Use of snow cover derived frotn satellite passive tnicrowave data as an indicator of clitnate change

Transcription

1 Annals f Glacilgy Internatinal Glacilgical Sciety Use f sn cver derived frtn satellite passive tnicrave data as an indicatr f clitnate change B. E. GOODISON AND A. E. WALKER Climate Research Branch, Canadian Climate Centre, 4905 Dufferin Street, Dnsvie, Ontari, Canada M3H 5T4 ABSTRACT. T assess future glbal change, mnitring f the climate system thrugh bservatin and analysis f seasnal and interannual fluctuatins f climate variables is necessary. Cryspheric elements such as sn cver are ften seen as sensitive indicatrs and integratrs f basic climate cnditins and hence an indicatr f reginal and glbal change. Sn-cver elements hich may serve as signals f variability and change are discussed ith respect t the effective use f cnventinal and remtely sensed infrmatin. Cnventinal data are shn t be effective fr assessing questins f tempral variability, but are limited fr spatial variability. Passive micrave satellite data make an imprtant cntributin by prviding spatial and tempral infrmatin n sn ater equivalent (SWE) and the reginal distributin f snpack extent and state. Use f NIMBUS-7 SMMR (Scanning Multichannel Micrave Radimeter) data t develp a time series f SWE is assessed as a cmplement t cnventinal data. Limitatins f SMMR cverage cmpared t DMSP SSM/I (Special Sensr Micrave/Imager) cverage fr prductin f SWE maps fr climate change analysis are discussed. Althugh the[e are limitatins during early seasn sn cver, infrmatin derived frm passive micrave data is shn t be able t map and cmpute the areal cverage f SWE alling interannual cmparisn f the amunt f ater available, the date f peak accumulatin and the assciated spatial distributin. Hever, the satellite data recrd is still t shrt t establish any definitive trend in sn-cver variability. INTRODUCTION The recent rk f the Intergvernmental Panel n Climate Change (Hugh tn and thers, 1990) prvided the first majr assessment f the evidence f, and the ptential fr, climate change. It is acknledged that there are many uncertainties in the predictin f future climate changes, particularly ith respect t timing, magnitude, and reginal patterns f change. Mnitring f the climate system thrugh systematic bservatin and analysis f climate-related variables n a glbal, natinal and reginal basis is ne cntributin t imprving ur predictive capability. Identificatin f a significant change in a climate variable is an integral part f this mnitring. The highest pririty science questin identified by the EOS Panel n Physical Climate and Hydrlgy (Barrn, 1991) as t determine h the glbal and reginal strages and fluxes f ater, including sn cver in nrthern latitudes, ill interact ith a change in climate. The IPCC (Hugh tn and thers, 1990) nted that sn, ice and glacial extent are key variables in the glbal climate system and that accurate infrmatin n cryspheric changes is essential fr full understanding f this system. Cryspheric cmpnents are ften very sensitive indicatrs and integratrs f basic climate elements, such as precipitatin, temperature and slar radiatin. Variatins in sn cver, therefre, may ptentially be an effective indicatr f reginal and glbal change. But has the science cmmunity defined key elements hich shuld be rutinely mnitred n a reginal r glbal basis in rder t assess natural variability r t detect climate change? This paper prpses sme f the elements f sn cver hich may be a reflectin f climate variability and change, and discuss the ptential cntributin f cnventinal and remtely sensed data, ith particular emphasis n passive micrave data. SNOW COVER AS AN INDICATOR OF CLIMATE VARIABILITY AND CHANGE The develpment f a cnsistent, cmpatible data series fr spatial and tempral analyses f sn cver variability is critical. Extractin f a data set cmplete enugh t address the prblem at hand can be time cnsuming and cstly; this is even mre critical hen using satellite data since the vlume f data t be prcessed and analysed is much greater than cnventinal data. Inherently e feel that cryspheric changes shuld reflect changes in climate, but in the case f sn cver hat aspect(s) shuld e mnitr? Shuld it be the: change in sn ater equivalent at peak accumulatin fr selected lcatins? change in date f the peak accumulatin? nrthard retreat f the snline n a given date? 137

2 Gdisn and Walker: Sn cver frm sa"tellite data change in the spatial distributin f sn cver ver a regin? change in number f days f cntinuus sn cver? change in date f the beginning r end f cntinuus sn cver? change in the date f the nset f spring melt? change in the frequency f the number f melt events during the inter seasn? mapping and identificatin f tempral utliers frm the main accumulatin perid? , - - SNOW DEPTH _ TEMPERATURE E.. :r: b:: 30 z :J If ::E 10 f- Ul DAY (Julian) 100 Fig. 1. Daily sn depth (cm ) and maximum air temperature (abve OOG) fr Regina, Saskatchean, Octber 19 t April " , - - SNOW DEPTH lliilliilil TEMPERATURE.[ :r: 30 b:: z Ul Fig. 2. Daily sn depth (cm) and maximum alr temperature (abve OOG) Jr Regina, Saskatchean, Octber 1981 t April Fig. 3. Distributin f Atmspheric Envirnment Service ( AES) synptic and climate statins ith recrded sn depth measurements n 1 March These are nly sme f the ptential "signals" hich might be used. Hever, each requires data t be analyzed differently. The frequency f the cllectin, archiving, and prcessing f cnventinal r satellite data int prduct infrmatin may range frm daily, eekly r mnthly r be limited t a set date. The questin f integrating grund data cllected at specific lcatins and n certain dates ith remte sensing infrmatin must be addressed. These are fundamental issues hich must be assessed by researchers as they develp a strategy fr using sn cver as an indicatr f climate variability and change. Ideally, the rutine analysis f the reginal and glbal distributin, amunt and spatial variability f sn cver shuld invlve bth cnventinal and remtely sensed infrmatin. The netrk f surface based bservatins f sn cver is quite dense in the cn tinen tal regins f the mid-latitudes, althugh a cmprehensive archive f these bservatins des nt yet exist. Hever, data frm these statins can be used effectively, and still frm the basis f much f ur analyses f sn-cver variability and change n a reginal basis. Figures 1 and 2 cmpare sn cver fr Regina, Saskatchean fr the and inter seasns as depicted by the daily sn depth recrded at the synptic statin. Occurrences f daily maximum temperatures abve are als pltted and prvide an indicatin f pssible melt events. The t in ter perids are quite differen t, ith exhibiting a mre cntinuus sn cver ith fe inter melt events. A time series f these archived data can cntribute directly t addressing several f the elements nted abve. One can cmpare differences in the amunt and date f maximum sn accumulatin, the number f days f cntinuus sn cver, differences in the nset and ending f cntinuus sn cver, and s n. Hever, there is n infrmatin abut spatial variability f the sn cver. Other synptic r climate statin data uld be required in rder t derive spatial sncver infrmatin fr a regin f interest; the representativeness f the infrmatin uld depend upn the density f the statin netrk. Figure 3 depicts the distributin f synptic and climate statins ver the Canadian prairie regin here sn depth measurements ere recrded n 1 March Althugh there are 190 statins ithin the study area, their spatial distributin is nt cnsistent ver the regin; reliable infrmatin abut the spatial variability f sn depth uld be limited in the areas here the statins ere idely spaced. T get a better glbal scale perspective f sn extent ne uld mre likely use the NOAA/NESDIS eekly nrthern hemisphere sn-cver maps (sn/n-sn) derived frm visible satellite imagery (e.g. NOAA, GOES, METEOSAT) (Wiesnet and thers, 1987). Recent re-analysis by Rbinsn and thers (1991) f this prduct t assess the change in glbal sn cver shed a decrease in sn cver f abut 8% frm the mid-1970s t the late I 9s, ith sn extent being especially l in spring since Hever, these prducts can nly address the issue f sn-cvered area, and its variability, nt the ater equivalent r depth f a sn cver. e

3 Gdisn and Walker: Sn cver frm satellite data CONTRIBUTION OF P ASSIVE MICROWAVE INFORMATION One f the limitatins f the NOAA eekly chart is that it is a eekly cmpsite map derived frm visible satellite data; clud cver ill preclude accurate assessment f the sn extent n any given day and smetimes it is necessary t use the extent derived during the previus eek. This is a natural limitatin f all ptical data fr peratinal sn-cver mapping. A majr advance in the remte sensing f sn cver has been the develpment f algrithms t determine sn depth, ater equivalent, areal extent and sn state (et/dry) using micrave satellite sensrs having all-eather capabilities (Chang and thers, 1990; Gdisn, 1989; Gdisn and thers, 1986; Gdisn and thers, 1990; Hallikainen and J lma, 1992; Kunzi and thers, 1982). The authrs have develped algrithms t derive sn ater equivalent (SWE ), areal extent and snpack state (et/dry) fr pen regins frm satellite passive micrave data. These algrithms ere develped frm intensive airbrne studies ver the Canadian prairies and tested using NIMBUS-7 SMMR and DMSP SSMjI data fr this mid-cntinent regin (Gdisn, 1989; Gdisn and thers, 1986, 1990). They are n used t prduce pera tinal sn ater equivalent maps fr the prairie regin f Canada and the U.S.A. frm DMSP SSM /I data n a real-time basis (Thirkettle and thers, 1991 ). Althugh this research as initiated t supprt hydrlgy, the derived sn-cver infrmatin is n being assessed fr its ptential in the study f climate and glbal change. The use f sn cver as an indicatr f climate change and the effective use f passive micrave data fr such analyses became very real issues in the authrs' recent effrt t prduce a sn-cver time series fr the Canadian prairies fr the SMMR perid (197886) as a cntributin t the Internatinal Space Year (ISY). Althugh a reliable, accessible archive f passive micrave data is readily available since the launch f NIMBUS-7 SMMR in 1978, there are sme practical limitatins t the prductin f sn cver time series, even fr relatively small regins. The Canadian pen prairie regin spans a lngitude f nly 18 and cvers an area f nly 5125km 2. Hever, the SMMR sath as nly 0 km ide and due t satellite per cnstraints the sensr as perated every secnd day. Data frm t r three successive peratinal perids ere required t prvide cmplete cverage f the study area, resulting in a cmpsite map f sn cver fr a three-t-five day perid (Fig. 4). One is then frced t analyze a eekly cmpsite f sn cver rather than a daily prduct. Changing eather patterns ver the fiveday perid culd prduce changes in the SWE and distributin f sn cver and changes in the ccurrence and distributin f melt areas r et sn (resulting in an underestimatin f SWE and sn cvered area frm the micrave data) hich uld prduce a nn-representative picture f sn cver acrss the regin fr the perid. The situatin is much imprved fr the peratinal DMSP SSM/I data, hich have been available since 1987, as the 10km sath prvides daily cverage f this tlii(ijk. I I \. Fig. 4. Nimbus-7 SMMR sath cverage fr a single day ( DA r 1) ver Western Canada. Fr cmplete cverage f the suthern Canadian prairies, data ere required frm rbits ver a five-day interval ( DA 1, 3 and 5). rs particular study area, eliminating the prblem f crn psiting and interpretatin f ptential artificial variatins in sn cver (Fig. 5). Daily cverage f the study regin is preferred, alling easy cmparisn ith cnventinal grund based data frm synptic and climate stains and sn curses. Use f data frm SMMR and SSMjI t prduce a cnsistent, cmpatible time series f sn cver frm year t year ill be difficult and must be dne ith care and cautin; prducts frm the t satellite sensrs cannt be directly cmpared unless the same crn psiting prcedure is used fr bth data sets. Hence the authrs have restricted their current analysis fr the ISY study t the SMMR time perid. As nted abve, NOAA A VHRR data are used t prduce maps f glbal sn cver extent. These data, having a I km spatial reslutin, are als useful fr reginal analyses. Hever, nly infrmatin n sn extent can be derived and the prblem f clud cver can limit the effectiveness in assessing the actual dates f the beginning r end f sn cver. This has been particularly true fr the fall perid. It as expected that passive micrave data culd vercme sme f these limitatins. As nted abve, algrithms have been develped and are being validated t derive sn ater equivalent frm passive micrave data. The current 139

4 Gdisn and Walker: Sn cver frm satellite data Fig. 6. Distributin f sn ater equivalent ver the Canadian prairies fr 1 March 1981 derived frm Nimbus-7 SMMR data. Fig. 5. DMSP SSMII sath cverage ver Western Canada. Cmplete cverage f the Canadian prairie regin can be btained n a daily basis using ne ascending and ne descending pass. peratinal SWE algrithm uses the vertically plarized brightness temperature gradient beteen 37 and 19 GHz fr SMM/I and 37 and 18 GHz fr SMMR. Success has been achieved fr pen areas hen there is dry sn; SWE retrievals are generally ithin 10- mm f averaged grund-based survey measurements (Gdisn, 1989). Figures 6 and 7 sh the reginal sn ater equivalent as derived frm SMMR data fr the first eek f March in 1981 and The large difference in areal cverage and distributin beteen the t years is readily evident. It is this type f infrmatin, put int a eekly time series fr every sn cver seasn, that can be assessed fr variability and change analyses. It cmplements the infrmatin available fr individual statins (see Figs 1 and 2), and prvides the spatial distributin f SWE hich is nt available frm cnventinal r ther remte sensing bservatins. Fr example, Figure 1 shs there is n sn cver at Regina; Figure 6 illustrates h large an area is actually devid f sn. Figure 2 prvides a detailed daily time prfile f sn depth at Regina, suggesting extensive cver during the inter; Figure 7 shs that in fact the area f peak accumulatin as centered near Regina and ther areas ithin the regin had cnsiderably less sn. An acknledged limitatin f passive micrave data is that SWE cannt be derived hen there is liquid 1 Fig. 7. Distributin f sn ater equivalent ver the Canadian prairies fr 1 March 1982 derived frm Nimbus-7 SMMR data. ater ithin the snpack. Walker and Gdisn (1993) present a methd fr mapping areas f et sn, thus alling a mre accurate determinatin f areal extent. This technique has been validated fr SSM/I data but has nt been verified fr SMMR data and has nt been applied t this time series analysis f SMMR derived sn cver. Special attentin has been given t the ability f passive micrave t map sn cver during the beginning f the sn-cver seasn hen the pack is shall and may be et and the grund may be arm and unfrzen. Using the current algrithm, ithut the et sn indicatr, there has been sme difficulty in accurately mapping SWE. Figure 8 depicts the SWE derived fr 1 December 1983, hen there as a cmplete, but relatively shall, sn cver ver the entire prairie area. SWE measured at Atmspheric Envirnment Service sn curses ranged frm 8 t 33 mm, ith a minimum sn depth f 7-l0cm; temperatures ere bel freezing at the time f the satellite verpass. Hever, the shall light density sn as nt readily seen. Warm grund temperatures hich can prduce a melt layer at the bttm f the snpack are suspected t be ne f the cntributing causes t this prblem. Assessment f the limitatins during these cnditins is n-ging; at the mment this prblem des limit the effectiveness in accurately defining the nset f cntinuus

5 Gdisn and Walker: Sn cver frm satellite data Regina «> :J t U) u. a. fes.m feb 1/11. f EB. /1l3 fes./ll,- feb. I/ fes 1{82 feb.1/84 fes '100 DAY OF me SEASON Fig. 8. Distributin f sn ater equivalent ver the Canadian prairies fr 1 December 1983 derived frm Nimbus-7 SMMR data. Values are SWE in mm measured at sn curses at synptic eather statins. Fig. 9. Sn cver as a percentage f ttal area f the Canadian prairies regin fr the first eek f February, 1979 t sn cver ver an area. Research has been initiated in Eurpe n the use f 19,37 and 85 GHz data frm SSMjI t map shall sn cver (Nagler and Rtt, 1992). Sn-cver maps derived frm passive micrave data d ffer the great advantage f assessing changes in areal sn-cver ater equivalent during the inter and frm year t year, alling fr the current limitatins nted abve. Figures 9 and 10 cmpare the sn cvered area fr the first eeks f February and March fr 1979 t Early February shs the tendency t have a higher percentage f sn cvered area ver the entire prairie area than during early March. The March survey, hever, may give a better indicatin f the ptential fr sil misture recharge and runff than the earlier survey alne. Fr this perid, n clear trend emerges ith respect t sn cver ver the prairies; the SMMR recrd is just t shrt t dra cnclusins abut climate variability and change. When prperly cmbined ith SSM/I and cnventinal recrds, the recrd can be extended t all fr a mre accurate analysis f tempral variability. Anther substantial cntributin f the passive micrave derived sn cver prduct is its ability t map and cmpute the areal cverage f SWE, thus alling cmparisn f the amunt f ater available frm year t year. Figure II cmpares the area cvered by different amunts f sn ater equivalent, in this example using mm increments. Fr example, 1986 as characterized by bth l areal extent and l ater equivalent; 1981 n the ther hand had l areal extent, but had a cnsiderably higher ater equivalent here there as sn. Again n discernible trend is evident ver the SMMR perid fr area I cverage f a particular amunt f ater equivalent. The satellite data are the nly viable data surce fr assembling these statistics n sn cver and amunt n a eekly basis. The cnventinal recrd cannt prvide the spatial variability fr such an analysis and nly sn curses can prvide a measurement fswe; there are t fe f these sites t cnstruct an accurate areal picture n a eekly time scale thrughut the seasn. ««b :J t VJ -' < t t u. t Z l) UJ a. ffil > u 3: z U) -' u. t'j z U '0 0 AR. 'f79 MAR l/ll. MAR '1B3 MAR 1/85 MAR. ' / MAR 1182 MAR '/B4 MAR 1/1lti DAY OF THE SEASON Fig. 10. Sn cver as a percentage f ttal area f the Canadian prairies regin fr the first eek f March, 1979 t , Hr G- 10 a. MAR MAR 1/8. MAR. 1/83 MAR MAR 1!BO 1\<\.AR, 1/82 MAR. 1/8 4 I'I.AR. 1/85 DAY OF THE SEASON I _ O - D "'O 4(}-6(J _ -90"", E:2l m-n Fig. 11. Sn cver as a percent f ttal sn cvered area fr mm intervals f sn ater equivalent,fr the first eek f March, 1979 t / / / 141

6 Gdisn and Walker: Sn cver frm satellite data CONCLUSIONS Passive micrave data prvide anther very useful surce hich ill cntribute t the effective analysis f variatins in sn cver and climate. These data shuld be used in cnjunctin ith cnventinal grund bservatins and ther remtely sensed infrmatin, such as derived frm NOAA AVHRR, in rder t derive the mst representative time series f sn cver infrmatin. If key sn-cver elements are t be mnitred fr evidence f reginal r glbal climate change, the passive micrave data ffer the flling advantages: (i) determinatin f areal extent and sn ater equivalent ver a regin; (ii) determinatin f the date f peak accumulatin and the interannual variability ver a regin; (iii) assessment f the nset f spring melt, the areal extent f melt regins and the frequency f ccurrence. Hever, there are still limitatins t the exclusive use f sn-cver infrmatin frm passive micrave data fr change analysis. It is nt pssible t derive SWE hen the snpack is et, hence limiting its use t map SWE thrugh t the disappearance f the pack. There are still limitatins in sn-cver retrievals during the early fall perid hen the grund is arm and the sn pack is shall. These t factrs limit determinatin f the beginning and end f the perid f cntinuus sn cver. It is clear that reginal sn-cver algrithms are required, and at the mment, retrievals in frested and muntain regins are still under develpment. Finally, like ith all ther satellite retrievals f climate elements, there is the questin f the cnsistency and cmpatibility f the methd f measurement. Fr passive micrave data, sn cver prducts frm SMMR using a three-tfive day integratin perid ill nt be ttally cmpatible ith a daily prduct frm SSMjI. Daily sn cver infrmatin derived frm SSM /I data are ging t be much mre useful in develping a time series hich can be cmbined ith cnventinal data fr the assessment f sn cver variability and change. At the mment, hever, the satellite recrd is t shrt fr climate change analysis. ACKNOWLEDGEMENTS Supprt fr the cmpilatin f the ISY sn atlas fr the Canadian prairies has been received frm the Canadian Space Agency and frm the Glbal Warming Science Green Plan initiative led by Envirnment Canada. Special thanks are extended t David Olsn fr data display and analysis. REFERENCES Barrn, E Panel reprt n physical climate and hydrlgy. Earth Observer, 3 (7), Chang, A. T. C., J. L.Fster and D. K. Hall Effect f vegetatin cver n micrave sn ater equivalent estimates. In Prceedings f the Internatinal Sympsium n Remte Sensing and Water Resurces, Enschede, 142 The Netherlands, August Internatinal Assciatin f Hydrlgists, Gdisn, B. E Determinatin f areal sn ater equivalent n the Canadian prairies using passive micrave satellite data. IGARSS'89. 12th Canadian Sympsium n Remte Sensing. Quantitative remte sensing: an ecnmic tlfr the nineties, Vancuver, Canada, July 10-14, Vlume 3, Gdisn, B. E., 1. Rubinstein, F. W. Thirkettle and E.J. Langham Determinatin f sn ater equivalent n the Canadian prairies using micrave radimetry. Internatinal Assciatin f Hydrlgical Sciences Publicatin 155 (Sympsium at Budapest Mdelling Snmelt-Induced Prcesses), Gdisn, B. E., A. E. Walker and F. W. Thirkettle Determinatin f sn cver n the Canadian prairies using passive micrave data. In Prceedings f the Internatinal Sympsium n Remte Sensing and Water Resurces, Enschede, The Netherlands, August Internatinal Assciatin f Hydrlgists, Hallikainen, M. T. and P.A.Jlma Cmparisn f algrithms fr retrieval f sn ater equivalent frm Nimbus-7 SMMR data in Finland. IEEE Trans. Gesci. Remte Sensing, 30 (1), Hughtn, J. T., G.]. Jenkins and J.J. Ephraums, eds Climate change: the IPCC scientific assessment. Cambridge, Cambridge University Press. Kunzi, K. F., S. Patil and H. Rtt Sn-cver parameters retrieved frm Nimbus-7 Scanning Multichannel Micrave Radimeter (SMMR ) data. IEEE Trans. Gesci. Remte Sensing, GE (4), Nagler, T. and H. Rtt Develpment and intercmparisns f sn mapping algrithms based n SSM/I data. IGARSS'92. Internatinal Gescience and Remte Sensing Sympsium. Internatinal Space Year: space remte sensing, Hustn, Texas, May 26-29, Ne Yrk, Institute f Electrical and Electrnics Engineers, Rbinsn, D. A., F. T. Keimig and K. F. Deey Recent variatins in Nrthern Hemisphere sn cver. In Prceedings f the 15th NOAA Annual Climate Diagnstics Wrkshp, Asheville, NC, Octber 29-Nvember 2, Asheville, NC, Natinal Oceanic and Atmspheric Administratin, Thirkettle, F., A. Walker, B. Gdisn and D. Graham Canadian prairie sn cver maps frm near real-time passive micrave dat frm satellite data t user infrmatin. In Franklin, S., M. D. Thmpsn and F.J. Ahern, eds. Prceedings f the 14th Canadian Sympsium n Remte Sensing, Calgary, May Ottaa, Canadian Remte Sensing Sciety, Walker, A. E. and B. E. Gdisn Discriminatin f a et sn cver using passive micrave satellite data. Ann. Glacil., 17 (see paper in this vlume). Wiesnet, D. R., C. F. Rpeleski, G.J. Kukla and D. A. Rbinsn A dicussin f the accuracy f NO AA satellite-derived glbal seasnal sn cver measurements. Internatinal Assciatin f Hydrlgical Sciences Publicatin 166 (Sympsium at Vancuver 1987 Large-Scale Effects f Seasnal Sn Cver), The accuracy f references in the text and in this list is the respnsibility f the authrs, t hm queries shuld be addressed.