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Simulating Past and Future Mass Balance of Place Glacier Using a Physically-Based, Distributed Glacier Mass Balance Model

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Simulating Past and Future Mass Balance of Place Glacier Using a Physically-Based, Distributed Glacier Mass Balance Model Simulating Past and Future Mass Balance of Place Glacier Using a Physically-Based, Distributed Glacier Mass Balance Model by RaJU Aryal B Sc . Tribhuvan Umvers1ty, Nepal. 1995 M.Sc. Tnbhuvan Unt\ersity. Nepal, 1999 M Sc, Nagoya Unt\ersity. Japan, 2002 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN NATURAL RESOURCES AND ENVIRONMENTAL STUDIES UNIVERSITY OF NORTHERN BRITISH COLUMBIA (Prince George) April2015 © Raju Aryal2015 Abstract The objective of this study is to develop a physically-based distributed glacier mass balance (GMB) model for Place Glacier, Brittsh Columbia, Canada, and apply the model to develop the historic and the future mass balance The model Is forced with climate data from Regional Atmospheric Modelmg System (RAMS) mesoscale atmospheric model output from 1979-2008 for developmg htstonc mass balance on Place Glacter The model IS also run in the future (2009-2040) to develop a proJeCtiOn of mass balance. The model simulated the histone glacter-wtde summer and wmter balance on Place Glacier satisfactorily. For all years, root mean squared error (RMSE) m simulated summer and winter balance are 0.43 m water equivalent (we) and 0.27 m w.e., respectively. Over the period of 29 years, the model simulated a cumulative net mass balance of -33.72 m w.e. The model outperformed both empirical temperature index (TI) and enhanced TI models in simulating summer balance on Place Glacier when forced with the same RAMS variables. A linear regression model based on Singular Value Decomposition (SVD) technique is used for downscaling future climate projections from a suite of Global Climate Models (GCMs). The cross-validation of downscaled daily air temperature showed a strong correlation with the validation dataset (r2 =0.85, p <0.05). However, the RMSE in downscaled daily air temperature is large {=2.4°C). With spatially average correlation of 1 0.38 and RMSE of 7.5 mm dai , the model for daily precipitation performed less satisfactorily in downscaling large-scale precipitation. For all variables, the error statistics improved with the monthly model. Future GCM projections from CanESM2, MIROC-ESM, MPI-ESM-LR, and HadGEM2-ES, are considered for downscaling. 11 CanESM2 predicted a large negative glacier-wide net mass balance of -2.50 m w.e. for Place Glacier in the future. For the remaining GCMs. the average of net mass balance is -0.96 m w .e. The average of the cumulative mass loss predicted from GCMs other than CanESM2 is -31 m w.e From 2009-2040. CanESM2, MIROC, MPI and HadGEM2 predicted an area loss of 52°o, 28°o, l8°o and 22°o, respectively Overall, all downscaled GCMs. except CanESM2, performed better m predtctmg future mass balance for Place Glacier. 111 Table of Contents Abstract II Table of Contents IV List of Tables Vlll List of Figures X List of Symbols XV Acknowledgements XX I Introduction 1.1 Statement of the Problem 2 1.2 Thesis Objectives 3 1.3 Literature Review 4 1.3.1 Global Glacier Changes 4 1.3.2 Glacier Changes in Conterminous North America 10 1.3.3 Glacier Mass balance Modelling 16 1.3.4 Climate Downscaling 20 1.4 Thesis Outline 25 2 Study Area and Previous Studies 27 2.1 Introduction 27 2.2 Study Area 27 2.3 Mass Balance Measurements 30 2.3.1 Winter Balance 33 2.3.2 Summer Balance 34 2.3.3 Net Balance 35 2.3.4 Source ofUncertainty 35 2.4 Previous Studies 37 IV 3 Data 42 3.1 Glacier Mass Balance Data 42 3.2 Meteorological Data 43 3.2 1 Climate Station Data 43 3.2.2 RAMS Mesoscale Data 44 3.2.3 NCEP NCAR Reanalysis Data 46 3.2 4 GCM Future ProjectiOns 48 3.3 Geomatic Data 50 4 Preparation of RAI\JS Data for Gl\18 l\lodelling 52 4.1 Introduction 52 4.2 RAMS Data Retrieval and B1as CorrectiOn 53 4.3 Temperature Lapse Rate 57 4.4 Precipitation Gradient 59 4.5 Elevation Correction 61 4.6 Results and Discussion 62 4.6.1 Bias Correction 62 4.6.2 Temperature Lapse Rate 70 4.6.3 Precipitation Gradient 73 4.6.4 Elevation Correction 76 4. 7 Conclusion 78 5 Glacier Mass Balance l\1odelling 81 5.1 Introduction 81 5.2 GMB Model Formulation 82 5.2.1 Modelling Winter Balance 83 5.2.2 SEB Melt Model 83 5.2.3 Subsurface Heat Flux and Surface Temperature 91 5.2.4 Precipitation and Air Temperature Distribution 93 5.2.5 Glacier Area Estimation 93 v 5.2.6 Model Calibration and Parameters 97 5.2.7 Model Setup and Implementation 99 5 2.8 Model Sensitivity Test and Expenment 100 5 3 Empirical Melt Models 102 5.3 I TI Melt Model 102 5.3 2 Enhanced TI Melt Model 103 5.4 Model Validation 104 5.5 Results and Discussion 104 5.5.1 Mass Balance Vahdat10n 104 5.5.2 Surface Temperature and Energy Balance 117 5.5.3 Model SensitiVIty and Expenment 127 5.5.4 Empirical Melt Models 130 5.6 Conclusion 135 6 Climate Downscaling 138 6.1 Introduction 138 6.2 Methods 139 6.2.1 SVD Downscaling Model 139 6.2.2 Predictors and Predictands 142 6.2.3 GCM Climate Downsca1ing 144 6.3 Results and Discussion 147 6.3. 1 Spatio-temporal Organization 147 6.3.2 SVD Downscaling Model 157 6.3.3 SVD Model Validation 164 VI 6.3.4 GCM Climate Downscaling 174 6.4 Conclusion 187 7 Future Gl\18 Projection 192 7. I Introduction 192 7.2 Methods 193 7.3 Btas Estimation 196 7.4 Results and Discussion 198 7.4.1 Input Data 198 7 4.2 Energy Balance 199 7.4.3 Mass Balance 206 7.5 Conclusion 215 8 Conclusion 221 8.1 Summary of Key Findings 221 8.1.1 RAMS Variables 221 8.1.2 GMB Model Development and Validation 223 8.1.3 Climate downscaling 227 8.1.4 Future GMB projection 229 8.2 Shortcomings and Suggestions for Future Research 233 References Cited Vll List of Tables 1.1 Specific mass balance, area loss, volume loss, and contribution to global sea level rise from different glacier systems around the world ( 1961-2006) 8 3.1 Summary of climate station data and GMB data used m the present study 44 3.2 Details on gridded climate data used m the study 50 4.1 Calculated lapse rate of RAMS summer air temperature, vertical gradient of hourly summer precipitatiOn, and vertical gradient of RAMS total wmter precipitation 76 4.2 Difference m elevatiOn between RAMS and OEM topography for locatiOn nearest to selected glacier sites 77 5.1 Glacier Mass balance (GMB) model parameters 98 5.2 GMB model sensitivity to changes m different climate variables 128 5.3 G MB model sensitivity to changes m model parameters relative to the control run in 1980 129 5.4 Inter-comparison of different melt models· skill in reproducing glacier-wide measured summer balance ( Bs) on Place Glacier 134 6.1 Percentage of total variance explained by the first three EOF modes for different NCEP and RAMS variables 151 6.2 Characterization of the leading three SVD modes for the calibration period 158 6.3 Optimized SVD model parameters and coefficient of determination ( 1979-1993) 164 6.4 Cross-validation of SVD model using 1994-2008 dataset 165 6.5 Spatially averaged RMSE errors in different fields and for different GCMs 179 6.6 Optimized SVD model parameters and coefficient of determination ( 1979-2008) 181 Vlll 7.1 Biases (GCM-RAMS) in different SEB components from different GCMs relative to the values estimated using RAMS climate fields 200 2 7.2 Calculated averaged values (W m' ) of energy balance components at the terminus along the mid-line of Place Glacier over the summer season ( 1 June-30 September) from 2009- 2040 203 7.3 Bias m glacier-wide summer (B.) and wmter (B") balance modelled from different GCMs in the historic period ( J 961-2005) 207 IX List of Figures 1.1 Global specific glacier mass balance change from 1961 to 2006 7 1.2 Glacier mass balance change in contermmous North Amenca from 1961 to 2006 14 2.1 Place Glacier study area 29 2.2 A network of ablatiOn stakes and snow pits for mass balance measurement, Place Glacier 32 3.1 NARR, RAMS outer and RAMS Inner domain 45 3.2 Topography of inner 8 km RAMS domam 46 3.3 NCEP and RAMS domain used m developing the downscalmg model 47 3.4 Place Glacier OEM used in the GMB model 51 4.1 Box plot of different RAMS variables used m the study 54 4.2 Bias correction factors for RAMS daily precipitatiOn 63 4.3 RAMS bias corrected daily precipitation 65 4.4 Difference between bias corrected daily RAMS precipitation and PRISM precipitation 66 4.5 Bias correction offsets for RAMS daily air temperature 68 4.6 RAMS bias corrected daily air temperature 69 4.7 Difference between bias corrected daily RAMS air temperature and PRISM temperature 69 4.8 Hourly time series of calculated air temperature lapse rate for summer months 71 4.9 Comparison between RAMS bias corrected air temperature and air temperature measured at on-glacier and off-glacier sites 73 4.10 Vertical gradient of hourly summer precipitation 75 4.11 Vertical gradient of total winter season precipitation 75 X 4. 12 RAMS grid cell location nearest to Helm Glacier, Place Glacier, Bridge Glacier, and Tiedemann Glacier in the southern Coast Mountains selected for determining elevation difference 77 5.1 Comparison between simulated and measured glacier-wtde (a) summer balance Bs, (b) winter balance B", and (c) annual net balance Ba from 1980-2008, Place Glacier 108 5.2 Place Glacier area stmulated by the GMB model from 1980-2008 110 5.3 Comparison between simulated and measured glacter-wtde (a) cumulative annual net mass balance, and (b) annual net mass balance time senes for Place Glacier over a period from 1980-2008 1 I I 5.4 Simulated ELA for Place Glacter 113 5.5 Spatially-distributed net mass balance on Place Glacter modelled for (a) 1980, (b) 1990, (c) 2000, and (d) 2008 114 5.6 Comparison between measured and simulated elevation-wise b for (a) 1981
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