Quantification of warming climate induced changes in terrestrial Arctic river ice thickness and phenology

Quantification of warming climate induced changes in terrestrial Arctic river ice thickness and phenology tài liệu, giáo...

AMERICAN METEOROLOGICAL SOCIETY Journal of Climate EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version The DOI for this manuscript is doi: 10.1175/JCLI-D-15-0569.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available If you would like to cite this EOR in a separate work, please use the following full citation: Park, H., Y Yoshikawa, K Oshima, Y Kim, T Ngo-Duc, J Kimball, and D Yang, 2015: Quantification of warming climate-induced changes in terrestrial Arctic river ice thickness and phenology J Climate doi:10.1175/JCLI-D-15-0569.1, in press © 2015 American Meteorological Society Manuscript (non-LaTeX) Click here to download Manuscript (non-LaTeX) manuscript_v2_fn_II_adj.docx Quantification of warming climate-induced changes in terrestrial Arctic river ice thickness and phenology Hotaek Park1,*, Yasuhiro Yoshikawa2, Kazuhiro Oshima1, Youngwook Kim3, Thanh Ngo-Duc4, John S Kimball3, Daqing Yang5 237-0061, Japan Kitami, Hokkaido, 090-8507, Japan Institute of Arctic Climate and Environment Research, JAMSTEC, Yokosuka, Department of Civil and Environmental Engineering, Kitami Institute of Technology, 10 11 University of Montana, MT 59812, USA 12 13 National University, Hanoi, 10000, Vietnam 14 15 Saskatoon, SK S7N 3H5, Canada Numerical Terradynamic Simulation Group, College of Forestry & Conservation, The Department of Meteorology and Climate Change, Hanoi College of Science, Vietnam National Hydrology Research Centre, Environment Canada, 11 Innovation Boulevard, 16 17 * corresponding author 18 e-mail: park@jamstec.go.jp 19 For submission to: Journal of Climate 20 21 Abstract 22 We used a land process model (CHANGE) to quantitatively assess changes in the ice 23 phenology, thickness and volume of terrestrial Arctic rivers from 1979–2009 The 24 CHANGE model was coupled with a river routing and discharge model enabling 25 explicit representation of river ice and water temperature dynamics Model simulated 26 river-ice phonological dates and thickness were generally consistent with in situ river 27 ice data and landscape freeze-thaw (FT) satellite observations Climate data indicated an 28 increasing trend in winter surface air temperature (SAT) over the pan-Arctic during the 29 study period Nevertheless, the river-ice thickness simulations exhibited a thickening 30 regional trend independent of SAT warming, and associated with less insulation and 31 cooling of underlying river ice by thinning snow cover Deeper snow depth (SND) 32 combined with SAT warming decreased simulated ice thickness, especially for Siberian 33 rivers where ice thickness is more strongly correlated with SND than SAT Overall, the 34 Arctic river ice simulations indicated regional trends toward later fall freezeup, earlier 35 spring breakup and consequently a longer annual ice-free period The simulated ice 36 phenological dates were significantly correlated with seasonal SAT warming We find 37 that SND is an important factor for winter river-ice growth, while ice phenological 38 timing is dominated by seasonal SAT The mean total Arctic river-ice volume simulated 39 from CHANGE was 54.1 km3 based on the annual maximum ice thickness in individual 40 grid cells, while river ice volume for the pan-Arctic rivers decreased by 2.82 km3 41 (0.5%) over the 1979–2009 record Arctic river ice is shrinking as a consequence of 42 regional climate warming and coincident with other cryospheric components, including 43 permafrost, glaciers and sea ice 44 45 Keywords: Arctic river ice, ice phenology, ice thickness and volume, land surface model, 46 surface air temperature, snow depth, freeze-thaw 47 48 Introduction 49 Arctic river ice is one of the major components of the global cryosphere and 50 has a distinctive seasonal phenology characterized by freezeup and growth during fall 51 and winter, followed by breakup with the onset of spring thawing and the seasonal flood 52 pulse This seasonality is closely related to atmospheric heat fluxes Arctic warming that 53 was significant over the past several decades (Berkryaev et al 2010) has resulted in 54 changes in seasonal river-ice phenology, characterized to decreases in ice thickness and 55 earlier ice breakup (Magnuson et al 2000; Vuglinsky, 2006; Lesack et al 2014; 56 Shiklomanov and Lammers, 2014) In cold Arctic rivers, ice growth depends on surface 57 air temperature (SAT) during the cold season, but is also affected by the insulating 58 effect of winter snow cover (Prowse and Beltaos, 2002) Thinner snow accumulation 59 through the winter may enhance the growth of river ice A decreasing trend in winter 60 snow depth (SND) has been observed in the terrestrial Arctic during recent decades 61 (IPCC, 2013), particularly for North America (Dyer and Mote, 2006; Park et al., 2012) 62 Conversely, long-term in situ SND observations in Eurasia show an increasing trend 63 (Bulygina et al., 2009) These contrasting snow cover changes may promote divergent 64 trends in river ice phenology due to associated regional differences in surface insulation 65 However, Shiklomanov and Lammers (2014) documented that in situ observations at 66 Russian river mouths where ice thickness decreased had not revealed any significant 67 correlation between ice thickness and SND 68 Seasonal snowmelt in the Arctic typically begins with SAT warming in the 69 spring The timing of snow cover depletion is dependent on multiple factors, though a 70 thinner snowpack generally disappears more rapidly in the spring A regional trend 71 toward earlier snowpack depletion has been observed in the terrestrial Arctic (Kim et al., 72 2015) Earlier snow cover retreat in the spring reduces ice albedo and therefore 73 enhances the decay of river ice (Gray and Prowse, 1993) Earlier snowmelt, runoff and 74 the spring flood pulse from surrounding uplands also likely weakens and breaks up river 75 ice earlier (Rawlins et al., 2005; Lesack et al., 2014) While a thicker snowpack may 76 maintain a higher surface albedo and delay melting of underlying ice in the spring, it 77 increases runoff and river discharge from additional snowmelt, promoting rapid river ice 78 breakup once thawing is underway Bieniek et al (2011) found that increased winter 79 snow cover in Alaska contributed to earlier ice breakup by increasing spring river 80 discharge Previous studies thus provide conflicting reports regarding the role of snow 81 cover on river-ice phenology 82 Most previous studies on Arctic river ice phenology have used in situ 83 observations made at either river mouths or other specific locations within river basins 84 If geomorphic and climatic heterogeneities of observation sites are considered, in situ 85 observations are limited in terms of expanding to regional or global scales Satellite 86 observational data have been widely used to examine changes in seasonal ice phenology 87 (i.e., freezeup and breakup dates) from local to regional scales (Gatto, 1990; Murphy et 88 al., 2001; Pavelsky and Smith, 2004; Vincent et al., 2004) However, the inability of 89 current satellite observations to accurately determine snow and ice thicknesses 90 inherently limits their application to studies of winter ice processes underlying snow 91 cover These limitations may be partially mitigated through numerical modeling A 92 number of models have been developed that have simulated ice freezeup and breakup 93 dynamics on various rivers (Beltaos, 1997; Ma and Fukushima, 2003; Prowse and 94 Conly, 1998; Yoshikawa et al., 2014) However, most of these models have focused on 95 relatively short river reaches and small areas 96 During the past decades, a number of attempts have been made to quantify 97 changes in cryospheric components over polar and high latitude regions of the globe 98 (Lemke et al., 2007) These assessments tended to point to changes in Arctic sea ice and 99 Greenland ice sheet dynamics, because of their large influence on regional and global 100 climate Although the influence of river ice on climate may be relatively smaller, the 101 importance of river ice to biogeochemical and socioeconomic systems has been widely 102 recognized, especially at local to regional scales (Prowse and Beltaos, 2002) As 103 mentioned above, recent climate change has coincided with large apparent changes in 104 river-ice phenology To date, however, very few studies have provided quantitative 105 assessments of the areal extent and volume of the ice (Brooks et al., 2013), and 106 associated changes in ice phenology for terrestrial Arctic rivers, including potential 107 changes from recent climate warming 108 The main objective of this study was to quantitatively assess changes in 109 terrestrial Arctic river-ice phenology, including ice volume, thickness, and annual 110 freezeup and breakup dates during the period 1979–2009 The assessment was made by 111 using an improved coupled hydrological and biogeochemical process model (CHANGE, 112 Park et al., 2011) integrated with a river routing and discharge model that includes river 113 ice and water temperature (Tw) dynamics We also conducted a model sensitivity study 114 to delineate factors affecting river ice growth and breakup diagnosed by model 115 experiments using several scenarios that incorporated different climatic forcings The 116 model was applied over the entire terrestrial Arctic river system, including Hudson Bay 117 rivers (Fig 1) The simulated hydrological variables (e.g., discharge, ice thickness, and 118 Tw) were compared with available in situ observations at the mouths and upstream 119 stations of the major pan-Arctic river basins (Fig 1) A satellite microwave remote 120 sensing record of landscape freeze-thaw (FT) seasonal dynamics was also used to verify 121 simulated Arctic river-ice phenological dates in relation to satellite observed changes in 122 landscape frozen and non-frozen conditions at the pan-Arctic scale 123 124 Model description 125 2.1 Land surface model 126 CHANGE (Park et al., 2011) is a state-of-the-art process-based model that 127 calculates heat, water, and carbon fluxes in the atmosphere–land system, soil thermal 128 and hydrologic states, snow hydrology, plant stomatal physiology and photosynthesis 129 Park et al (2011) provides a detailed description of the CHANGE model, while model 130 elements pertaining to this study are summarized below CHANGE numerically solves 131 the heat and hydraulic conduction equations and represents permafrost dynamics 132 including an explicit treatment of soil FT phase transitions over up to 50.5 m of soil 133 depth A two-layer energy and mass balance approach is used to simulate snow 134 accumulation and snowmelt at the land surface The energy balance includes snowmelt, 135 refreezing, and changes in the snowpack heat content The water mass balance 136 represents snow accumulation/ablation, changes in snow water equivalent, and water 137 yield from the snowpack The snowpack is compacted by snow/ice metamorphism and 138 overburden, affecting snow density The calculated snow density and snow water 139 equivalent determine the thickness of the snowpack 140 Water at the soil surface is split between soil infiltration and surface runoff 141 The vertical water flux between soil column layers is numerically solved with Darcy’s 142 law If the surface soil layer becomes saturated, excess surface water is determined as 143 surface runoff For lower soil layers, CHANGE routes excess soil moisture to deeper figure Click here to download Rendered Figure figure1.docx Fig The seven major Arctic watersheds and river systems used for model evaluations in this study Gray areas represent other remaining pan-Arctic watersheds Black points represent river mouth locations for the seven watersheds and upstream hydrological stations used for evaluating model simulations Blue dots represent sub-basin outlet locations used for assessing contributions of the basins to the estimated total river-ice volume over all pan-Arctic rivers figure Click here to download Rendered Figure figure2.docx Ob at Salekhard (66.63 oN, 66.60 oE) Ob at HPS Novosibirskaya (54.80 oN, 82.95 oE) Yenisey at Igarka (67.48 oN, 86.48 oE) Lena at Kusur (70.68 oN, 127.39 oE) Lena at Tabaga (61.83 oN, 129.6 oE) Kolyma at Kolymskoye (68.73 oN, 158.72 oE) Yukon at Pilot Station (61.56 oN, 162.53 oW) 0.8 0.4 -0.4 -0.8 Yenisey at Bol Porog (65.63 oN, 90.02 oE) 0.8 0.4 -0.4 -0.8 Lena at Ust-Mil (59.63 oN, 133.03 oE) 0.8 0.4 -0.4 -0.8 Yukon at Eagle (66.47 oN, 141.11oW) Mackenzie at Arctic Red River (67.27 oN, 133.45 oW) Mackenzie at Liard River (61.44 oN, 123.28 oW) 0.8 0.4 -0.4 -0.8 Obs With ice effect Without ice effect Correlation Fig Daily discharges simulated by CHANGE (red) compared to observations (blue) at the mouths and upstream stations of the major Arctic rivers Two sets of daily discharge simulations are represented, including river ice effects (red line) and without representing river ice (red dot) The daily discharges were averaged from 1979–2008 except for Kolyma, which was averaged from 1979–1994; blue and red shades denote one temporal standard deviation ranges The bold black lines represent daily correlation coefficients between observations and simulations for the available periods within individual watersheds; dashed gray lines denote a 90% significance level figure Click here to download Rendered Figure figure3.docx O Y P S L Yn KK In KS Ol In: Indigirka KK: Kolyma-Kolymskoje KS: Kolyma-Srednekolymsk L: Lena O: Ob Ol: Olenek P: Pechora S: Sev.Dvina Y: Yenisey Fig Comparison of observed and simulated winter (Jan–Mar) mean snow depth at the mouths of the major Arctic watersheds Red (horizontal) and blue (vertical) lines represent standard deviations of the observations and simulations, respectively figure Click here to download Rendered Figure figure4.docx Sev Dvina at Ust Pinega* (1979–88) Ob at Salekhard* (1979–94) Yenisey at Igarka* (1979–89) Yenisey at Kuz’movka (1979–08) Yenisey at Bolporog (1979–08) Lena at Kusur* (1979–92) Lena at Tabaga (1979–08) Kolyma at Srednekolymsk* (1979–88) Mackenzie at Inuvik* (1979–96) Fig Simulated daily river ice thicknesses (lines) compared to available observations (dots) at the mouths and upstream stations of the major Arctic rivers The comparisons were made for the periods that observations were available for individual watersheds The shading and vertical lines on the dots denote one standard deviation ranges figure Click here to download Rendered Figure figure5.docx (a) (b) Fig The spatial distribution of correlation coefficient between CHANGE and FT-ESDR derived primary thaw dates (a) and frozen dates (b) for the 1979–2009 period The correlation is significant where r ≥ │0.30│ figure Click here to download Rendered Figure figure6.docx Fig Simulated daily river water temperatures (line) compared with available observations (dots) at the mouths of the major Arctic rivers The comparisons were made for the periods that observations were available for individual watersheds The shading and vertical lines on the dots denote one standard deviation ranges figure Click here to download Rendered Figure figure7.docx Siberia North America Fig Time series of anomalous annual freezing index (top), snow depth from January-March (middle), and maximum river-ice thickness (bottom) in Siberia (60– 73°N, 90–135°E, left) and North America (60–73°N, 215–260°E, right) Rivers The annual values of snow depth and maximum ice thickness represent model simulations rather than observations In the figures, the light gray lines denote annual values; black lines denote three-year-averages, and black dotted lines represent longer term trends for the 1979-2009 simulation record figure Click here to download Rendered Figure figure8.docx North America Freezeup date (days) Siberia Fig Time series of anomalous ice breakup date (top), freezeup date (middle), and ice-free duration (bottom) in Siberia (60–73°N, 90–135°E, left) and North America (60– 73°N, 215–260°E, right) Rivers The annual values of the three variables represent model simulations rather than observations In the figures, the light gray lines denote annual values; the black lines denote three-year-averages, and black dotted lines represent longer term trends for the 1979-2009 simulation period figure Click here to download Rendered Figure figure9.docx Fig Time series of anomalous freezing index (Oct–Apr, top), simulated winter mean snow depth (Jan–Mar, middle), and total ice volume (bottom) derived from model simulated maximum ice thickness (blue) and the degree-day ice growth model (red) over the pan-Arctic rivers Annual anomalies for freezing index and snow depth represent differences from the 1979–2009 period means Dotted lines represent the long-term trend over the 1979-2009 record Shaded areas in the bottom plot denote one temporal standard deviation ranges figure 10 Click here to download Rendered Figure figure10.docx Fig 10 Model estimated trend maps for freezing index during October–April (a), average snow depth for January–March (b), maximum river-ice thickness (c), ice breakup date (d), ice freezeup date (e), and annual ice-free duration (f) over the 1979– 2009 simulation period figure 11 Click here to download Rendered Figure figure11.docx Fig 11 Average differences in estimated maximum river-ice thickness for model experiments DSO_M (a), USO_M (b), DTO_M (c), UTO_M (d), DSUTO_M (e), and USDTO_M (f) against the control experiment (CTRL) for the 1979–2009 simulation period figure 12 Click here to download Rendered Figure figure12.docx Fig 12 Average differences in estimated river-ice breakup dates for model experiments DTA_M (a), UTA_M (b), DSO_MUTA_M (c), and USO_MUTA_M (d) from the control (CTRL) for the 1979–2009 simulation period figure 13 Click here to download Rendered Figure figure13.docx (a) (b) Fig 13 Differences in estimated maximum ice thickness (a) and ice breakup dates (b) for the individual model experiments from the CTRL for Siberia (black) and North America (gray) Rivers, defined in Fig 7, for the 1979–2009 simulation period figure 14 Click here to download Rendered Figure figure14.docx SND SAT Snowfall -3oC -2oC -1oC +1oC +2oC +3oC Surface air temperature Fig 14 Sensitivity of estimated maximum river-ice thickness to changes in overlying snowfall (circles) and surface air temperature (squares) during October–March The values reported from the individual model experiments are averaged over the pan-Arctic river systems defined in Figure The calculation was done by the same method used in Figures and on the basis of the annual differences in individual grid cells between the model experiments and the control for the 1979–2009 period figure 15 Click here to download Rendered Figure figure15.docx km3 Lena 10 Yenisey Ob Mackenzie Fig 15 Comparison of contribution rates of river-ice volume from basins with different climates and watershed areas (Fig 1) to the average total maximum ice volume 54.1 km3 over the pan-Arctic rivers Colors denote estimated average river-ice volume contributed by individual basins over the 1979–2009 period ... of 42 regional climate warming and coincident with other cryospheric components, including 43 permafrost, glaciers and sea ice 44 45 Keywords: Arctic river ice, ice phenology, ice thickness and. .. SAT warming, and associated with less insulation and 31 cooling of underlying river ice by thinning snow cover Deeper snow depth (SND) 32 combined with SAT warming decreased simulated ice thickness, ... climate warming 108 The main objective of this study was to quantitatively assess changes in 109 terrestrial Arctic river- ice phenology, including ice volume, thickness, and annual 110 freezeup and