Name
Title | ||
|---|---|---|
Reductions Of Noise And Uncertainty In Annual Global Surface Temperature Anomaly Data |
Abstract | ||
|---|---|---|
Global climate variability is currently a topic of high scientific and public interest, with potential ramifications for the Earth's ecologic systems and policies governing world economy. Across the broad spectrum of global climate variability, the least well understood time scale is that of decade-to-century. 1 The bases for investigating past changes across that period band are the records of annual mean Global Surface Temperature Anomaly (GSTA) time series, produced variously in many painstaking efforts. 2-5 However, due to incipient instrument noise, the uneven distribution of sensors spatially and temporally, data gaps, land urbanization, and bias corrections to sea surface temperature, noise and uncertainty continue to exist in all data sets. 1,2,6-8 Using the Empirical Mode Decomposition method as a filter, we can reduce this noise and uncertainty and produce a cleaner annual mean GSTA dataset. The noise in the climate dataset is thus reduced by one-third and the difference between the new and the commonly used, but unfiltered time series, ranges up to 0.1506. C, with a standard deviation up to 0.01974. C, and an overall mean difference of only 0.0001. C. Considering that the total increase of the global mean temperature over the last 150 years to be only around 0.6. C, we believe this difference of 0.1506. C is significant. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S1793536909000151 | ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS |
Keywords | Field | DocType |
Global temperature change, down sampling, HHT filtering | Global climate,Meteorology,Mean difference,Urbanization,Mean radiant temperature,Sea surface temperature,Standard deviation,Mathematics,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
1 | 3 | 2424-922X |
Citations | PageRank | References |
5 | 0.56 | 1 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norden E. Huang | 1 | 950 | 81.56 |
| Zhaohua Wu | 2 | 859 | 74.94 |
| Jorge E. Pinzón | 3 | 79 | 12.16 |
| Claire L. Parkinson | 4 | 14 | 1.59 |
| Steven Long | 5 | 42 | 6.95 |
| Karin Blank | 6 | 42 | 6.61 |
| Per Gloersen | 7 | 5 | 0.56 |
| Xianyao Chen | 8 | 121 | 13.31 |