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- Author or Editor: T. S. Kang x
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Abstract
As the highest mountain on Earth, Mount Everest is an iconic peak that offers an unrivalled natural platform for measuring ongoing climate change across the full elevation range of Asia’s water towers. However, Everest’s extreme environment challenges data collection, particularly on the mountain’s upper slopes, where glaciers accumulate mass and mountaineers are most exposed. Weather stations have operated on Everest before, including the world’s previous highest, but coverage has been sparse in space and time. Here we describe the installation of a network of five automatic weather stations (AWSs), including the two highest stations on Earth (8,430 and 7,945 m MSL) which greatly improves monitoring of this iconic mountain. We highlight sample applications of the new data, including an initial assessment of surface energy fluxes at Camp II (6,464 m MSL) and the South Col (7,945 m MSL), which suggest melt occurs at both sites, despite persistently below-freezing air temperatures. This analysis indicates that melt may even be possible at the 8,850 m MSL summit, and prompts a reevaluation of empirical temperature index models used to simulate glacier melt in the Himalayas that focus only on air temperature. We also provide the first evaluation of numerical weather forecasts at almost 8,000 m MSL and use of model output statistics to reduce forecast error, showcasing an important opportunity to improve climber safety on Everest. Looking forward, we emphasize the considerable potential of these freely available data for understanding weather and climate in the Himalayas and beyond, including tracking the behavior of upper-atmosphere winds, which the AWS network is uniquely positioned to monitor.
Abstract
As the highest mountain on Earth, Mount Everest is an iconic peak that offers an unrivalled natural platform for measuring ongoing climate change across the full elevation range of Asia’s water towers. However, Everest’s extreme environment challenges data collection, particularly on the mountain’s upper slopes, where glaciers accumulate mass and mountaineers are most exposed. Weather stations have operated on Everest before, including the world’s previous highest, but coverage has been sparse in space and time. Here we describe the installation of a network of five automatic weather stations (AWSs), including the two highest stations on Earth (8,430 and 7,945 m MSL) which greatly improves monitoring of this iconic mountain. We highlight sample applications of the new data, including an initial assessment of surface energy fluxes at Camp II (6,464 m MSL) and the South Col (7,945 m MSL), which suggest melt occurs at both sites, despite persistently below-freezing air temperatures. This analysis indicates that melt may even be possible at the 8,850 m MSL summit, and prompts a reevaluation of empirical temperature index models used to simulate glacier melt in the Himalayas that focus only on air temperature. We also provide the first evaluation of numerical weather forecasts at almost 8,000 m MSL and use of model output statistics to reduce forecast error, showcasing an important opportunity to improve climber safety on Everest. Looking forward, we emphasize the considerable potential of these freely available data for understanding weather and climate in the Himalayas and beyond, including tracking the behavior of upper-atmosphere winds, which the AWS network is uniquely positioned to monitor.
Abstract
Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).
The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.
Abstract
Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).
The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.
