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Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Reviews of Geophysics 58(3), (2020): e2019RG000672, doi:10.1029/2019RG000672., Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea‐level observing system, the knowledge of regional sea‐level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea‐level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea‐level change. Here we review the individual processes which lead to sea‐level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea‐level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea‐level observation network—particularly as related to satellite observations—in the improved scientific understanding of the contributors to regional sea‐level change., The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The authors acknowledge support from the National Aeronautics and Space Administration under Grants 80NSSC17K0565, 80NSSC170567, 80NSSC17K0566, 80NSSC17K0564, and NNX17AB27G. A. A. acknowledges support under GRACE/GRACEFO Science Team Grant (NNH15ZDA001N‐GRACE). T. W. acknowledges support by the National Aeronautics and Space Administration (NASA) under the New (Early Career) Investigator Program in Earth Science (Grant: 80NSSC18K0743). C. G. P was supported by the J. Lamar Worzel Assistant Scientist Fund and the Penzance Endowed Fund in Support of Assistant Scientists at the Woods Hole Oceanographic Institution.

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Reviews of Geophysics 58(3), (2020): e2019RG000672, doi:10.1029/2019RG000672., Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea‐level observing system, the knowledge of regional sea‐level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea‐level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea‐level change. Here we review the individual processes which lead to sea‐level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea‐level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea‐level observation network—particularly as related to satellite observations—in the improved scientific understanding of the contributors to regional sea‐level change., The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The authors acknowledge support from the National Aeronautics and Space Administration under Grants 80NSSC17K0565, 80NSSC170567, 80NSSC17K0566, 80NSSC17K0564, and NNX17AB27G. A. A. acknowledges support under GRACE/GRACEFO Science Team Grant (NNH15ZDA001N‐GRACE). T. W. acknowledges support by the National Aeronautics and Space Administration (NASA) under the New (Early Career) Investigator Program in Earth Science (Grant: 80NSSC18K0743). C. G. P was supported by the J. Lamar Worzel Assistant Scientist Fund and the Penzance Endowed Fund in Support of Assistant Scientists at the Woods Hole Oceanographic Institution.

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea‐level observing system, the knowledge of regional sea‐level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea‐level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea‐level change. Here we review the individual processes which lead to sea‐level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea‐level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea‐level observation network—particularly as related to satellite observations—in the improved scientific understanding of the contributors to regional sea‐level change

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991-2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km(2)) glacier with one of the greatest elevation ranges globally (264-6108m a. s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of -0.74 mw.e. a(-1)(+/-sigma = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model-and GRACE-derived mass change time series (R-2 = 0.58 and p << 0.001), and between summer (R-2 = 0.69 and p = 0.003) and annual (R-2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991-2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km(2)) glacier with one of the greatest elevation ranges globally (264-6108m a. s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of -0.74 mw.e. a(-1)(+/-sigma = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model-and GRACE-derived mass change time series (R-2 = 0.58 and p << 0.001), and between summer (R-2 = 0.69 and p = 0.003) and annual (R-2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991-2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km(2)) glacier with one of the greatest elevation ranges globally (264-6108m a. s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of -0.74 mw.e. a(-1)(+/-sigma = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model-and GRACE-derived mass change time series (R-2 = 0.58 and p << 0.001), and between summer (R-2 = 0.69 and p = 0.003) and annual (R-2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991-2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km(2)) glacier with one of the greatest elevation ranges globally (264-6108m a. s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of -0.74 mw.e. a(-1)(+/-sigma = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model-and GRACE-derived mass change time series (R-2 = 0.58 and p << 0.001), and between summer (R-2 = 0.69 and p = 0.003) and annual (R-2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991-2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km(2)) glacier with one of the greatest elevation ranges globally (264-6108m a. s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of -0.74 mw.e. a(-1)(+/-sigma = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model-and GRACE-derived mass change time series (R-2 = 0.58 and p << 0.001), and between summer (R-2 = 0.69 and p = 0.003) and annual (R-2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities to adapt on shorter time scales than traditional university curricula allow for, and therefore requires new modes of knowledge transfer. The universal applicability of data science tools to a broad range of problems has generated new opportunities to foster exchange of ideas and computational workflows across disciplines. In recent years, hack weeks have emerged as an effective tool for fostering these exchanges by providing training in modern data analysis workflows. While there are variations in hack week implementation, all events consist of a common core of three components: tutorials in state-of-the-art methodology, peer-learning and project work in a collaborative environment. In this paper, we present the concept of a hack week in the larger context of scientific meetings and point out similarities and differences to traditional conferences. We motivate the need for such an event and present in detail its strengths and challenges. We find that hack weeks are successful at cultivating collaboration and the exchange of knowledge. Participants self-report that these events help them both in their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration and cultivate best practices., Comment: 15 pages, 2 figures, submitted to PNAS, all relevant code available at https://github.com/uwescience/HackWeek-Writeup

Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities to adapt on shorter time scales than traditional university curricula allow for, and therefore requires new modes of knowledge transfer. The universal applicability of data science tools to a broad range of problems has generated new opportunities to foster exchange of ideas and computational workflows across disciplines. In recent years, hack weeks have emerged as an effective tool for fostering these exchanges by providing training in modern data analysis workflows. While there are variations in hack week implementation, all events consist of a common core of three components: tutorials in state-of-the-art methodology, peer-learning and project work in a collaborative environment. In this paper, we present the concept of a hack week in the larger context of scientific meetings and point out similarities and differences to traditional conferences. We motivate the need for such an event and present in detail its strengths and challenges. We find that hack weeks are successful at cultivating collaboration and the exchange of knowledge. Participants self-report that these events help them both in their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration and cultivate best practices., Comment: 15 pages, 2 figures, submitted to PNAS, all relevant code available at https://github.com/uwescience/HackWeek-Writeup

We present a detailed, complete glacier inventory for Alaska and neighboring Canada using multi-sensor satellite data from 2000 to 2011. For each glacier, we derive outlines and 51 variables, including center-line lengths, outline types and debris cover. We find 86 723 km(2) of glacier area (27 109 glaciers >0.025 km(2)), similar to 12% of the global glacierized area outside ice sheets. Of this area 12.0% is drained by 39 marine-terminating glaciers (74 km of tidewater margin), and 19.3% by 148 lake- and river-terminating glaciers (420 km of lake-/river margin). The overall debris cover is 11%, with considerable differences among regions, ranging from 1.4% in the Kenai Mountains to 28% in the Central Alaska Range. Comparison of outlines from different sources on >2500 km(2) of glacierized area yields a total area difference of similar to 10%, emphasizing the difficulties in accurately delineating debris-covered glaciers. Assuming fully correlated (systematic) errors, uncertainties in area reach 6% for all Alaska glaciers, but further analysis is needed to explore adequate error correlation scales. Preliminary analysis of the glacier database yields a new set of well-constrained area/length scaling parameters and shows good agreement between our area altitude distributions and previously established synthetic hypsometries. The new glacier database will be valuable to further explore relations between glacier variables and glacier behavior.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the similar to 198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999-2010 provided most of the outlines. Their total extent is estimated as 726 800 +/- 34 000 km(2). The uncertainty, about +/- 5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the similar to 198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999-2010 provided most of the outlines. Their total extent is estimated as 726 800 +/- 34 000 km(2). The uncertainty, about +/- 5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the 198000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726800 34000km2.The uncertainty, about 5%,is derived fromcarefulsingle-glacierand basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliablyis an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the $198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800 ` 34 000 km2. The uncertainty, about `5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

Many glaciological and hydrological studies require outlines of individual glaciers rather than total ice cover. Here we develop a new semi-automatic algorithm that uses a digital elevation model (DEM) and outlines of glacier complexes to calculate the extents of individual glaciers. The algorithm first applies hydrological modeling tools to a modified DEM to calculate flowsheds. It then merges flowsheds that belong to individual glaciers using a distance-based approach, whose required empirical parameters are derived from the Juneau Icefield area in Alaska. In this region, 2% of similar to 1300 glaciers were misclassified. The algorithm was validated on >25 000 km(2) of ice in other regions in Alaska and on >40 000 km(2) of ice in Arctic Canada, resulting in similar to 2% and similar to 3% misclassified glaciers, respectively. Results indicate that the algorithm is robust provided the DEM and the outlines are of good quality.

Glaciers distinct from the Greenland and Antarctic Ice Sheets are losing large amounts of water to the world's oceans. However, estimates of their contribution to sea level rise disagree. We provide a consensus estimate by standardizing existing, and creating new, mass-budget estimates from satellite gravimetry and altimetry and from local glaciological records. In many regions, local measurements are more negative than satellite-based estimates. All regions lost mass during 2003-2009, with the largest losses from Arctic Canada, Alaska, coastal Greenland, the southern Andes, and high-mountain Asia, but there was little loss from glaciers in Antarctica. Over this period, the global mass budget was -259 +/- 28 gigatons per year, equivalent to the combined loss from both ice sheets and accounting for 29 +/- 13% of the observed sea level rise.

Temperature and precipitation data from three weather stations in the St Elias Mountains of Alaska and northwestern Canada were used to drive one-dimensional (1-D) (elevation-dependent) and 0-D degree-day mass-balance models. Model outputs were optimized against a 10 day resolution time series of mass variability during 2003-07 obtained from Gravity Recovery and Climate Experiment (GRACE) mass concentration (mascon) solutions. The models explained 52-60% of the variance in the GRACE time series. Modelled mass variations matched the phase of the GRACE observations, and all optimized model parameters were within the range of values determined from conventional mass-balance and meteorological observations. We describe a framework for selecting appropriate weather stations and mass-balance models to represent glacier variations of large regions. There is potential for extending these calibrated mass-balance models forwards or backwards in time to construct mass-balance time series outside of the GRACE measurement window.