Surface Humidity Changes in Different Temporal Scales
Affiliation(s)
Department of Epidemiology and Biostatistics, State University of New York at Albany, Rensselaer, USA.
Department of Epidemiology and Biostatistics, State University of New York at Albany, Rensselaer, USA.
ABSTRACT
As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1° × 1° grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards; then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent; meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.
As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1° × 1° grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards; then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent; meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.
KEYWORDS
Specific Humidity Climate, El Niño-Like Movement, Long-Term Trend, KZ Filters, Spatial Pattern, Temporal Scales, High Resolution, Visualization
Specific Humidity Climate, El Niño-Like Movement, Long-Term Trend, KZ Filters, Spatial Pattern, Temporal Scales, High Resolution, Visualization
Cite this paper
Zurbenko, I. and Luo, M. (2015) Surface Humidity Changes in Different Temporal Scales. American Journal of Climate Change, 4, 226-238. doi: 10.4236/ajcc.2015.43018.
Zurbenko, I. and Luo, M. (2015) Surface Humidity Changes in Different Temporal Scales. American Journal of Climate Change, 4, 226-238. doi: 10.4236/ajcc.2015.43018.
References
[1] Saha, K. (2008) The Earth’s Atmosphere: Its Physics and Dynamics. Springer-Verlag, Berlin Heidelberg. [2] Bridgman, H. and Oliver, J. (2006) The Global Climate System: Patterns, Processes, and Teleconnections. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511817984 [3] Willett, K., Dunn, R., Thorne, P., Bell, S., de Podesta, M., Parker, D., Jones, P. and Williams Jr., C. (2014) HadISDH Land Surface Multi-Variable Humidity and Temperature Record for Climate Monitoring. Climate of the Past, 10, 1983-2006.
http://dx.doi.org/10.5194/cp-10-1983-2014 [4] Willett, K., Jones, P., Gillett, N. and Thorne, P. (2008) Recent Changes in Surface Humidity: Development of the HadCRUH Dataset. Journal of Climate, 21, 5364-5383.
http://dx.doi.org/10.1175/2008JCLI2274.1 [5] Amenu, G. and Praveen, K. (2005) NVAP and Reanalysis-2 Global Precipitable Water Products: Intercomparison and Ariability Studies. Bulletin of the American Meteorological Society, 86, 245-256.
http://dx.doi.org/10.1175/BAMS-86-2-245 [6] Dessler, A. and Davis, S. (2010) Trends in Tropospheric Humidity from Reanalysis Systems. Journal of Geophysical Research, 115, D19127.
http://dx.doi.org/10.1029/2010jd014192 [7] Haar, V., Bytheway, J. and Forsythe, J. (2012) Weather and Climate Analyses Using Improved Global Water Vapor Observations. Geophysical Research Letter, 39, L15802. [8] Zurbenko, I. and Potrzeba, A. (2013) Periods of Excess Energy in Extreme Weather Events. Journal of Climatology, 2013, Article ID: 410898.
http://dx.doi.org/10.1155/2013/410898 [9] Yang, W. and Zurbenko, I. (2010) Kolmogorov-Zurbenko Filters. WIREs Computational Statistics, 2, 340-351.
http://dx.doi.org/10.1002/wics.71 [10] Wikipedia (2015) Kolmogorov-Zurbenko Filter.
http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Zurbenko_filter [11] Zurbenko, I. and Cyr, D. (2011) Climate Fluctuations in Time and Space. Climate Research, 46, 67-76.
http://dx.doi.org/10.3354/cr00956 [12] Zurbenko, I. and Luo, M. (2012) Restoration of Time-Spatial Scales in Global Temperature Data. American Journal of Climate Change, 1, 154-163.
http://dx.doi.org/10.4236/ajcc.2012.13013 [13] National Climatic Data Center (2009) International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 2.5, Monthly Summaries. Updated Monthly at
http://dx.doi.org/10.5065/D6CF9N3F [14] Adam, S., Lott, N. and Vose, R. (2011) The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704-708.
http://dx.doi.org/10.1175/2011BAMS3015.1 [15] Dai, A. (2006) Recent Climatology, Variability, and Trends in Global Surface Humidity. Journal of Climate, 19, 3589-3606.
http://dx.doi.org/10.1175/JCLI3816.1 [16] Williams, C.N., Menne, M.J. and Thorne, P.W. (2012) Benchmarking the Performance of Pairwise Homogenization of Surface Temperatures in the United States. Journal of Geophysical Research, 117, Article ID: D05116.
http://dx.doi.org/10.1029/2011jd016761 [17] Letestu, S. (1966) International Meteorological Tables. WMO-No.188.TP.94, World Meteor Organization, Geneva. [18] Luo, M. and Zurbenko, I. (2012) Comparison of Time and Spatial Scales in Global Temperature Data. In: JSM Proceedings 2012, Section on Statistics and Environment, American Statistical Association, Alexandria, 3040-3051. [19] Zurbenko, I. (1986) The Spectral Analysis of Time Series, North-Holland Series in Statistics and Probability. Elsevier, Amsterdam. [20] Dahlman, L. (2009) Climate Variability: Oceanic Nino Index. Climate Watch Magazine, 30 August 2009. [21] Lanzante, J.R. (1996) Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, including Applications to Historical Radiosonde Station Data. International Journal of Climatology, 16, 1197-1226.
http://dx.doi.org/10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L [22] Trenberth, K. and Guillemot, C. (1998) Evaluation of the Atmospheric Moisture and Hydrological Cycle in the NCEP/ NCAR Reanalyses. Climate Dynamics, 14, 213-231.
http://dx.doi.org/10.1007/s003820050219 [23] Barry, L., Craig, G. and Thuburn, J. (2002) Poleward Heat Transport by the Atmospheric Heat Engine. Nature, 415, 774-777.
http://dx.doi.org/10.1038/415774a [24] Zurbenko, I. and Potrzeba-Macrina, A. (2013) Tides in the Atmosphere. Air Quality, Atmosphere, & Heath, 6, 39-46.
http://dx.doi.org/10.1007/s11869-011-0143-6
[1] Saha, K. (2008) The Earth’s Atmosphere: Its Physics and Dynamics. Springer-Verlag, Berlin Heidelberg. [2] Bridgman, H. and Oliver, J. (2006) The Global Climate System: Patterns, Processes, and Teleconnections. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511817984 [3] Willett, K., Dunn, R., Thorne, P., Bell, S., de Podesta, M., Parker, D., Jones, P. and Williams Jr., C. (2014) HadISDH Land Surface Multi-Variable Humidity and Temperature Record for Climate Monitoring. Climate of the Past, 10, 1983-2006.
http://dx.doi.org/10.5194/cp-10-1983-2014 [4] Willett, K., Jones, P., Gillett, N. and Thorne, P. (2008) Recent Changes in Surface Humidity: Development of the HadCRUH Dataset. Journal of Climate, 21, 5364-5383.
http://dx.doi.org/10.1175/2008JCLI2274.1 [5] Amenu, G. and Praveen, K. (2005) NVAP and Reanalysis-2 Global Precipitable Water Products: Intercomparison and Ariability Studies. Bulletin of the American Meteorological Society, 86, 245-256.
http://dx.doi.org/10.1175/BAMS-86-2-245 [6] Dessler, A. and Davis, S. (2010) Trends in Tropospheric Humidity from Reanalysis Systems. Journal of Geophysical Research, 115, D19127.
http://dx.doi.org/10.1029/2010jd014192 [7] Haar, V., Bytheway, J. and Forsythe, J. (2012) Weather and Climate Analyses Using Improved Global Water Vapor Observations. Geophysical Research Letter, 39, L15802. [8] Zurbenko, I. and Potrzeba, A. (2013) Periods of Excess Energy in Extreme Weather Events. Journal of Climatology, 2013, Article ID: 410898.
http://dx.doi.org/10.1155/2013/410898 [9] Yang, W. and Zurbenko, I. (2010) Kolmogorov-Zurbenko Filters. WIREs Computational Statistics, 2, 340-351.
http://dx.doi.org/10.1002/wics.71 [10] Wikipedia (2015) Kolmogorov-Zurbenko Filter.
http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Zurbenko_filter [11] Zurbenko, I. and Cyr, D. (2011) Climate Fluctuations in Time and Space. Climate Research, 46, 67-76.
http://dx.doi.org/10.3354/cr00956 [12] Zurbenko, I. and Luo, M. (2012) Restoration of Time-Spatial Scales in Global Temperature Data. American Journal of Climate Change, 1, 154-163.
http://dx.doi.org/10.4236/ajcc.2012.13013 [13] National Climatic Data Center (2009) International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 2.5, Monthly Summaries. Updated Monthly at
http://dx.doi.org/10.5065/D6CF9N3F [14] Adam, S., Lott, N. and Vose, R. (2011) The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704-708.
http://dx.doi.org/10.1175/2011BAMS3015.1 [15] Dai, A. (2006) Recent Climatology, Variability, and Trends in Global Surface Humidity. Journal of Climate, 19, 3589-3606.
http://dx.doi.org/10.1175/JCLI3816.1 [16] Williams, C.N., Menne, M.J. and Thorne, P.W. (2012) Benchmarking the Performance of Pairwise Homogenization of Surface Temperatures in the United States. Journal of Geophysical Research, 117, Article ID: D05116.
http://dx.doi.org/10.1029/2011jd016761 [17] Letestu, S. (1966) International Meteorological Tables. WMO-No.188.TP.94, World Meteor Organization, Geneva. [18] Luo, M. and Zurbenko, I. (2012) Comparison of Time and Spatial Scales in Global Temperature Data. In: JSM Proceedings 2012, Section on Statistics and Environment, American Statistical Association, Alexandria, 3040-3051. [19] Zurbenko, I. (1986) The Spectral Analysis of Time Series, North-Holland Series in Statistics and Probability. Elsevier, Amsterdam. [20] Dahlman, L. (2009) Climate Variability: Oceanic Nino Index. Climate Watch Magazine, 30 August 2009. [21] Lanzante, J.R. (1996) Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, including Applications to Historical Radiosonde Station Data. International Journal of Climatology, 16, 1197-1226.
http://dx.doi.org/10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L [22] Trenberth, K. and Guillemot, C. (1998) Evaluation of the Atmospheric Moisture and Hydrological Cycle in the NCEP/ NCAR Reanalyses. Climate Dynamics, 14, 213-231.
http://dx.doi.org/10.1007/s003820050219 [23] Barry, L., Craig, G. and Thuburn, J. (2002) Poleward Heat Transport by the Atmospheric Heat Engine. Nature, 415, 774-777.
http://dx.doi.org/10.1038/415774a [24] Zurbenko, I. and Potrzeba-Macrina, A. (2013) Tides in the Atmosphere. Air Quality, Atmosphere, & Heath, 6, 39-46.
http://dx.doi.org/10.1007/s11869-011-0143-6