Peculiarities of stochastic regime of Arctic ice cover time evolution over 1987–2014 from microwave satellite sounding on the basis of NASA team 2 algorithm
The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08–F17 satellites; it is permanently supplemented with new data on the Earth’s exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic...
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| Published in | Izvestiya. Atmospheric and oceanic physics Vol. 51; no. 9; pp. 929 - 934 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Moscow
Pleiades Publishing
01.12.2015
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-4338 1555-628X |
| DOI | 10.1134/S0001433815090169 |
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| Abstract | The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08–F17 satellites; it is permanently supplemented with new data on the Earth’s exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987–2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95–98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect. |
|---|---|
| AbstractList | The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08–F17 satellites; it is permanently supplemented with new data on the Earth’s exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987–2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95–98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect. The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08-F17 satellites; it is permanently supplemented with new data on the Earth's exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60 degree N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987-2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95-98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect. |
| Author | Repina, I. A. Raev, M. D. Sharkov, E. A. Tikhonov, V. V. Komarova, N. Yu |
| Author_xml | – sequence: 1 givenname: M. D. surname: Raev fullname: Raev, M. D. email: mraev@asp.iki.rssi.ru organization: Space Research Institute, Russian Academy of Sciences – sequence: 2 givenname: E. A. surname: Sharkov fullname: Sharkov, E. A. organization: Space Research Institute, Russian Academy of Sciences – sequence: 3 givenname: V. V. surname: Tikhonov fullname: Tikhonov, V. V. organization: Space Research Institute, Russian Academy of Sciences – sequence: 4 givenname: I. A. surname: Repina fullname: Repina, I. A. organization: Space Research Institute, Russian Academy of Sciences, Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Russian State Hydrometeorological University – sequence: 5 givenname: N. Yu surname: Komarova fullname: Komarova, N. Yu organization: Space Research Institute, Russian Academy of Sciences |
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| Cites_doi | 10.1002/2013GL058951 10.1098/rspa.2011.0728 10.1007/s00382-003-0309-5 10.1002/2014GL060799 10.7868/S0205961413040076 10.1029/2005JC003384 10.1002/grl.50349 10.1002/2014GL060369 10.7868/S0205961414020110 10.1038/nclimate1884 10.2528/PIERB14021706 10.7868/S0205961415020104 10.1038/ngeo2253 10.1029/2009JC005436 |
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| References | TikhonovV.V.RepinaI.A.AlekseevaT.A.IvanovV.V.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.Reconstruction of the concentration of Arctic ice cover on the basis of SSM/I dataSovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa2013102182193 ComisoJ.C.Polar Oceans from Space2009New YorkSpringer EfronB.Netraditsionnye metody mnogomernogo statisticheskogo analiza1988MoscowFinansy i statistika MarkusT.StroeveJ.C.MillerJ.Recent changes in Arctic sea ice melt onset, freezeup, and melt season lengthJ. Geophys. Res.2009114C1202410.1029/2009JC005436 SharkovE.A.Radioteplovoe distantsionnoe zondirovanie Zemli: fizicheskie osnovy. V 2-kh tomakh. Tom 12014MoscowIKI RAN ShepherdT.G.Atmospheric circulation as a source of uncertainly in climate change projectionsNature Geosci.201471070370810.1038/ngeo2253 TikhonovV.V.RepinaI.A.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.New algorithm for the reconstruction of ice cover concentration based on passive microwave sounding dataIssled. Zemli Kosmosa201423543 MeierW.N.HovelsrudG.K.van OortB.E.H.KeyJ.R.KovacsK.M.MichelCh.HaasCh.GranskogM.A.GerlandS.PerovichD.K.MakshtasA.ReistJ.D.Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activityRev. Geophys.2014 ParkinsonC.L.ComisoJ.C.On the 2012 record Arctic sea cover: Combined impact of preconditioning and an August stormGeophys. Res. Lett.2013401356136110.1002/grl.50349 IvanovV.V.AlekseevV.A.AlekseevaT.A.KoldunovN.V.RepinaI.A.SmirnovA.V.Is Arctic ice cover becoming seasonal?Issled. Zemli Kosmosa201345065 MsadekR.VecchiG.A.WiltonM.GudgelR.G.Importance of initial conditions in seasonal predictions of Arctic sea ice extentGeophys. Res. Lett.201441145208521510.1002/2014GL060799 ShitikovV.K.RozenbergG.S.Randomizatsiya i butstrep: statisticheskii analiz v biologii i ekologii s ispol’zovaniem R2013TolyattiKassandra StroeveJ.C.MarkusT.BoisvertL.MillerJ.BarrettA.Changes in Arctic melt season and implications for sea ice lossGeophys. Res. Lett.20144141216122510.1002/2013GL058951 Agarwal, S., Moon, W., and Wettlaufer, J.S., Trends, noise and re-entrant persistence in Arctic sea ice, Proc. R. Soc. A, 2012, vol. 468. doi 10.1098/rspa.2011.0728 RennerA.H.H.GerlandS.HaasCh.SpreenG.BeckersJ.F.HansenE.NicolausM.GoodwinH.Evidence of Arctic sea ice thinning from direct observationsGeophys. Res. Lett.201441145029503610.1002/2014GL060369 TikhonovV.V.RepinaI.A.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.A complex algorithm for determining the ice situation of polar regions based on satellite microwave radiometry (VASIA 2)Issled. Zemli Kosmosa201527893 ErmakovD.M.RaevM.D.SuslovA.I.SharkovE.A.Electronic database of multi-year global thermal radio field of the Earth in the context of a multi-scale study of the ocean–atmosphere systemIssled. Zemli Kosmosa20071713 TikhonovV.V.BoyarskiiD.A.SharkovE.A.RaevM.D.RepinaI.A.IvanovV.V.AlexeevaT.A.KomarovaN.Yu.Microwave model of radiation from the multilayer “ocean–atmosphere” system for remote sensing studies of the polar regionsProg. Electromagn. Res. B20145912313310.2528/PIERB14021706 KhromovS.P.PetrosyantsM.A.Meteorologiya i klimatologiya2006MoscowMGU SpreenG.KaleschkeL.HeygsterG.Sea ice remote sensing AMSR-E 89-GHz channelsJ. Geophys. Res.2008113C02S03 VavrusS.HarrisonS.P.The impact of sea-ice dynamics on the Arctic climate systemClim. Dyn.2003207–8741757 KapschM.-L.GravensenR.G.TjernstromM.Springtime atmospheric energy transport and the control of Arctic summer sea-ice extentNature Clim. Change20133874474810.1038/nclimate1884 LerouxM.Global Warming: Myth or Reality? The Erring Ways of Climatology2005New YorkSpringer/PRAXIS T. Markus (6678_CR9) 2009; 114 M. Leroux (6678_CR8) 2005 A.H.H. Renner (6678_CR13) 2014; 41 V.V. Tikhonov (6678_CR22) 2015; 2 W.N. Meier (6678_CR10) 2014 D.M. Ermakov (6678_CR4) 2007; 1 6678_CR1 C.L. Parkinson (6678_CR12) 2013; 40 V.V. Tikhonov (6678_CR19) 2013; 10 G. Spreen (6678_CR17) 2008; 113 S.P. Khromov (6678_CR7) 2006 M.-L. Kapsch (6678_CR6) 2013; 3 V.V. Tikhonov (6678_CR21) 2014; 2 J.C. Stroeve (6678_CR18) 2014; 41 S. Vavrus (6678_CR23) 2003; 20 T.G. Shepherd (6678_CR15) 2014; 7 V.V. Tikhonov (6678_CR20) 2014; 59 V.V. Ivanov (6678_CR5) 2013; 4 R. Msadek (6678_CR11) 2014; 41 V.K. Shitikov (6678_CR16) 2013 J.C. Comiso (6678_CR2) 2009 E.A. Sharkov (6678_CR14) 2014 B. Efron (6678_CR3) 1988 |
| References_xml | – reference: LerouxM.Global Warming: Myth or Reality? The Erring Ways of Climatology2005New YorkSpringer/PRAXIS – reference: IvanovV.V.AlekseevV.A.AlekseevaT.A.KoldunovN.V.RepinaI.A.SmirnovA.V.Is Arctic ice cover becoming seasonal?Issled. Zemli Kosmosa201345065 – reference: ShitikovV.K.RozenbergG.S.Randomizatsiya i butstrep: statisticheskii analiz v biologii i ekologii s ispol’zovaniem R2013TolyattiKassandra – reference: StroeveJ.C.MarkusT.BoisvertL.MillerJ.BarrettA.Changes in Arctic melt season and implications for sea ice lossGeophys. Res. Lett.20144141216122510.1002/2013GL058951 – reference: ErmakovD.M.RaevM.D.SuslovA.I.SharkovE.A.Electronic database of multi-year global thermal radio field of the Earth in the context of a multi-scale study of the ocean–atmosphere systemIssled. Zemli Kosmosa20071713 – reference: TikhonovV.V.RepinaI.A.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.New algorithm for the reconstruction of ice cover concentration based on passive microwave sounding dataIssled. Zemli Kosmosa201423543 – reference: EfronB.Netraditsionnye metody mnogomernogo statisticheskogo analiza1988MoscowFinansy i statistika – reference: VavrusS.HarrisonS.P.The impact of sea-ice dynamics on the Arctic climate systemClim. Dyn.2003207–8741757 – reference: TikhonovV.V.RepinaI.A.AlekseevaT.A.IvanovV.V.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.Reconstruction of the concentration of Arctic ice cover on the basis of SSM/I dataSovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa2013102182193 – reference: ParkinsonC.L.ComisoJ.C.On the 2012 record Arctic sea cover: Combined impact of preconditioning and an August stormGeophys. Res. Lett.2013401356136110.1002/grl.50349 – reference: MsadekR.VecchiG.A.WiltonM.GudgelR.G.Importance of initial conditions in seasonal predictions of Arctic sea ice extentGeophys. Res. Lett.201441145208521510.1002/2014GL060799 – reference: SpreenG.KaleschkeL.HeygsterG.Sea ice remote sensing AMSR-E 89-GHz channelsJ. Geophys. Res.2008113C02S03 – reference: TikhonovV.V.BoyarskiiD.A.SharkovE.A.RaevM.D.RepinaI.A.IvanovV.V.AlexeevaT.A.KomarovaN.Yu.Microwave model of radiation from the multilayer “ocean–atmosphere” system for remote sensing studies of the polar regionsProg. Electromagn. Res. B20145912313310.2528/PIERB14021706 – reference: MarkusT.StroeveJ.C.MillerJ.Recent changes in Arctic sea ice melt onset, freezeup, and melt season lengthJ. Geophys. Res.2009114C1202410.1029/2009JC005436 – reference: SharkovE.A.Radioteplovoe distantsionnoe zondirovanie Zemli: fizicheskie osnovy. V 2-kh tomakh. Tom 12014MoscowIKI RAN – reference: KapschM.-L.GravensenR.G.TjernstromM.Springtime atmospheric energy transport and the control of Arctic summer sea-ice extentNature Clim. Change20133874474810.1038/nclimate1884 – reference: KhromovS.P.PetrosyantsM.A.Meteorologiya i klimatologiya2006MoscowMGU – reference: TikhonovV.V.RepinaI.A.RaevM.D.SharkovE.A.BoyarskiiD.A.KomarovaN.Yu.A complex algorithm for determining the ice situation of polar regions based on satellite microwave radiometry (VASIA 2)Issled. Zemli Kosmosa201527893 – reference: ShepherdT.G.Atmospheric circulation as a source of uncertainly in climate change projectionsNature Geosci.201471070370810.1038/ngeo2253 – reference: ComisoJ.C.Polar Oceans from Space2009New YorkSpringer – reference: Agarwal, S., Moon, W., and Wettlaufer, J.S., Trends, noise and re-entrant persistence in Arctic sea ice, Proc. R. Soc. A, 2012, vol. 468. doi 10.1098/rspa.2011.0728 – reference: MeierW.N.HovelsrudG.K.van OortB.E.H.KeyJ.R.KovacsK.M.MichelCh.HaasCh.GranskogM.A.GerlandS.PerovichD.K.MakshtasA.ReistJ.D.Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activityRev. Geophys.2014 – reference: RennerA.H.H.GerlandS.HaasCh.SpreenG.BeckersJ.F.HansenE.NicolausM.GoodwinH.Evidence of Arctic sea ice thinning from direct observationsGeophys. Res. Lett.201441145029503610.1002/2014GL060369 – volume-title: Meteorologiya i klimatologiya year: 2006 ident: 6678_CR7 – volume: 41 start-page: 1216 issue: 4 year: 2014 ident: 6678_CR18 publication-title: Geophys. Res. Lett. doi: 10.1002/2013GL058951 – volume-title: Randomizatsiya i butstrep: statisticheskii analiz v biologii i ekologii s ispol’zovaniem R year: 2013 ident: 6678_CR16 – ident: 6678_CR1 doi: 10.1098/rspa.2011.0728 – volume: 20 start-page: 741 issue: 7–8 year: 2003 ident: 6678_CR23 publication-title: Clim. Dyn. doi: 10.1007/s00382-003-0309-5 – volume: 41 start-page: 5208 issue: 14 year: 2014 ident: 6678_CR11 publication-title: Geophys. Res. Lett. doi: 10.1002/2014GL060799 – volume: 4 start-page: 50 year: 2013 ident: 6678_CR5 publication-title: Issled. Zemli Kosmosa doi: 10.7868/S0205961413040076 – volume: 113 start-page: C02S03 year: 2008 ident: 6678_CR17 publication-title: J. Geophys. Res. doi: 10.1029/2005JC003384 – volume-title: Global Warming: Myth or Reality? The Erring Ways of Climatology year: 2005 ident: 6678_CR8 – volume: 10 start-page: 182 issue: 2 year: 2013 ident: 6678_CR19 publication-title: Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa – volume: 1 start-page: 7 year: 2007 ident: 6678_CR4 publication-title: Issled. Zemli Kosmosa – volume-title: Netraditsionnye metody mnogomernogo statisticheskogo analiza year: 1988 ident: 6678_CR3 – volume-title: Radioteplovoe distantsionnoe zondirovanie Zemli: fizicheskie osnovy. V 2-kh tomakh. 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| SubjectTerms | Algorithms Arctic ice Arctic Ice Cover State Monitoring Based on Satellite Data Climatology Confidence intervals Earth and Environmental Science Earth Sciences Exploration Geophysics/Geodesy Ice Ice cover Ice environments Marine Mathematical analysis Multichannel communication Polynomials Regression analysis Satellite sounding Satellites Space research Statistical analysis |
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| Title | Peculiarities of stochastic regime of Arctic ice cover time evolution over 1987–2014 from microwave satellite sounding on the basis of NASA team 2 algorithm |
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