subject: Request from Michael Mann to Review PNAS MS # 2009-09401
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B3.07; Q3.07) Date: Fri, 11 Sep 2009 11:11:48 -0400 Message-Id: <23125268190839@ejpweb15>
September 11, 2009
Dear Dr. Jones,
Michael Mann is conducting the review of a Direct Submission by wang et al. (MS
#2009-09401) and would like you to be a reviewer for this article if your schedule permits.
We would need to have your critique within the next 10 days.
Reviewers are asked to recuse themselves from handling a manuscript if they have a
potential conflict of interest, intellectual or financial, that precludes them from
rendering an impartial scientific judgment or evaluation. Reviewers who have a conflict but
believe that it does not preclude their making a proper judgment must disclose to the
journal the nature of the conflict. If you are a recent collaborator of any of the authors
(that is, you have coauthored a paper with any of the authors within the past 48 months),
we ask that you recuse yourself.
To ACCEPT or DECLINE to review this manuscript, click the appropriate link below. If you
choose to ACCEPT, you will have access to the manuscript and review form immediately. It is
our policy that reviewers remain anonymous. If you choose to DECLINE, we would be grateful
if you could identify others we might contact who would also be qualified to review this
Thank you for your help.
PNAS Editorial Office
Title: "Improving Spatial Temperature Estimation of Global Climate Change - The Case of
Tracking #: 2009-09401
Jin-Feng wang (Chinese Academy of Sciences)
Mao-Gui Hu (Chinese Academy of Sciences)
George Christakos (San Diego State University)
Cheng-Sheng Jiang (Chinese Academy of Sciences)
Yan-Sha Guo (Chinese Academy of Sciences)
Ai-Hua Ma (Beijing Normal University)
Accept the invitation and begin your review.
Decline the invitation and provide alternate reviewer suggestions.
A small variation of global annual temperature would cause considerable ecological,
economic and social consequences at a regional scale or worldwide, which is why
considerable efforts are made worldwide to improve the accuracy of temperature estimation
across space. These efforts typically include extending the monitoring network, improving
record quality, and integrating multi-sourced information bases. Statistical inference of
global climate attributes on the basis of observed samples can introduce considerable
uncertainty in temperature estimation. A theoretical model of annually averaged spatial
temperature estimation is proposed that is statistically unbiased and minimizes the
estimation error variance throughout the large territories of heterogeneous climate change.
On the basis of the extensive China dataset, it is shown that the study of climate change
can be improved by using proposed model in the spatial analysis of temperature values.
Compared to the currently
used grid average technique, the proposed model can reduce the standard deviation of the
annually averaged temperature predictions by 0.50C and the standard error deviation of the
annually averaged temperature anomaly by 50%. The proposed method is applicable in the
study of climate attributes at both the regional and global levels.