Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)

Authors
Erfan Naseri 1
Alireza Massah Bavani 1
Tofigh Sadi 2
1 University of Tehran
2 Regional Water Company of Alborz
10.52547/jsaeh.8.1.93
Abstract


Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)

Abstract

One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.



Key words: Climate change, Detection, Attribution, Optimal Fingerprint, Hillsides of Central Southern Alborz Mountains
Keywords
Climate Change
Detection
attribution
Optimal Fingerprint
Hillsides of Central Southern Alborz Mountains
Allen, M. R., and S. F. B. Tett .1999. Checking for model consistency in optimal fingerprinting. Climate Dynamics, 15:409–424, DOI: 10.1007/s003820050291.
Allen, M., P. Stott. 2003. Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Climate Dynamics, 21:477-491, DOI: 10.1007/s00382-003-0313-9.
Dileepkumar, R.; K, Achutarao and T.Arulalan. 2018. Human influence on sub-regional surface air temperature change over India. Scientific reports, 8:8967. DOI: https//:doi.org.10.1038/s41598-018-27185-8.
Duan, J.; Z. Ma, P. Wu, E. Xoplaki, G. Hegerl, L. Li, A. Schurer, D. Guan, L. Chen, Y. Daun and J. Luterbacher.2019. Nature Sustainability, DOI: http//:doi.org.10.1038/s41893-019-0276-4.
Ganopolski, A. and Calov, R. 2011. The role of orbital forcing, carbon dioxide and regolith in 100 kyr glacial cycles. Clim. Past, 7:1415–1425, DOI: https://doi.org/10.5194/cp-7-1415-2011.
Velasquez, P., M, Messmer and C, C.Raible.2019. A new bias-correction method for precipitation over complex terrain suitable for different climate states. Geoscientific-model-development, 7:1415–1425, DOI: https://doi.org/10.5194/gmd-2019-131.
Hannart, A., A. Ribes and P. Naveau. 2014. Optimal fingerprinting under multiple sources of uncertainty. Geophysical Research Letters, 41:1261-1268, DOI: 10.1002/2013GL058653.
Hasselmann, K. 1993. Optimal fingerprints for the detection of time dependent climate change. Journal of Climate, 6:1957–1971, DOI: https://doi.org/10.1175/1520 0442 (1993) 006 2.0.CO;2.
Hasselmann, K.1979. On the signal-to-noise problem in atmospheric response studies. In: Shaw DB (ed) Meteorology over the tropical oceans. Royal Meteorological Society, 251–259. 7:1415–1425, DOI: https://doi.org/10.1002/qj.49710644918.
Hasselmann, K. 1997.Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dynamics, 13:601-611, DOI: 10.1007/s003820050185.
Hegerl, G. C., and F. W. Zwiers .2011. Use of models in detection and attribution of climate change. WIREs Climate Change, 2:520–590, DOI:10.1002/wcc.121.
Intergovernmental Panel on Climate Change.2010. Meeting report of the Intergovernmental Panel on climate change expert meeting on detection and attribution related to anthropogenic climate change, report, pp. 55, IPCC, Working Group I Technical Support Unit, Univ. of Bern, Bern, Switzerland.
Jannani Abbasi, Sh., and H. Malekinrejad.2013. Evaluation geostatistical methods based on GIS for precipitation by spatial zoning (Case study: Kouhpaye-Sagzi Plane). First National Conference on Rainwater Catchment Systems, Mashhad, Iran.
Marvel K., and C, Bonfils. 2013. Identifying external influences on global precipitation, PNAS, 48: 19301-19306, DOI: https://doi.org/10.1073/pnas.1314382110.
Mondal, A.; P. P. Mujumdar. 2012. On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and stream flow. Water Resources Research, 48:10520. DOI: 10.1029/2011WR011468.
Naseri,E., A.R, Massah Bavani., T, Saadi. 2019. Performance Assessment of GCMs for Estimating the Average Temperature of Alborz Province in (1985-2015). 6th Regional Conference on Climate Change, Tehran, IRAN.
Peng, D.; T. Zhou, L. Zhang and L.Zou. 2019. Detecting human influence on the temperature changes in Central Asia. Climate Dynamics, 53:4553–4568. DOI: http//:doi.org.10.1007/s00382-019-04804-2.
Ribes, A., F. W. Zwiers, J. M. Azaïs, and P. Naveau . 2016. A new statistical approach to climate change detection and attribution. Climate Dynamics, 1-20, DOI: 10.1007/s00382-016-3079-6.
Ribes, A., L. Terray, and S. Planton .2013.Application of regularized optimal fingerprinting to attribution. Part I: method, properties and idealized analysis. Climate Dynamics, 41:2817-2836, DOI: 10.1007/s00382-013-1735-7.
Saadi, T,. B, Alijani., M .Akbary., A.R, Massah Bavani.2016. Detection of extreme precipitation changes and attribution to climate change using standard optimal fingerprinting (Case study: The Southwest of Iran). Jsaeh, 3:65-80. DOI: 10.18869/acadpub.jsaeh.3.3.65.
Scinocca, J.F., V.V. Kharin, Y. Jiao, M.W. Qian, M. Lazare, L. Solheim, G.M. Flato, S. Biner, M. Desgagne, and B. Dugas. 2016. Coordinated Global and Regional Climate Modeling. J. Climate, 29:17–35, DOI: https://doi.org/10.1175/JCLI-D-15-0161.1.
Stocker, T. F., D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P. M. Midgley (Eds.).2013. Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge.
Wan, H., X. Zhang, F. W. Zwiers and S. K. Min. 2015. Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence. Climate Dynamics, 47:1713-1726, DOI: 10.1007/s00382-014-2423-y.
Westra, S., L. V. Alexander, and F. W. Zwiers .2013. Global increasing trends in annual maximum daily precipitation. Journal of Climate, 26:3904-3918, DOI: https://doi.org/10.1175/JCLI-D-12-00502.1.
Zhang, X., H. Wan, F. W. Zwiers, G.C. Hegerl and S. K. Min. 2013. Attributing intensification of precipitation extremes to human influence. Geophysical Research Letters, 40: 5252-5257, DOI: 10.1002/grl.51010.