A Fuzzy Weighted Moving Average to Analyze Actual Warming
Key Laboratory of Desert and Desertification, Chinese Academy of Sciences, Lanzhou 730000, China
| Abstract: | This article firstly proposes a fuzzy weighted moving average (FWMA) to compute well for the beginning and end parts. Then, the FWMA is applied to both of the annual anomalies and the annual standardized differences (variance adjusted anomalies) of temperature among the six top nations or regions of cumulative CO2 emissions, Canada and the globe to reveal and compare the interdecadal trends. The related topic of ‘land/sea warming contrast’ was re-analyzed with the FWMA. The main findings are somewhat unexpected as follows: (1) The FWMA curves of anomalies showed that all six nations got warming up stronger than the globe except for India. But the sequence order of the nation’s warming extents in the last decade were much different from those of nation’s cumulative CO2 emissions. (2) The FWMA of the annual standardized differences of temperature showed much better than the annual anomalies. The sequence order of nation’s warming up agreed with that of the cumulative CO2 emissions among the seven nations. In contrary, the globe got warming obviously higher than all the seven nations or regions. The direct reason, in statistics, is that the climatologic variance of the globe is much smaller than all the seven nations, in statistics. (3) The related phenomena of ‘land/sea warming contrast’ appeared only in their anomalies, but disappeared all in the standardized differences. The direct reason is depended upon the around 2 times of difference of the climatologic variance between the land and sea. (4) The FWMA curves for the globe actually much closes to that of the sea than the land. |
| Keywords: | Fuzzy Weighted Moving Average (FWMA); Climate Change; CO2 Emission; Annual Temperature; Anomalies; Standardized Differences; Land/Sea Warming Contrast |
| DOI: | 10.57237/j.earth.2022.01.005 |
View:
Downloads:
Received: 3 January 2023
Accepted: 16 February 2023
Published Online: 2 March 2023
References
| [1] | Russian Federal Service for Hydrometeorology and Environmental Monitoring (ROSHYDROMET). Climate change bulletin [R]. http://www.meteorf.ru/default.aspx, 2022. (in Russian) |
| [2] | Liu Y, Song H, An Z, et al. (2020). Recent anthropogenic curtailing of Yellow River runoff and sediment load is unprecedented over the past 500 y [J]. PNAS, 2020. www.pnas.org/cgi/doi/10.1073/pnas.1922349117. |
| [3] | Sun C, Li J, Kucharski F, Kang I, Jin F, Wang K, et al. Recent acceleration of Arabian Sea warming induced by the Atlantic-western Pacific trans-basin multidecadal variability [J]. Geophysical Research Letters, 2019, 46: 1662–1671. https://doi.org/10.1029/2018GL081175. |
| [4] | 丁一汇, 王会军. 近百年中国气候变化科学问题的新认识 [J]. 科学通报, 2016, 61 (10): 1029–1041. |
| [5] | Ren G Y, Ding Y H, Zhao Z C, Zheng J Y, Wu T W, Tang G L, Xu Y. Recent progress in studies of climate change in China [J]. Adv. Atmos. Sci., 2012, 29: 958-977. |
| [6] | Yang B, et al. Long-term decrease in Asian monsoon rainfall and abrupt climate change events over the past 6,700 years [J]. PNAS, 2021, 118 (30): e210207118. https://doi.org/10.1073/pnas.2102007118 |
| [7] | Tollefson J. Can the world slow global warming? [J]. Nature, 19 Sep. 2019, V573：325. |
| [8] | Baas S, Kwakernaak H. Rating and ranking of multiple-aspect alternatives using fuzzy sets [J]. Automatica, 1977, 13: 47–58. |
| [9] | Chang P, Hung K, Lin K, Chang C. A comparison of discrete algorithms for fuzzy weighted average [J]. IEEE Transactions on Fuzzy Systems, 2006, 14 (5): 663-675. |
| [10] | Lina K, Hung K. An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making [J]. Knowledge-Based Systems, 2011, 24： 877–889. |
| [11] | Tung C, Chu P. Improved linear programming for fuzzy weighted average [J]. Journal of Interdisciplinary Mathematics, 2014, 17 (3): 271-276. |
| [12] | Morice C, Kennedy J, Rayner N, Winn J, Hogan E, Killick R E, Simpson I. An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set [J]. Journal of Geophysical Research: Atmospheres, 2020, 126: e2019JD032361. |
| [13] | Harris I, Osborn T J, Jones P, et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset [J]. Sci Data, 2020, 7: 109. https://doi.org/10.1038/s41597-020-0453-3 |
| [14] | Environment and Climate Change Canada. Climate trends and variations bulletin [R]. https://www.britannica.COm/place/Canada/Climate, 2020. |
| [15] | 江剑民. 四种常用统计参数突变点的扫描式检测算法程序及其应用 [M]. Sydney, Australia: BioByword Publishing Pty Ltd, 2021, ISBN: 978-1-922620-00-2. pp.164. https://item.taobao.com/item.htm?ft=t&id=677024429370 |
| [16] | Robert J, Allen R J, Hassan T, Cynthia A, Randles C A, Su H. Enhanced land–sea warming contrast elevates aerosol pollution in a warmer world [J]. Nat Clim Change, 2019, 302 (9): 300-305. https://doi.org/10.1038/s41558-019-0401-4 |
| [17] |
Jiang J, et al. Chapter 5: Significant change-points of subperiod levels in tree-ring chronologies as indications of climate changes [M]. in Justin A. Daniels Edit: |
| [18] | IPCC. (Eds Houghton J T, Ding Y, Griggs D J, Noguer M, Van der Linden P J, Dai X, Maskell K, Johnson C A.) Climate change 2001: The scientific basis. contribution of working group I to the third assessment report of the international panel on climate change [M]. Cambridge, UK: Cambridge University Press, 2001. |
| [19] | 任国玉 等. 中国气温变化研究最新进展 [J]. 气候与环境研究, 2005, 10 (4): 701-716. |
| [20] | Manabe S, Stouffer R J, Spelman M J, Bryan K. Transient responses of a coupled ocean–atmosphere model to gradual changes of atmospheric CO2. Part I: annual mean response [J]. J. Clim, 1991, 4: 785–818. |
| [21] | Sutton R T, Dong B, Gregory J M. Land/sea warming ratio in response to climate change: IPCC AR4 model results and Comparison with observations [J]. Geophysical Research Letters, 2007, 34: L02701. doi: 10.1029/2006GL028164. |
| [22] | Jones P D, Osborn T J, Briffa K R, Folland C K, Horton E B, Alexander L V, Parker D E, and Rayner N A. Adjusting for sampling density in grid box land and ocean surface temperature time series [J]. J Geophys Res, 2001, 106: 3371– 3380. |
| [23] | Shukla P R, Skea J, Slade R, van Diemen R, Haughey E, Malley J, Pathak M, Portugal Pereira J. Technical Summary, 2019. In: Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. [P. R. Shukla, J. Skea, E. Calvo Buendlia, V. Masson-Delmotte, H.-O. Portner, D. C. Roberts, P. Zhai, R. Slade, S. Connor, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds)] [M]. https://www.ipcc.ch/srccl/cite-report/ |
| [24] | Lambert F, Chiang J. COntrol of land-ocean temperature Contrast by ocean heat uptake [J]. Geophys Res Lett, 34: L13704. |
| [25] | Joshi M, & Gregory J. Dependence of the land-sea Contrast in surface climate response on the nature of the forcing [J]. Geophys Res Lett. 2008, 35: L24802. |
| [26] | Compo G, Sardeshmukh P. Oceanic influences on recent Continental warming [J]. Clim Dyn, 2009, 32: 333–342. |
| [27] | Fasullo J T. Robust land–ocean contrasts in energy and water cycle feedbacks [J]. J Clim., 2010, 23: 4677–4693. |
| [28] | Boer G J. The ratio of land to ocean temperature change under global warming [J]. Clim Dyn, 2011, 37: 2253-2270. https://doi.org/10.1007/s00382-011-1112-3 |
| [29] | Byrne M P, & O’Gorman P A. Land–ocean warming contrast over a wide range of climates: convective quasi-equilibrium theory and idealized simulations [J]. J Clim, 2013, 26: 4000–4016. |
| [30] | Byrne M P, & O’Gorman P A. Link between land–ocean warming COntrast and surface relative humidity in simulations with COupled climate models [J]. Geophys Res Lett, 2013, 40: 5223–5227 |
| [31] | Albert O S. An analysis of climate feedback contributions to the land/sea warming contrast [D]. Thesis of master degree of Science, Department of Earth, Ocean and Atmospheric Sciences, Florida State University, 2014. |
| [32] | Geoffroy O, Saont-Martin D, Voldoire A. Land-sea warming COntrast: the role of the horizontal energy transport [J]. Clim Dyn, 2015, 45 (11): 3493-511. https://doi.org/10.1007/s00382-015-2552-y |
| [33] | Haustein, et al. A real time global warming index [J]. Nature Scientific reports, 2017, 7: 15417. DOI: 10.1038/s41598-017-14828-5 |
| [34] | Jones P D, Lister D H, Osborn T J, Harpham C, Salmon M, and Morice C P. Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010 [J]. J Geophys Res, 2012, 117: D05127. doi: 10.1029/2011JD017139. |
| [35] | Kennedy J J, Rayner N A, Smith R O, Saunby M, and Parker D E. Reassessing biases and other uncertainties in sea-surface temperature observations measured in situ since 1850: 1. Measurement and sampling errors [J]. J Geophys Res, 2011, 116: D14103. doi: 10.1029/2010JD015218. |
| [36] | Kennedy J J, Rayner N A, Smith R O, Saunby M, and Parker D E. Reassessing biases and other uncertainties in sea-surface temperature observations measured in situ since 1850: 2. Biases and homogenization [J]. J Geophys Res, 2011, 116: D14104. doi: 10.1029/2010JD015220. |
| [37] | Morice C P, Kennedy J J, Rayner N A, and Jones P D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set [J]. J Geophys Res, 2012, 117: D08101. doi: 10.1029/2011JD017187. |
| [38] | 廖宏,朱懿旦. 全球碳循环与中国百年气候变化 [J], 第四纪研究, 2010, 30 (3): 445-455. |
| [39] | Zhu y, Jiang J,* Chen Y D, Zhang Q. Applications of multiple change-point detections to monthly streamflow and rainfall in Xijiang River in southern China, Part I: correlation and variance [J]. Theoretical and Applied Climatology, 2019, 136 (1): 237-248. https://doi.org/10.1007/s00704-018-2480-y. |
| [40] | Chen Y D, Jiang J,* Zhu Y, Zhang Q. Applications of multiple change-point detections to monthly streamflow and rainfall in Xijiang River in southern China, Part II: trend and mean [J]. Theoretical and Applied Climatology, 2019, 136 (1): 489-497. https://doi.org/10.1007/s00704-018-2475-8. |
| [41] | Trenberth K E. Has there been a hiatus? [J]. Science, 2015, 349: 691–692. DOI: 10.1126/science.aac9225. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us