Comments On The BEST FAQ

[image above is from the BEST FAQ page]

The Berkeley Earth Surface Temperature [BEST] project has a Frequently Asked Question [FAQ] available. In this post, I will respond to two of their answers. First

One of the elements that we have analyzed is temperature records from only the very best sites (as classified by Anthony Watts and his team) contrasted with the poorer sites. This analysis is in the paper “Earth Atmospheric Land Surface Temperature and Station Quality in the United States”, available here.

The Fall et al 2011 paper was only the first contribution which is looking into this issue. We only examined a subset of aspects concerning siting and will have new results to report soon.

Additionally, each of our 39,028 sites has been classified as urban or rural using the map published by the Modis satellite team, and have also used that classification to look for differences. The results of that analysis are in the paper “Influence of Urban Heating on the Global Temperature Land Average”, available here.

The discrimination into rural and urban is insufficient to examine the diversity of effects of landscape type on the temperature trend record. Different landscape types have different temperature trends as we reported in

Fall, S., D. Niyogi, A. Gluhovsky, R. A. Pielke Sr., E. Kalnay, and G. Rochon, 2009: Impacts of land use land cover on temperature trends over the continental United States: Assessment using the North American Regional Reanalysis. Int. J. Climatol., DOI: 10.1002/joc.1996.

Among our results, we found that [highlight added]

 “…..most of the warming trends that we identify can be explained on the basis of LULC changes”

“Our results suggest that for both non-changed and converted land types, agriculture, urbanization and barren soils offered the clearest patterns in terms of sign and magnitude of the OMR trends. Conversion to agriculture resulted in a strong cooling. Conversely, all conversions of agricultural lands resulted in warming. Urbanization and conversion to bare soils were also mostly associated with warming. We conclude that these LULC types constitute strong drivers of temperature change.

Deforestation generally resulted in warming (with the exception of a shift from forest to agriculture) but no clear picture emerged for afforestation. Within each land use conversion type, a great variation of warming/cooling was observed, as attested by relatively large standard deviations. In addition, our analysis shows that there is not always a straightforward relationship between the different types of conversions: for example, (1) both conversion of urban to barren and the opposite resulted in slightly negative OMRs; (2) there was a weak warming of areas that shifted from bare soils to grassland/shrubland and for the opposite as well and (3) both conversion from forest to grassland/shrubland and the opposite were associated with a weak warming. In a number of cases, our estimates were hampered by the lack of significance due to a small number of samples. All these considerations lead us to conclude that the effects of LULC changes on temperatures trends are significant but more localized studies need to be conducted using high-resolution datasets.

We also reported in

Montandon, L.M., S. Fall, R.A. Pielke Sr., and D. Niyogi, 2011: Distribution of landscape types in the Global Historical Climatology Network. Earth Interactions, 15:6, doi: 10.1175/2010EI371

that landscape types are not accurately sampled in the GHCN. This question also needs to be asked of the BEST analyses. The abstract of the Montandon et al 2011 article reads [highlight added]

“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 toward urban and cropland (.50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban 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 Earth’s land surface in the last century would be higher than what the GHCNv.2-based GAST analyses report.”

Such a finding of a larger surface temperature trend, however, does not necessarily mean this is due to added greenhouse gases, since such a trend is not seen in the lower troposphere, 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.

The BEST project needs to bin their data by landscape type in order to determine if they obtain the same finding as in Montandon et al 2011.

The BEST answers continue with

Our study addressed only one area of the concerns: was the temperature rise on land improperly affected by the four key biases (station quality, homogenization, urban heat island, and station selection)? The answer turned out to be no – but they were questions worthy of investigation. Berkeley Earth has not addressed issues of the tree ring and proxy data, climate model accuracy, or human attribution.

BEST has overstated the completeness of their study.  They have not yet examined all aspects of station quality,  homogenization, urbanization, and station selection.  With respect to station quality, for example, BEST used the classification provided in Fall et al 2011, yet this was just a first evaluation and we are in the process of significantly improving its accuracy. BEST has prematurely assumed the siting quality issue has been adequately assessed. BEST also fails to acknowledge that land use change involves much more than urbanization. Vast areas of land have been converted as we report on in

 Pielke Sr., R.A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K. Goldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet-Ducoudré, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. Wiley Interdisciplinary Reviews: Climate Change, Invited paper, in press.

BEST, so far, has not examined the role of land use/land cover change on their temperature trends.  They do not even seem to be aware that this is an issue.

My Final Summary

The BEST project provides an interesting new group to examine the land surface temperature record as applied to long term temperature trends and anomalies. However, they have failed to adequately consider the range of issues that are yet to be resolved. and have prematurely reported their findings and conclusions both in their submitted papers and in their media interactions.