your research”. Thanks to Global Warming Art for the graphs used above.
Edit: Sources for the above graphs from Global Warming Art (1-5, 7) except for (6), which is sourced from Climate Change in Australia:
1. The instrumental record of global average temperatures as compiled by the NASA‘s Goddard Institute for Space Studies. The data set used follows the methodology outlined by Hansen et al. (2006). Following the common practice of the Intergovernmental Panel on Climate Change, the zero on this figure is the mean temperature from 1961-1990.
The uncertainty in the analysis of global temperature is discussed in Foland et al. (2001) and Brohan et al. (2006). They estimate that global averages since ~1950 can be expected to be within ~0.05 °C of reported values with 95% confidence. In the recent period, these uncertainties are driven primarily by considering the potential impact of regions where no temperature record is available. For averages prior to ~1890, the uncertainty reaches ~0.15 °C driven primarily by limited sampling and the effects of changes in sea surface measurement techniques. Uncertainties between 1980 and 1890 are intermediate between these values.
Incorporating such uncertainties, Foland et al. (2001) estimated the global temperature change from 1901 to 2000 as 0.57 ± 0.17 °C, which contributed to the 0.6 ± 0.2 °C estimate reported by the Intergovernmental Panel on Climate Change (IPCC 2001a, ). Both estimates are 95% confidence intervals.
2. This figure compares the global average surface temperature record, as compiled by Jones and Moberg (2003; data set TaveGL2v with 2005 updates), to the microwave sounder (MSU) satellite data of lower atmospherictltglhmam version 5.2 with 2005 updates) and Schabel et al. (RSS 2002; data set tlt_land_and_ocean with 2005 updates). These two satellite records reflect two different ways of interpreting the same set of microwave sounder measurements and are not independent records. Each record is plotted as the monthly average and straight lines are fit through each data set from January 1982 to December 2004. The slope of these lines are 0.187°C/decade, 0.163°C/decade, and 0.239°C/decade for the surface, UAH, and RSS respectively.
It is important to know that the 5.2 version of Christy et al.’s satellite temperature record contains a significant correction over previous versions. In summer 2005, Mears and Wentz (2005) discovered that the UAH processing algorithms were incorrectly adjusting for diurnal variations, especially at low latitude. Correcting for this problem raised the trend line 0.035°C/decade, and in so doing brought it into much better agreement with the ground based records and with independent satellite based analysis (e.g. Fu et al. 2004). The discovery of this error also explains why their satellite based temperature trends had disagreed most prominently in the tropics.
3. This figure shows the change in annually averaged sea level at 23 geologically stable tide gauge sites with long-term records as selected by Douglas (1997). The thick dark line is a three-year moving average of the instrumental records. This data indicates a sea level rise of ~18.5 cm from 1900-2000. Because of the limited geographic coverage of these records, it is not obvious whether the apparent decadal fluctuations represent true variations in global sea level or merely variations across regions that are not resolved.
For comparison, the recent annually averaged satellite altimetry data  from TOPEX/Poseidon are shown in red. These data indicate a somewhat higher rate of increase than tide gauge data, however the source of this discrepancy is not obvious. It may represent systematic error in the satellite record and/or incomplete geographic sampling in the tide gauge record. The month to month scatter on the satellite measurements is roughly the thickness of the plotted red curve.
4. This figure shows the change in average thickness of mountain glaciers around the world. This information, known as the glaciological mass balance, is found by measuring the annual snow accumulation and subtracting surface ablation driven by melting, sublimation, or wind erosion. These measurements do not account for thinning associated with iceberg calving, flow related thinning, or subglacial erosion. All values are corrected for variations in snow and firn density and expressed in meters of water equivalent (Dyurgerov 2002).
Measurements are shown as both the annual average thickness change and the accumulated change during the fifty years of measurements presented. Years with a net increase in glacier thickness are plotted upwards and in red; years with a net decrease in glacier thickness (i.e. positive thinning) are plotted downward and in blue. Only three years in the last 50 have experienced thickening in the average.
Systematic measurements of glacier thinning began in the 1940s, but fewer than 15 sites had been measured each year until the late 1950s. Since then more than 100 sites have contributed to the average in some years (Dyurgerov 2002, Dyurgerov and Meier 2005). The percentage of measurement sites at which net thinning has been observed averages two-thirds over this interval, and reached a maximum of 96% in 2003 (Dyurgerov 2005). Error bars indicate the standard error in the mean.
Specifically, Knutson & Tuleya performed an experiment using climate models to estimate the strength achieved by cyclones allowed to intensify over either a modern summer ocean or over an ocean warmed by carbon dioxide concentrations 220% higher than present day. A number of different climate models were considered as well as conditions over all the major cyclone forming ocean basins. Depending on site and model, the ocean warming involved ranged from 0.8 to 2.4 °C.
Results, which were found to be robust across different models, showed that storms intensified by a about one half category (on the Saffir-Simpson Hurricane Scale) as a result of the warmer oceans. This is accomplished with a ~6% increase in wind speed or equivalently a ~20% increase in energy (for a storm of fixed size). Most significantly these result suggest that global warming may lead to a gradually increase in the probability of highly destructive category 5 hurricanes.
This work does not provide any information about future frequency of tropical storms. Also, since it considers only the development of storms under nearly ideal conditions for promoting their formation, this work is primarily a prediction for how the maximum achievable storm intensity will change. Hence, this does not directly bare on the growth or development of storms under otherwise weak or marginal conditions for storm development (such as high upper level wind shear). However, it is plausible that warmer oceans will somewhat extend the regions and seasons under which hurricane may develop.
6. Projections are given relative to the period 1980-1999 (referred to as the 1990 baseline for convenience). The projections give an estimate of the average climate around 2030, 2050 and 2070, taking into account consistency among climate models. Individual years will show variation from this average. The 50th percentile (the mid-point of the spread of model results) provides a best estimate result. The 10th and 90th percentiles (lowest 10% and highest 10% of the spread of model results) provide a range of uncertainty. Emissions scenarios are from the IPCC Special Report on Emission Scenarios. Low emissions is the B1 scenario, medium is A1B and high is A1FI.
7. This image is a comparison of 10 different published reconstructions of mean temperature changes during the last 1000 years. More recent reconstructions are plotted towards the front and in redder colors, older reconstructions appear towards the back and in bluer colors. An instrumental history of temperature is also shown in black. The medieval warm period and little ice age are labeled at roughly the times when they are historically believed to occur, though it is still disputed whether these were truly global or only regional events. The single, unsmoothed annual value for 2004 is also shown for comparison. (Image:Instrumental Temperature Record.png shows how 2004 relates to other recent years).
It is unknown which, if any, of these reconstructions is an accurate representation of climate history; however, these curves are a fair representation of the range of results appearing in the published scientific literature. Hence, it is likely that such reconstructions, accurate or not, will play a significant role in the ongoing discussions of global climate change and global warming. For each reconstruction, the raw data has been decadally smoothed with a σ = 5 yr Gaussian weighted moving average. Also, each reconstruction was adjusted so that its mean matched the mean of the instrumental record during the period of overlap. The variance (i.e. the scale of fluctuations) was not adjusted (except in one case noted below).
Edit #2:As per ilajd’s comment below, here is the Hadley CRUT3V adjusted for UHI (scale adjusted to match graph #1)