Lessons from Past Climate Predictions: IPCC AR4

Note: SkS commenters identified a flaw in this post, and it has now been updated.  This version is out of date - the updated post is here.

In 2007, the IPCC published its Fourth Assessment Report (AR4).  In the Working Group I (the physical basis) report, Chapter 8 was devoted to climate models and their evaluation.  Section 8.2 discusses the advances in modeling between the Third Assessment Report (TAR) and AR4.

"Model improvements can...be grouped into three categories. First, the dynamical cores (advection, etc.) have been improved, and the horizontal and vertical resolutions of many models have been increased. Second, more processes have been incorporated into the models, in particular in the modelling of aerosols, and of land surface and sea ice processes. Third, the parametrizations of physical processes have been improved. For example, as discussed further in Section 8.2.7, most of the models no longer use flux adjustments (Manabe and Stouffer, 1988; Sausen et al., 1988) to reduce climate drift."

In the Frequently Asked Questions (FAQ 8.1), the AR4 discusses the reliability of  models in projecting future climate changes.  Among the reasons it cites that we can be confident in model projections is their ability to model past climate changes in a process known as "hindcasting".

"Models have been used to simulate ancient climates, such as the warm mid-Holocene of 6,000 years ago or the last glacial maximum of 21,000 years ago (see Chapter 6). They can reproduce many features (allowing for uncertainties in reconstructing past climates) such as the magnitude and broad-scale pattern of oceanic cooling during the last ice age. Models can also simulate many observed aspects of climate change over the instrumental record. One example is that the global temperature trend over the past century (shown in Figure 1) can be modelled with high skill when both human and natural factors that influence climate are included. Models also reproduce other observed changes, such as the faster increase in nighttime than in daytime temperatures, the larger degree of warming in the Arctic and the small, short-term global cooling (and subsequent recovery) which has followed major volcanic eruptions, such as that of Mt. Pinatubo in 1991 (see FAQ 8.1, Figure 1). Model global temperature projections made over the last two decades have also been in overall agreement with subsequent observations over that period (Chapter 1)."

AR4 hindcast

Figure 1: Global mean near-surface temperatures over the 20th century from observations (black) and as obtained from 58 simulations produced by 14 different climate models driven by both natural and human-caused factors that influence climate (yellow). The mean of all these runs is also shown (thick red line). Temperature anomalies are shown relative to the 1901 to 1950 mean. Vertical grey lines indicate the timing of major volcanic eruptions.

Projections and their Accuracy

The IPCC AR4 used the IPCC Special Report on Emission Scenarios (SRES), which we examined in our previous discussion of the TAR.  As we noted in that post, thus far we are on track with the SRES A2 emissions pathChapter 10.3 of the AR4 discusses future model projected climate changes, as does a portion of the Summary for Policymakers.  Figure 2 shows the projected change in global average surface temperature for the various SRES.

AR4 projections

Figure 2: Solid lines are multi-model global averages of surface warming (relative to 1980–1999) for the scenarios A2, A1B, and B1, shown as continuations of the 20th century simulations. Shading denotes the ±1 standard deviation range of individual model annual averages. The orange line is for the experiment where concentrations were held constant at year 2000 values. The grey bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the six SRES marker scenarios.

We digitized Scenario A2, the red line in Figure 2, and compared it to the observed global surface temperature change as measured by NASA GISS (Figure 3).

AR4 projections

Figure 3: IPCC AR4 Scenario A2 model projections (blue) vs. GISTEMP (red) since 1990

The shape of the model projections is simply a result of the model averaging.  However, the linear global warming trend since 1990 is 0.22°C per decade for the models, vs. 0.19°C per decade for the observational data.  The model projections actually begin in 2000.  The trends over that period are 0.12°C (projected) vs. 0.15°C (observed) per decade.

What Does This Tell Us?

The IPCC AR4 was only published a few years ago, and thus it's difficult to evaluate the accuracy of its projections at this point.  The Scenario A2 simulations do show that over periods as short as a decade, it's not unusual for climate models to project a slowed rate of global surface warming.  In fact, while climate "skeptics" have made a lot of fuss over the slowed warming over the past decade, the IPCC AR4 model average actually projected an even lower rate of warming for the period 2000 to 2010 with Scenario A2.  Although this slowed projection was merely coincidental, it shows once again that we must evaluate long-term trends longer than a single decade.

Section 10.5.2 of the report discusses the sensitivity of climate models to increasing atmospheric CO2.

"Fitting normal distributions to the results, the 5 to 95% uncertainty range for equilibrium climate sensitivity from the AOGCMs is approximately 2.1°C to 4.4°C and that for TCR [transient climate response] is 1.2°C to 2.4°C (using the method of Räisänen, 2005b). The mean for climate sensitivity is 3.26°C and that for TCR is 1.76°C."

Thus the accuracy of the IPCC AR4 projections thus far provides yet another piece to the long list of evidence that equilibrium climate sensitivity (including only fast feedbacks) is approximately 3°C for doubled CO2.  However, it will take at least another decade of data to adequately evaluate the accuracy of the 2007 IPCC projections.

Posted by dana1981 on Thursday, 22 September, 2011

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