We recently re-examined the physical reality that global warming continues unabated. We have also previously examined a number of studies demonstrating that the observed global warming has primarily been caused by humans, for example by looking for human 'fingerprints' in global warming patterns (Figure 1), or by using physics, statistics, and/or climate models to determine the causes of the warming.
Figure 1: Various human-caused global warming 'fingerprints'
In this post we examine a paper published in Climate Dynamics, Drost, Karoly, and Braganza 2012 (DKB12), which uses the former approach, looking for specific 'fingerprints' of human-caused global warming.
DKB12 notes that there are several measurements of global-scale temperature variations besides average global surface air temperature (GM) which can be used to distinguish between natural and human-caused global warming. Some of these indices include:
"...the land–ocean temperature contrast (LO), the Northern Hemisphere meridional temperature gradient (MTG), the magnitude of the annual cycle of average temperatures over land (AC) and the hemispheric temperature contrast (NS)"
These measurements have been previously used to show that humans are the primary cause of the current global warming (e.g. Braganza et al. 2003 and 2004). DKB12 expands on those previous studies to include data from the past 10 years and determine if evidence for the human-caused 'fingerprints' has grown, and also to test the accuracy of climate models from the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 (CMIP3) in predicting these changes.
DKB12 uses temperature data from GISS, NCDC, and HadCRUT3v, and they examined a subset of the CMIP3 models:
"...that had submitted at least one simulation for the Pre-Industrial Control scenario (PICNTRL) and multiple simulations for the twentieth century (20C3M) and emission scenario A1B (SRESA1B), a midrange future emission scenario. The reason for restricting the analysis to data only from models that fit these criteria is that multiple output from the same model for the 20C3M and A1B scenario will provide an indication of the model’s internal variability."
DKB12 notes that Braganza et al. (2004) was able to detect statistically significant trends in their analysis of the indices over the period 1950–1999 in both observations and model data, so they compare those results to the most recent 50-year period (1961-2010) to determine if the trends have now become even more statistically significant.
First they ran control simulations to provide the 5–95% confidence interval for natural variability of 50-year trends for each index for a single climate realization. DKB12 notes that
"If observational trends in the indices are outside this interval then it is most likely that they can not be attributable to natural variability."
In other words, if the trend in these indices is larger than the spread of trends in the control runs which don't include a human-caused global warming component, then those trends are probably not due to natural variability alone.
The models and data for each index are compared in Figure 2, and the trends are compared in Figure 3.
Figure 2: The temporal evolution of the mean (dash-dot line), one standard deviation (dark grey shaded area), and the minimum and maximum range (light grey shaded area) of the indices determined for all historical simulations for the 8 models. The 3 thin light-grey lines in each graph are the values for the indices derived from the 3 observational datasets used in this study. The twentieth century simulation data were extended with data from the SRESA1B simulations. Figure 3 from Drost, Karoly, and Braganza 2011.
Figure 3: Trends in the indices in the observations (A, B, C) and in all the historical simulations of the model data (1–8) for the period 1961–2010 at annual time scales. Listed along the x axis are: Observations (black squares) A = NCDC, B = HadCRUT3v, C = GISS. Models (grey circles): The numbers refer to the models as listed in Table 1 of DKB12. The shaded area marks the 5–95% confidence interval for no trend in each index. Figure 4 from Drost, Karoly, and Braganza 2011.
Comparing the black squares (trends observational data) to the shaded area (5-95% confidence interval in the control runs) in Figure 3 is the key in determining whether the observed trends in these indices can be attributed to human influences. DKB12 concludes as follows:
It's worth noting that for NS, MTG, and AC, the HadCRUT3v trend (labeled 'B' in Figure 2) does not fall outside of the 'no trend' 95% significance level, but HadCRUT3v has also been replaced by HadCRUT4 and is therefore the least reliable of the three observational datasets, having a known cool bias in recent decades. DKB12 concludes:
"...these results indicate that the observational trends in the indices have gained significance over the last decade. Furthermore, as this analysis uses three observational datasets, our results have higher confidence as our findings are in general robust across the three datasets."
Regarding the accuracy of models in simulating the changes in these indices (grey circles vs. the shaded region in Figure 3), DKB12 notes,
"Although the range of trends as simulated by the models cover the range of possible trends as indicated by the observational data quite well, there is a tendency for some models to overestimate the trend in GM and underestimate the trend in LO."
Some models (particularly cccma_cgcm3_1 [1 in Figure 3] and ncar_ccsm3_0 [6 in Figure 3]) predict more overall global surface warming than observed, although most models simulate the observed average global surface warming accurately. Due to those overpredictions, on average the models simulate a 0.167°C per decade average global surface warming trend from 1961-2010, whereas the observed trend is approximately 0.138 ± 0.028°C per decade, approximately 20% lower.
Some models (particularly mpi_echam5 [4 in Figure 3] and mri_cgcm2_3_2a [5 in Figure 3]) do not adequately simulate the larger surface warming over land as compared to the warming over the oceans.
The 95% significance level for MTG and AC lies within the margin of error of the multi-model ensemble mean trends (1.04 ± 0.13°C/100 year for MTG and -0.44 ± 0.07°C per century for AC), so the multi-model ensemble mean trends in these indices are either significant at, or very near the 95% significance level. However, there is a wide spread in individual model simulations for both the Northern hemisphere meridional temperature gradient trend and the trend in the annual cycle magnitude.
The mean value of all the trends in NS in the simulations is 0.46 ± 0.05°C per century. This means that the multi-model ensemble mean trend in the hemispheric temperature contrast is significant at the 95% level, although similar to MTG and AC, there is a wide spread between individual model NS trend simulations.
The ratio of LO changes (RLO) is a key in looking for a human 'fingerprint'. If there is no radiative forcing, we expect to see a ratio of 1, whereas under global warming scenarios we expect a ratio of greater than 1. Joshi and Gregory (2008) showed that RLO varies significantly depending on whether changes in radiative forcings were due to CO2 changes or to natural changes. DKB12 examines the recent RLO trends in both observations and models:
"The mean values for RLO over the period 1990–2010 in the observational datasets are 1.69 (GISS), 1.40 (NCDC) and 1.39 (HadCRUT3v)....The [multi-model] mean value for RLO at 2010 is 1.54 ± 0.04 which sits well within the range of values determined from the observational data."
Overall, DKB12 finds increased evidence for human-cased global warming compared to the Braganza studies last decade.
"This increased evidence can be described in two ways. Qualitatively we see increased evidence as the multiobservational mean trend in the indices GM, LO, MTG, and AC are all outside the 5–95% confidence interval for natural variability of 50 year trends. The same statement can nearly be said of NS as well, except that the uncertainty estimate of the multi-observational mean trend in NS overlaps with our estimate of the range of intrinsic variability in the index. The fact that the trends in these observational indices have higher significance than in Braganza et al. (2004) reflects increased evidence for anthropogenic climate change."
"...there is also increased evidence for anthropogenic climate change from a [quantitative] point of view as we have greatly increased the amount of data on which we have applied the analysis and we find consistently similar results among all observational and model data. Evaluating all results together has increased our confidence that changes in the climate indices are statistically significant and, following from the attribution studies of Braganza et al. (2003; 2004), that such changes are very likely caused by anthropogenic gas emissions.
This finding is further supported by the analysis of the sixth index, the ratio of warming over land to that over the oceans"
While these results are consistent with previous studies, the most interesting aspect is that this increased evidence for human-caused global warming comes at a time when the average warming of surface air temperatures has temporarily slowed. DKB12 also shows that while there is a wide spread in model simulations of some of these indices, on average the model runs accurately simulate these human global warming fingerprints.
Posted by dana1981 on Monday, 11 March, 2013
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