Where are the Newly Recognized Uncertainties Assessed in the 2005 GISS Surface Temperature Record?

Jim Hansen has reported on the GISS temperature analysis for 2005. The GISS summary stated,

“The highest global surface temperature in more than a century of instrumental data was recorded in the 2005 calendar year in the GISS annual analysis. However, the error bar on the data implies that 2005 is practically in a dead heat with 1998, the warmest previous year.”

The summary further states,

“Our analysis differs from others by including estimated temperatures up to 1200 km from the nearest measurement station (7). The resulting spatial extrapolations and interpolations are accurate for temperature anomalies at seasonal and longer time scales at middle and high latitudes, where the spatial scale of anomalies is set by Rossby waves…”

Conspicuously absent in his discussion is the warm bias that we have identified, along with several other issues of uncertainty, in the quantification of surface temperature anomalies. The assumption that anomalies ” up to 1200 km from the nearest measurement station” can be used to construct a quantitative anomaly analysis, based on the wavelength of Rossby waves, by itself should raise a serious question on the robustness of the GISS anomaly analysis. As we have shown in our papers that are listed below, there are several issues with the spatial representativeness of any individual surface observing site.

These uncertainties will clearly raise the error bars in their analyses. These uncertainties were reported in the recent weblog entitled “Science Questions on the Global Surface Temperature Trends” and the questions are repeated below. Until these issues are resolved, his conclusion regarding the warmth of 2005 should be very much be considered incomplete.

Question: What are the level(s) at which the temperatures used in the GISS analysis were monitored? Are the temperature trends height invariant near the surface?

Question: What is the magnitude of the warm bias in the GISS analysis of the surface temperature trends that we reported on in Pielke Sr., R.A., and T. Matsui, 2005: Should light wind and windy nights have the same temperature trends at individual levels even if the boundary layer averaged heat content change is the same?�

Question: What photographic documentation is available for the global network of GISS surface temperature sites used to construct the long term global surface temperature analyses, such as we have identified for eastern Colorado in Davey, C.A., and R.A. Pielke Sr., 2005: “Microclimate exposures of surface-based weather stations – implications for the assessment of long-term temperature trends?”

Question: What are the quantitative trends in surface absolute humidity for the sites used to construct the GISS surface temperature trends, and what is the uncertainty in the surface heat changes that is introduced if this information is not available? We reported on this issue in Pielke Sr., R.A., C. Davey, and J. Morgan, 2004: Assessing “global warmingâ€? with surface heat contentâ€? and Davey, C.A., R.A. Pielke Sr., and K.P. Gallo, 2005: Differences between near-surface equivalent temperature and temperature trends for the eastern United States – Equivalent temperature as an alternative measure of heat content.â€?

Question: What are the quantitative uncertainties introduced from each step of the homogenization adjustment as applied in the GISS analysis? Do they vary geographically? This includes adjustments made due to the time of observation, a change of instrument, the change in location, and from urbanization. Pielke Sr., R.A., T. Stohlgren, L. Schell, W. Parton, N. Doesken, K. Redmond, J. Moeny, T. McKee, and T.G.F. Kittel, 2002: Problems in evaluating regional and local trends in temperature: An example from eastern Colorado�

Question: What is the degree of overlap in the data set used in the GISS analysis with analyses by other groups that are used to construct the global average surface temperature trend analyses? To frame this question another way, what raw surface temperature data is used in each analysis that is not used in the other analyses? The best estimate we have seen is that 90-95% of the raw data is the same. A paper on this topic will shortly be submitted for peer review, and a copy made available on this web site when appropriate.

Existing papers published by Jim Hansen and colleagues either do not consider at all these uncertainties, or inadequately consider them such as with respect to the “homogenization” of the data; e.g.

Hansen, J., R. Ruedy, J. Glascoe, and Mki. Sato 1999. GISS analysis of surface temperature change. J. Geophys. Res. 104, 30997-31022, doi:10.1029/1999JD900835.

Hansen, J.E., R. Ruedy, Mki. Sato, M. Imhoff, W. Lawrence, D. Easterling, T. Peterson, and T. Karl 2001. A closer look at United States and global surface temperature change. J. Geophys. Res. 106, 23947-23963, doi:10.1029/2001JD000354.

In the 2001 paper, they state that they have added from their 1999 paper the following,

“(1) incorporation of corrections for time-of-observation bias and station history adjustments in the United States based on Easterling et al. [1996a], (2) reclassification of rural, small-town, and urban stations in the United States, southern Canada, and northern Mexico based on satellite measurements of night light intensity [Imhoff et al., 1997], and (3) a more flexible urban adjustment than that employed by Hansen et al. [1999], including reliance on only unlit stations in the United States and rural stations in the rest of the world for determining long-term trends.”

However, new issues have arisen that Jim Hansen needs to consider. Until these concerns are quantified in the peer-reviewed literature, the interpretation presented in the GISS review of the temperature anomalies should be considered as incomplete.

Also, as repeatedly stated on our web site and in our paper – Pielke Sr., R.A., 2003: “Heat storage within the Earth system”, the assessment of climate heat system changes should be performed using the more robust metric of ocean heat content changes.

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