In 2007, I taught the class Human Impacts on Weather and Climate at the University of Colorado in Boulder. One of my students, Laure Montandon, completed a term paper
Over the following several years, in collaboration with Souleymane Fall and Dev Niyogi of Purdue University, she led the development of this unfunded research into a peer reviewed paper. This paper is now available:
Montandon, L.M., S. Fall, R.A. Pielke Sr., and D. Niyogi, 2010: Distribution of landscape types in the Global Historical Climatology Network. Earth Interactions, in press, doi: 10.1175/2010EI371.1
The abstract reads
The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased towards urban and cropland (>50% stations vs. 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations vs. 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are under-represented (14% stations vs. 48.1% land) as well as non-urban areas that have remained uncultivated in the past century (14.2% stations versus 43.2% land). Results from the temperature trends over the different landscapes confirm that the temperature trends are different for different LULC and that the GHCNv.2 stations network might be missing on long term larger positive trends. This opens the possibility that the temperature increases of the Earth’s land surface in the last century would be higher than what the GHCNv.2 based GAST analyses reports.
Excerpts from the conclusion read
Using over 5000 stations and different LULC datasets, a synthesis regarding the representation of the current GHCN-monthly temperature dataset (Version 2, GHCNv.2 hereafter) was successfully conducted…. Our results confirm the findings from Hansen et al. (2001) that the GHCNv.2 metadata is outdated.
The scientific level of understanding on how LULC affect climate is low and the scientific community should focus on better understanding the related impacts, improving the global distribution of temperature stations network, and updating the descriptions of the LULC and other metadata for each station (including photographic documentation) in order to address this issue. The analysis presented in this paper should also be updated
with more recent temperature datasets and land use metadata. The trend analysis exercise was undertaken to gain a perspective on the potential impact of the land cover distribution on the surface temperatures, and should be repeated in a more formal manner with historical land use change data, more detailed metadata and up-to-date datasets in a follow up study.
Our finding that, based on the HHCNv.2, that “temperature increases of the Earth’s land surface in the last century would be higher than what the GHCNv.2 based GAST analyses reports”, results in an even greater divergence in long term trends between surface and lower tropospheric temperatures as we reported on in
Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., J.R. Christy, and R.T. McNider, 2009: An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841.
Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., J.R. Christy, and R.T. McNider, 2010: Correction to: “An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841″, J. Geophys. Res., 115, D1, doi:10.1029/2009JD013655.
Our new paper also shows that a set of data (the GHCN) which is used in the construction of a multi-decadal global annual average surface temperature trend has serious quantitative errors. These errors are compounded by biases resulting from poor siting of the observations (often even in rural areas) and a systematic bias in the extrapolation of the data to a grid (i.e. “homogenization”) as we will have found for the USHCN and will report on here once the review process of that paper is complete.