Temperature Reconstructions


In this section, the reconstructions of MBH98, the associated uncertainties, and raw data used in calibration and verification are available in a variety of formats.

A. Large-Scale Trends   B. Spatial Patterns   C. Influence of Climate Forcings

 


C. Influence of Climate Forcings

The statistical relationship between variations in northern hemisphere (NH) mean temperature and estimates of the histories (see MBH98) of solar, greenhouse gas, and volcanic forcings is shown below. (See here for a note about the difference between the plot shown below and that shown in MBH98.)

 

NH mean temperatureFigure 17:
Relationship of annual-mean Northern hemisphere mean (NH) temperature reconstruction to estimates of three candidate forcings (see MBH98) between 1610 and 1995.


Click here or on image for better viewing and access to data.

The time axis denotes the center of a 200 year moving correlation window. Significance levels are based on the null hypothesis that the surface temperature series is a realization of natural variability represented as represented by a red noise process with the persistence structure of the observed NH series (see MBH98 for details). One-sided significance levels for correlations with the different forcing agents are shown, under the assumption that only positive relationships with GHG and CO2, and negative relationships with DVI, are physically meaningful. These confidence levels are approximately constant over time, and are thus represented by their average values over time for simplicity [although the number of degrees of freedom in the CO2 series is somewhat decreased prior to 1800 when the series is essentially flat, so that the confidence intervals are slightly too liberal in this case]. Significance levels for correlations of temperature with CO2 and solar irradiance are nearly identical, and the 90%,95%, and 99% (positive) significance levels are shown by the horizontal dashed lines. The 95% (negative) significance level for DVI is shown by a horizontal dotted line. The lower dotted line indicates the 99% significance level for correlation with GHG if a two-sided hypothesis test is invoked [this is only added to emphasize that the seemingly spurious negative correlation of NH with GHG apparent during the late 18th/early 19th century is in fact not statistically significant if the a priori physical requirement of a positive relationship between CO2 and temperature is not taken into account in hypothesis testing]. The gray bars indicates two different 200 year windows of data in the moving correlation, with the long-dashed vertical lines indicating the center of the corresponding windows.

 

While the natural (solar and volcanic) forcings appear to be important factors governing the natural variations of temperatures in past centuries, only human greenhouse gas forcing alone, as noted by MBH98, can statistically explain the unusual warmth of the past few decades. The possible influences of regional industrial aerosol cooling during the latter part of the 20th century (see IPCC95) were not included in our attribution analysis, and this cooling may in fact mask an even stronger greenhouse gas signal during the past few decades.

MBH98 noted that the contemporaneous (ie, zero-delay) response to forcings implicit in their statistical attribution analysis may underestimate the true, lagged responses to forcing. Volcanic responses appear to be slightly greater and more consistently significant about 1 year following the eruption (see Briffa et al, 1998), while the response of the climate to global radiative forcings should be significantly delayed (e.g., 10-20 years based on most sensitivity estimates--see IPCC95) by the thermal inertia of the oceans. Below, we investigate possible such lagged relationships to forcing. We also examine the sensitivity of the time-dependent attribution approach discussed above to employing a shorter (100 year) window. A complimentary approach to the attribution of forcings involves the use of climate model forced with estimated histories of greenhouse, volcanic, and solar radiative forcings, to estimate the expected large-scale temperature trends in past centuries. Preliminary results of such an experiment (Robertson et al, 1998) show a favorable comparison with our hemispheric temperature reconstructions.

 

As above, but employing varying value of lag of temperatures relative to forcing - click here for larger image.Figure 18:
Relationship between NH series and forcings as above, but employing varying value of lag of temperatures relative to forcing. Symbols are same as above. The first 5 panels make use of the 200 year window used above. In the first panel, we repeat the zero-lag case shown above for comparison, while the 2nd panel shows the results for 1 year lag, the 3rd panel 10 year lag, and the 4th panel 15 year lag. The 5th panel shows the results based on employing 100 year moving window in the time-dependent attribution analysis, and with a 15 year lag in the relationship of temperature to forcings. For lags much larger than 1 year, the relationship of temperatures to volcanic forcing is not physically meaningful, and is quite small. Thus, the relationship to volcanic forcing is not shown in the 3rd, 4th, and 5th panels.

 

From the above analysis it is clear that when physically reasonable lags are incorporated into the attribution analysis, there is evidence of even greater statistical relationships with particular forcings. At the physically expected lag of 1 year, the relationship between temperature variations and volcanic forcing is slightly more consistent and significant (at or near 95% significant for much of the interval examined, in contrast with the zero lag case). For lags of 10-15 years the relationship between GHG increases in recent decades and increasing temperatures is considerably more significant, while the relationship with solar irradiance is considerably less significant. For the shorter (100 year window) there are few enough degrees of freedom in the temperature and forcing series, that the statistics are not as stable (ie, the results are much "noisier"). In particular, larger negative correlations with GHG are achieved prior to 1800 in this case, although these are not significant taking into account the decreased degrees of freedom in the series]. Nonetheless, even with the large sampling variations that arise in the 100 year window case, the relationship between recent warming and increasing greenhouse gas concentrations is the dominant statistical feature. It is evident that the inclusion of a representation of the lagged response of temperatures to forcing heightens the evidence for a recent anthropogenic impact on 20th century climate beyond that presented in MBH98.


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