Articles | Volume 14, issue 10
https://doi.org/10.5194/tc-14-3565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-3565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Oct 2020
Research article |
| 29 Oct 2020
| 29 Oct 2020
Systematic bias of Tibetan Plateau snow cover in subseasonal-to-seasonal models
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Shuzhen Hu
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Pang-Chi Hsu
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Weidong Guo
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Jiangfeng Wei
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Related authors
No articles found.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
Short summary
Short summary
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
Short summary
Short summary
A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
Short summary
Short summary
Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Xiaolu Ling, Ying Huang, Weidong Guo, Yixin Wang, Chaorong Chen, Bo Qiu, Jun Ge, Kai Qin, Yong Xue, and Jian Peng
Hydrol. Earth Syst. Sci., 25, 4209–4229, https://doi.org/10.5194/hess-25-4209-2021, https://doi.org/10.5194/hess-25-4209-2021, 2021
Short summary
Short summary
Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system, for which a long-term SM product with high quality is urgently needed. In situ observations are generally treated as the true value to systematically evaluate five SM products, including one remote sensing product and four reanalysis data sets during 1981–2013. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
Short summary
Short summary
The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Yingxia Gao, Nicholas P. Klingaman, Charlotte A. DeMott, and Pang-Chi Hsu
Geosci. Model Dev., 13, 5191–5209, https://doi.org/10.5194/gmd-13-5191-2020, https://doi.org/10.5194/gmd-13-5191-2020, 2020
Short summary
Short summary
Both the air–sea coupling and ocean mean state affect the fidelity of simulated boreal summer intraseasonal oscillation (BSISO). To elucidate their relative effects on the simulated BSISO, a set of experiments was conducted using a superparameterized AGCM and its coupled version. Both air–sea coupling and cold ocean mean state improve the BSISO amplitude due to the suppression of the overestimated variance, while the former (latter) could further upgrade (degrade) the BSISO propagation.
Jun Ge, Andrew J. Pitman, Weidong Guo, Beilei Zan, and Congbin Fu
Hydrol. Earth Syst. Sci., 24, 515–533, https://doi.org/10.5194/hess-24-515-2020, https://doi.org/10.5194/hess-24-515-2020, 2020
Short summary
Short summary
We investigate the impact of revegetation on the hydrology of the Loess Plateau based on high-resolution simulations using the Weather Research and Forecasting (WRF) model. We find that past revegetation has caused decreased runoff and soil moisture with increased evapotranspiration as well as little response from rainfall. WRF suggests that further revegetation could aggravate this water imbalance. We caution that further revegetation might be unsustainable in this region.
Xiao-Lu Ling, Cong-Bin Fu, Zong-Liang Yang, and Wei-Dong Guo
Geosci. Model Dev., 12, 3119–3133, https://doi.org/10.5194/gmd-12-3119-2019, https://doi.org/10.5194/gmd-12-3119-2019, 2019
Short summary
Short summary
Observation and simulation can provide the temporal and spatial variation of vegetation characteristics, while they are not satisfactory for understanding the mechanism of the exchange between ecosystems and atmosphere. Data assimilation (DA) can combine the observation and models via mathematical statistical analysis. Results show that the ensemble adjust Kalman filter (EAKF) is the optimal algorithm. In addition, models perform better when the DA accepts a higher proportion of observations.
Xueqian Wang, Weidong Guo, Bo Qiu, Ye Liu, Jianning Sun, and Aijun Ding
Atmos. Chem. Phys., 17, 4989–4996, https://doi.org/10.5194/acp-17-4989-2017, https://doi.org/10.5194/acp-17-4989-2017, 2017
Short summary
Short summary
Land use or cover change is a fundamental anthropogenic forcing for climate change. Based on field observations, we quantified the contributions of different factors to surface temperature change and deepened the understanding of its mechanisms. We found evaporative cooling plays the most important role in the temperature change, while radiative forcing, which is traditionally emphasized, is not significant. This study provided firsthand evidence to verify the model results in IPCC AR5.
Zhongfeng Xu, Zhaolu Hou, Ying Han, and Weidong Guo
Geosci. Model Dev., 9, 4365–4380, https://doi.org/10.5194/gmd-9-4365-2016, https://doi.org/10.5194/gmd-9-4365-2016, 2016
Short summary
Short summary
This paper devises a new diagram called the vector field evaluation (VFE) diagram. The VFE diagram is a generalized Taylor diagram and is able to provide a concise evaluation of model performance in simulating vector fields (e.g., vector winds) in terms of three statistical variables. The VFE diagram can be applied to the evaluation of full vector fields or anomaly fields as needed. Some potential applications of the VFE diagram in model evaluation are also presented in the paper.
Xin Huang, Aijun Ding, Lixia Liu, Qiang Liu, Ke Ding, Xiaorui Niu, Wei Nie, Zheng Xu, Xuguang Chi, Minghuai Wang, Jianning Sun, Weidong Guo, and Congbin Fu
Atmos. Chem. Phys., 16, 10063–10082, https://doi.org/10.5194/acp-16-10063-2016, https://doi.org/10.5194/acp-16-10063-2016, 2016
Short summary
Short summary
We conducted a comprehensive modelling work to understand the impact of biomass burning on synoptic weather during agricultural burning season in East China. We demonstrated that the numerical model with fire emission, chemical processes, and aerosol–meteorology online coupled could reproduce the change of air temperature and precipitation induced by air pollution during this event. This study highlights the importance of including human activities in numerical-model-based weather forecast.
Weidong Guo, Xueqian Wang, Jianning Sun, Aijun Ding, and Jun Zou
Atmos. Chem. Phys., 16, 9875–9890, https://doi.org/10.5194/acp-16-9875-2016, https://doi.org/10.5194/acp-16-9875-2016, 2016
Short summary
Short summary
Basic characteristics of land–atmosphere interactions at four neighboring sites with different underlying surfaces in southern China, a typical monsoon region, are analyzed systematically. Despite the same climate background, the differences in land surface characteristics like albedo and aerodynamic roughness length due to land use/cover change exert distinct influences on the surface radiative budget and energy allocation and result in differences of near-surface micrometeorological elements.
Related subject area
Discipline: Snow | Subject: Atmospheric Interactions
On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice
Identifying airborne snow metamorphism with stable water isotopes
Seasonal Snow-Atmosphere Modeling: Let's do it
Understanding snow saltation parameterizations: lessons from theory, experiments and numerical simulations
A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations
From atmospheric water isotopes measurement to firn core interpretation in Adélie Land: a case study for isotope-enabled atmospheric models in Antarctica
Black carbon concentrations and modeled smoke deposition fluxes to the bare-ice dark zone of the Greenland Ice Sheet
Dynamics of the snow grain size in a windy coastal area of Antarctica from continuous in situ spectral-albedo measurements
Forcing and impact of the Northern Hemisphere continental snow cover in 1979–2014
On the energy budget of a low-Arctic snowpack
The role of sublimation as a driver of climate signals in the water isotope content of surface snow: laboratory and field experimental results
Synoptic control on snow avalanche activity in central Spitsbergen
Interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions as seen by X-ray absorption spectroscopy
Tracing devastating fires in Portugal to a snow archive in the Swiss Alps: a case study
Warm-air entrainment and advection during alpine blowing snow events
Quantifying the impact of synoptic weather types and patterns on energy fluxes of a marginal snowpack
Radar measurements of blowing snow off a mountain ridge
Brief communication: Rare ambient saturation during drifting snow occurrences at a coastal location of East Antarctica
Understanding snow bedform formation by adding sintering to a cellular automata model
Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations
Brief communication: Analysis of organic matter in surface snow by PTR-MS – implications for dry deposition dynamics in the Alps
Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars
Lara Foth, Wolfgang Dorn, Annette Rinke, Evelyn Jäkel, and Hannah Niehaus
The Cryosphere, 18, 4053–4064, https://doi.org/10.5194/tc-18-4053-2024, https://doi.org/10.5194/tc-18-4053-2024, 2024
Short summary
Short summary
It is demonstrated that the explicit consideration of the cloud dependence of the snow surface albedo in a climate model results in a more realistic simulation of the surface albedo during the snowmelt period in late May and June. Although this improvement appears to be relatively insubstantial, it has significant impact on the simulated sea-ice volume and extent in the model due to an amplification of the snow/sea-ice albedo feedback, one of the main contributors to Arctic amplification.
Sonja Wahl, Benjamin Walter, Franziska Aemisegger, Luca Bianchi, and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2024-745, https://doi.org/10.5194/egusphere-2024-745, 2024
Short summary
Short summary
Wind-driven airborne transport of snow is a frequent phenomenon in snow-covered regions and a process difficult to study in the field as it is unfolding over large distances. Thus, we use a ring wind tunnel with infinite fetch positioned in a cold-laboratory to study the evolution of the shape and size of airborne snow. With the help of stable water isotope analyses, we identify the hitherto unobserved process of airborne snow metamorphism that leads to snow particle rounding and growth.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
EGUsphere, https://doi.org/10.5194/egusphere-2024-489, https://doi.org/10.5194/egusphere-2024-489, 2024
Short summary
Short summary
Accurate information about atmospheric variables are needed to produce simulations of mountain snowpacks. Here we present a model which can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground, and that this leads to differences in the distribution of snow by the end of the winter. Overall, this model shows promise to improve forecasts of snow in mountains.
Daniela Brito Melo, Armin Sigmund, and Michael Lehning
The Cryosphere, 18, 1287–1313, https://doi.org/10.5194/tc-18-1287-2024, https://doi.org/10.5194/tc-18-1287-2024, 2024
Short summary
Short summary
Snow saltation – the transport of snow close to the surface – occurs when the wind blows over a snow-covered surface with sufficient strength. This phenomenon is represented in some climate models; however, with limited accuracy. By performing numerical simulations and a detailed analysis of previous works, we show that snow saltation is characterized by two regimes. This is not represented in climate models in a consistent way, which hinders the quantification of snow transport and sublimation.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, https://doi.org/10.5194/tc-18-849-2024, 2024
Short summary
Short summary
Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Christophe Leroy-Dos Santos, Elise Fourré, Cécile Agosta, Mathieu Casado, Alexandre Cauquoin, Martin Werner, Benedicte Minster, Frédéric Prié, Olivier Jossoud, Leila Petit, and Amaëlle Landais
The Cryosphere, 17, 5241–5254, https://doi.org/10.5194/tc-17-5241-2023, https://doi.org/10.5194/tc-17-5241-2023, 2023
Short summary
Short summary
In the face of global warming, understanding the changing water cycle and temperatures in polar regions is crucial. These factors directly impact the balance of ice sheets in the Arctic and Antarctic. By studying the composition of water vapor, we gain insights into climate variations. Our 2-year study at Dumont d’Urville station, Adélie Land, offers valuable data to refine models. Additionally, we demonstrate how modeling aids in interpreting signals from ice core samples in the region.
Alia L. Khan, Peng Xian, and Joshua P. Schwarz
The Cryosphere, 17, 2909–2918, https://doi.org/10.5194/tc-17-2909-2023, https://doi.org/10.5194/tc-17-2909-2023, 2023
Short summary
Short summary
Ice–albedo feedbacks in the ablation region of the Greenland Ice Sheet are difficult to constrain and model. Surface samples were collected across the 2014 summer melt season from different ice surface colors. On average, concentrations were higher in patches that were visibly dark, compared to medium patches and light patches, suggesting that black carbon aggregation contributed to snow aging, and vice versa. High concentrations are likely due to smoke transport from high-latitude wildfires.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
Short summary
Short summary
To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Guillaume Gastineau, Claude Frankignoul, Yongqi Gao, Yu-Chiao Liang, Young-Oh Kwon, Annalisa Cherchi, Rohit Ghosh, Elisa Manzini, Daniela Matei, Jennifer Mecking, Lingling Suo, Tian Tian, Shuting Yang, and Ying Zhang
The Cryosphere, 17, 2157–2184, https://doi.org/10.5194/tc-17-2157-2023, https://doi.org/10.5194/tc-17-2157-2023, 2023
Short summary
Short summary
Snow cover variability is important for many human activities. This study aims to understand the main drivers of snow cover in observations and models in order to better understand it and guide the improvement of climate models and forecasting systems. Analyses reveal a dominant role for sea surface temperature in the Pacific. Winter snow cover is also found to have important two-way interactions with the troposphere and stratosphere. No robust influence of the sea ice concentration is found.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Abigail G. Hughes, Sonja Wahl, Tyler R. Jones, Alexandra Zuhr, Maria Hörhold, James W. C. White, and Hans Christian Steen-Larsen
The Cryosphere, 15, 4949–4974, https://doi.org/10.5194/tc-15-4949-2021, https://doi.org/10.5194/tc-15-4949-2021, 2021
Short summary
Short summary
Water isotope records in Greenland and Antarctic ice cores are a valuable proxy for paleoclimate reconstruction and are traditionally thought to primarily reflect precipitation input. However,
post-depositional processes are hypothesized to contribute to the isotope climate signal. In this study we use laboratory experiments, field experiments, and modeling to show that sublimation and vapor–snow isotope exchange can rapidly influence the isotopic composition of the snowpack.
Holt Hancock, Jordy Hendrikx, Markus Eckerstorfer, and Siiri Wickström
The Cryosphere, 15, 3813–3837, https://doi.org/10.5194/tc-15-3813-2021, https://doi.org/10.5194/tc-15-3813-2021, 2021
Short summary
Short summary
We investigate how snow avalanche activity in central Spitsbergen, Svalbard, is broadly controlled by atmospheric circulation. Avalanche activity in this region is generally associated with atmospheric circulation conducive to increased precipitation, wind speeds, and air temperatures near Svalbard during winter storms. Our results help place avalanche activity on Spitsbergen in the wider context of Arctic environmental change and provide a foundation for improved avalanche forecasting here.
Thorsten Bartels-Rausch, Xiangrui Kong, Fabrizio Orlando, Luca Artiglia, Astrid Waldner, Thomas Huthwelker, and Markus Ammann
The Cryosphere, 15, 2001–2020, https://doi.org/10.5194/tc-15-2001-2021, https://doi.org/10.5194/tc-15-2001-2021, 2021
Short summary
Short summary
Chemical reactions in sea salt embedded in coastal polar snow impact the composition and air quality of the atmosphere. Here, we investigate the phase changes of sodium chloride. This is of importance as chemical reactions proceed faster in liquid solutions compared to in solid salt and the precise precipitation temperature of sodium chloride is still under debate. We focus on the upper nanometres of sodium chloride–ice samples because of their role as a reactive interface in the environment.
Dimitri Osmont, Sandra Brugger, Anina Gilgen, Helga Weber, Michael Sigl, Robin L. Modini, Christoph Schwörer, Willy Tinner, Stefan Wunderle, and Margit Schwikowski
The Cryosphere, 14, 3731–3745, https://doi.org/10.5194/tc-14-3731-2020, https://doi.org/10.5194/tc-14-3731-2020, 2020
Short summary
Short summary
In this interdisciplinary case study, we were able to link biomass burning emissions from the June 2017 wildfires in Portugal to their deposition in the snowpack at Jungfraujoch, Swiss Alps. We analysed black carbon and charcoal in the snowpack, calculated backward trajectories, and monitored the fire evolution by remote sensing. Such case studies help to understand the representativity of biomass burning records in ice cores and how biomass burning tracers are archived in the snowpack.
Nikolas O. Aksamit and John W. Pomeroy
The Cryosphere, 14, 2795–2807, https://doi.org/10.5194/tc-14-2795-2020, https://doi.org/10.5194/tc-14-2795-2020, 2020
Short summary
Short summary
In cold regions, it is increasingly important to quantify the amount of water stored as snow at the end of winter. Current models are inconsistent in their estimates of snow sublimation due to atmospheric turbulence. Specific wind structures have been identified that amplify potential rates of surface and blowing snow sublimation during blowing snow storms. The recurrence of these motions has been modeled by a simple scaling argument that has its foundation in turbulent boundary layer theory.
Andrew J. Schwartz, Hamish A. McGowan, Alison Theobald, and Nik Callow
The Cryosphere, 14, 2755–2774, https://doi.org/10.5194/tc-14-2755-2020, https://doi.org/10.5194/tc-14-2755-2020, 2020
Short summary
Short summary
This study measured energy available for snowmelt during the 2016 and 2017 snow seasons in Kosciuszko National Park, NSW, Australia, and identified common traits for days with similar weather characteristics. The analysis showed that energy available for snowmelt was highest in the days before cold fronts passed through the region due to higher air temperatures. Regardless of differences in daily weather characteristics, solar radiation contributed the highest amount of energy to snowpack melt.
Benjamin Walter, Hendrik Huwald, Josué Gehring, Yves Bühler, and Michael Lehning
The Cryosphere, 14, 1779–1794, https://doi.org/10.5194/tc-14-1779-2020, https://doi.org/10.5194/tc-14-1779-2020, 2020
Short summary
Short summary
We applied a horizontally mounted low-cost precipitation radar to measure velocities, frequency of occurrence, travel distances and turbulence characteristics of blowing snow off a mountain ridge. Our analysis provides a first insight into the potential of radar measurements for determining blowing snow characteristics, improves our understanding of mountain ridge blowing snow events and serves as a valuable data basis for validating coupled numerical weather and snowpack simulations.
Charles Amory and Christoph Kittel
The Cryosphere, 13, 3405–3412, https://doi.org/10.5194/tc-13-3405-2019, https://doi.org/10.5194/tc-13-3405-2019, 2019
Short summary
Short summary
Snow mass fluxes and vertical profiles of relative humidity are used to document concurrent occurrences of drifting snow and near-surface air saturation at a site dominated by katabatic winds in East Antarctica. Despite a high prevalence of drifting snow conditions, we demonstrate that saturation is reached only in the most extreme wind and transport conditions and discuss implications for the understanding of surface mass and atmospheric moisture budgets of the Antarctic ice sheet.
Varun Sharma, Louise Braud, and Michael Lehning
The Cryosphere, 13, 3239–3260, https://doi.org/10.5194/tc-13-3239-2019, https://doi.org/10.5194/tc-13-3239-2019, 2019
Short summary
Short summary
Snow surfaces, under the action of wind, form beautiful shapes such as waves and dunes. This study is the first ever study to simulate these shapes using a state-of-the-art numerical modelling tool. While these beautiful and ephemeral shapes on snow surfaces are fascinating from a purely aesthetic point of view, they are also critical in regulating the transfer of heat and mass between the atmosphere and snowpacks, thus being of huge importance to the Earth system.
Yvan Orsolini, Martin Wegmann, Emanuel Dutra, Boqi Liu, Gianpaolo Balsamo, Kun Yang, Patricia de Rosnay, Congwen Zhu, Wenli Wang, Retish Senan, and Gabriele Arduini
The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, https://doi.org/10.5194/tc-13-2221-2019, 2019
Short summary
Short summary
The Tibetan Plateau region exerts a considerable influence on regional climate, yet the snowpack over that region is poorly represented in both climate and forecast models due a large precipitation and snowfall bias. We evaluate the snowpack in state-of-the-art atmospheric reanalyses against in situ observations and satellite remote sensing products. Improved snow initialisation through better use of snow observations in reanalyses may improve medium-range to seasonal weather forecasts.
Dušan Materić, Elke Ludewig, Kangming Xu, Thomas Röckmann, and Rupert Holzinger
The Cryosphere, 13, 297–307, https://doi.org/10.5194/tc-13-297-2019, https://doi.org/10.5194/tc-13-297-2019, 2019
Niels Souverijns, Alexandra Gossart, Stef Lhermitte, Irina V. Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Duran-Alarcon, Brice Boudevillain, Christophe Genthon, Claudio Scarchilli, and Nicole P. M. van Lipzig
The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, https://doi.org/10.5194/tc-12-3775-2018, 2018
Short summary
Short summary
Snowfall observations over Antarctica are scarce and currently limited to information from the CloudSat satellite. Here, a first evaluation of the CloudSat snowfall record is performed using observations of ground-based precipitation radars. Results indicate an accurate representation of the snowfall climatology over Antarctica, despite the low overpass frequency of the satellite, outperforming state-of-the-art model estimates. Individual snowfall events are however not well represented.
Cited articles
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B.,
Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model:
Verification from Field Site to Terrestrial Water Storage and Impact in the
Integrated Forecast System, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009.
Bamzai, A. S. and Shukla, J.: Relation between Eurasian Snow Cover, Snow
Depth, and the Indian Summer Monsoon: An Observational Study, J. Climate,
12, 3117–3132, https://doi.org/10.1175/1520-0442(1999)012<3117:RBESCS>2.0.CO;2, 1999.
Barnett, T. P., Dümenil, L., Schlese, U., Roeckner, E., and Latif, M.:
The effect of Eurasian snow cover on regional and global climate variations,
J. Atmos. Sci., 46, 661–686,
https://doi.org/10.1175/1520-0469(1989)046<0661:TEOESC>2.0.CO;2, 1989.
Chen, L. and Frauenfeld, O. W.: A comprehensive evaluation of precipitation
simulations over China based on CMIP5 multimodel ensemble projections, J.
Geophys. Res.-Atmos., 119, 5767–5786, https://doi.org/10.1002/2013JD021190,
2014.
Chen, X. N., Long, D., Hong, Y., Liang, S. L., and Hou, A. Z.: Observed
radiative cooling over the Tibetan Plateau for the past three decades driven
by snow cover-induced surface albedo anomaly, J. Geophys. Res.-Atmos., 122,
6170–6185, https://doi.org/10.1002/2017jd026652, 2017.
Clark, M. P. and Serreze, M. C.: Effects of variations in east Asian snow
cover on modulating atmospheric circulation over the north pacific ocean, J.
Climate, 13, 3700–3710, https://doi.org/10.1175/1520-0442(2000)013<3700:eoviea>2.0.co;2, 2000.
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C.
S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B.,
Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.:
The Community Climate System Model version 3 (CCSM3), J. Climate, 19,
2122–2143, https://doi.org/10.1175/jcli3761.1, 2006.
de Andrade, F. M., Coelho, C. A. S., and Cavalcanti, I. F. A.: Global
precipitation hindcast quality assessment of the Subseasonal to Seasonal
(S2S) prediction project models, Clim. Dynam., 52, 5451–5475,
https://doi.org/10.1007/s00382-018-4457-z, 2019.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Koehler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Deutscher Wetterdienst: Global Precipitation Analysis Products of the Global Precipitation Climatology Centre, available at: https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, last access: October 2020.
Dirmeyer, P. A., Gentine, P., Ek, M. B., and Balsamo, G.: Chapter 8 – Land
Surface Processes Relevant to Sub-seasonal to Seasonal (S2S) Prediction, in:
Sub-Seasonal to Seasonal Prediction, edited by: Robertson, A. W. and
Vitart, F., Elsevier, 165–181, 2019.
Diro, G. T. and Lin, H.: Subseasonal Forecast Skill of Snow Water
Equivalent and Its Link with Temperature in Selected SubX Models, Weather
Forecast., 35, 273–284, https://doi.org/10.1175/WAF-D-19-0074.1, 2020.
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M. A., Beljaars, A.,
Schär, C., and Elder, K.: An Improved Snow Scheme for the ECMWF Land
Surface Model: Description and Offline Validation, J. Hydrometeorol., 11,
899–916, https://doi.org/10.1175/2010JHM1249.1, 2010.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003.
European Centre for Medium-Range Weather Forecasts: S2S datasets, available at: https://apps.ecmwf.int/datasets/, last access: October 2020a.
European Centre for Medium-Range Weather Forecasts: ERA-Interim data, available at: https://apps.ecmwf.int/datasets/, last access: October 2020b.
Fayad, A., Gascoin, S., Faour, G., López-Moreno, J. I., Drapeau, L.,
Page, M. L., and Escadafal, R.: Snow hydrology in Mediterranean mountain
regions: A review, J. Hydrol., 551, 374–396,
https://doi.org/10.1016/j.jhydrol.2017.05.063, 2017.
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and Kasheta, T.:
Enhancements to, and forthcoming developments in the Interactive Multisensor
Snow and Ice Mapping System (IMS), Hydrol. Process., 21, 1576–1586,
https://doi.org/10.1002/hyp.6720, 2007.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow–atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change, 8,
954–963, https://doi.org/10.1038/s41558-018-0295-6, 2018.
Hong, S.-Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics
Scheme (WSM6), Asia-Pac., J. Atmos. Sci., 42, 129–151, 2006.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/mwr3199.1, 2006.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman,
K. P., Hong, Y., and Stocker, E. F., and Wolff, D. B.: The TRMM multisatellite
precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/jhm560.1, 2007.
Immerzeel, W. W., Droogers, P., de Jong, S. M., and Bierkens, M. F. P.:
Large-scale monitoring of snow cover and runoff simulation in Himalayan
river basins using remote sensing, Remote Sens. Environ., 113, 40–49,
https://doi.org/10.1016/j.rse.2008.08.010, 2009.
Jeelani, G., Feddema, J. J., van der Veen, C. J., and Stearns, L.: Role of
snow and glacier melt in controlling river hydrology in Liddar watershed
(western Himalaya) under current and future climate, Water Resour. Res., 48,
W12508, https://doi.org/10.1029/2011WR011590, 2012.
Jeong, J. H., Linderholm, H. W., Woo, S. H., Folland, C., Kim, B. M., Kim,
S. J., and Chen, D. L.: Impacts of Snow Initialization on Subseasonal
Forecasts of Surface Air Temperature for the Cold Season, J. Climate, 26,
1956–1972, https://doi.org/10.1175/jcli-d-12-00159.1, 2013.
Kain, J. S.: The Kain-Fritsch convective parameterization: An update, J.
Appl. Meteorol., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:tkcpau>2.0.co;2, 2004.
Kolstad, E. W.: Causal Pathways for Temperature Predictability from Snow
Depth, J. Climate, 30, 9651–9663, https://doi.org/10.1175/JCLI-D-17-0280.1,
2017.
Koren, V., Schaake, J., Mitchell, K., Duan, Q. Y., Chen, F., and Baker, J.
M.: A parameterization of snowpack and frozen ground intended for NCEP
weather and climate models, J. Geophys. Res.-Atmos., 104, 19569–19585,
https://doi.org/10.1029/1999JD900232, 1999.
Li, F., Orsolini, Y. J., Keenlyside, N., Shen, M. L., Counillon, F., and
Wang, Y. G.: Impact of Snow Initialization in Subseasonal-to-Seasonal Winter
Forecasts With the Norwegian Climate Prediction Model, J. Geophys. Res.-Atmos., 124,
10033–10048, https://doi.org/10.1029/2019JD030903, 2019.
Li, W., Guo, W., Hsu, P.-C., and Xue, Y.: Influence of the Madden–Julian
oscillation on Tibetan Plateau snow cover at the intraseasonal time-scale,
Sci. Rep., 6, 30456, https://doi.org/10.1038/srep30456, 2016.
Li, W., Guo, W., Qiu, B., Xue, Y., Hsu, P.-C., and Wei, J.: Influence of
Tibetan Plateau snow cover on East Asian atmospheric circulation at
medium-range time scales, Nat. Commun., 9, 4243,
https://doi.org/10.1038/s41467-018-06762-5, 2018.
Li, W., Chen, J., Li, L., Chen, H., Liu, B., Xu, C.-Y., and Li, X.:
Evaluation and Bias Correction of S2S Precipitation for Hydrological
Extremes, J. Hydrometeorol., 20, 1887–1906,
https://doi.org/10.1175/JHM-D-19-0042.1, 2019.
Li, W., Qiu, B., Guo, W., Zhu, Z., and Hsu, P.-C.: Intraseasonal variability
of Tibetan Plateau snow cover, Int. J. Climatol., 40, 3451–3466,
https://doi.org/10.1002/joc.6407, 2020a.
Li, W., Qiu, B., Guo, W., and Hsu, P.-C.: Rapid response of the East Asian
trough to Tibetan Plateau snow cover, Int. J. Climatol.,
https://doi.org/10.1002/joc.6618, in press, 2020b.
Lin, H. and Wu, Z. W.: Contribution of the Autumn Tibetan Plateau Snow
Cover to Seasonal Prediction of North American Winter Temperature, J.
Climate, 24, 2801–2813, https://doi.org/10.1175/2010jcli3889.1, 2011.
Lin, P., Wei, J., Yang, Z. L., Zhang, Y., and Zhang, K.: Snow data
assimilation-constrained land initialization improves seasonal temperature
prediction, Geophys. Res. Lett., 43, 11423–411432,
https://doi.org/10.1002/2016GL070966, 2016.
Mariotti, A., Ruti, P. M., and Rixen, M.: Progress in subseasonal to
seasonal prediction through a joint weather and climate community effort,
npj Clim. Atmos. Sci., 1, 4, https://doi.org/10.1038/s41612-018-0014-z,
2018.
NASA Goddard Earth Sciences Data and Information Services Center: TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25 degree x 0.25 degree V7, available at: https://disc.gsfc.nasa.gov, last access: October 2020.
National Center for Atmospheric Research: NCEP FNL data, available at: https://rda.ucar.edu/datasets/ds083.2/, last access: October 2020.
National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief Model, https://doi.org/10.7289/V5C8276M, last access: October 2020.
National Snow and Ice Data Center: IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1, available at: https://nsidc.org/data/G02156, last access: October 2020.
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019.
Orsolini, Y. J., Senan, R., Balsamo, G., Doblas-Reyes, F. J., Vitart, F.,
Weisheimer, A., Carrasco, A., and Benestad, R. E.: Impact of snow
initialization on sub-seasonal forecasts, Clim. Dynam., 41, 1969–1982,
https://doi.org/10.1007/s00382-013-1782-0, 2013.
Robertson, A. W., Kumar, A., Peña, M., and Vitart, F.: Improving and
Promoting Subseasonal to Seasonal Prediction, B. Am. Meteorol. Soc., 96,
ES49–ES53, https://doi.org/10.1175/BAMS-D-14-00139.1, 2014.
Schmitt Quedi, E. and Mainardi Fan, F.: Sub seasonal streamflow forecast
assessment at large-scale basins, J. Hydrol., 584, 124635,
https://doi.org/10.1016/j.jhydrol.2020.124635, 2020.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B.,
and Ziese, M.: GPCC full data reanalysis version 6.0 at 0.5: monthly
land-surface precipitation from rain-gauges built on GTS-based and historic
data, GPCC Data Rep., https://doi.org/10.5676/DWD_GPCC/FD_M_V7_100, 2011.
Senan, R., Orsolini, Y. J., Weisheimer, A., Vitart, F., Balsamo, G.,
Stockdale, T. N., Dutra, E., Doblas-Reyes, F. J., and Basang, D.: Impact of
springtime Himalayan-Tibetan Plateau snowpack on the onset of the Indian
summer monsoon in coupled seasonal forecasts, Clim. Dynam., 47, 2709–2725,
https://doi.org/10.1007/s00382-016-2993-y, 2016.
Song, L. and Wu, R.: Intraseasonal Snow Cover Variations Over Western
Siberia and Associated Atmospheric Processes, J. Geophys. Res.-Atmos., 124,
8994–9010, https://doi.org/10.1029/2019JD030479, 2019.
Song, L., Wu, R. G., and An, L.: Different Sources of 10-to 30-day
Intraseasonal Variations of Autumn Snow over Western and Eastern Tibetan
Plateau, Geophys. Res. Lett., 46, 9118–9125,
https://doi.org/10.1029/2019gl083852, 2019.
Su, F., Duan, X., Chen, D., Hao, Z., and Cuo, L.: Evaluation of the Global
Climate Models in the CMIP5 over the Tibetan Plateau, J. Climate, 26,
3187–3208, https://doi.org/10.1175/JCLI-D-12-00321.1, 2013.
Suriano, Z. J. and Leathers, D. J.: Great Lakes Basin Snow-Cover Ablation
and Synoptic-Scale Circulation, J. Appl. Meteorol. Clim., 57,
1497–1510, https://doi.org/10.1175/jamc-d-17-0297.1, 2018.
UCAR/NCAR/CISL/TDD: The NCAR Command Language (Version 6.6.2), https://doi.org/10.5065/D6WD3XH5, last access: October 2020.
University Corporation for Atmospheric Research: WRF Users Page, available at: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html, last access: October 2020.
Vitart, F.: Madden–Julian Oscillation prediction and teleconnections in the
S2S database, Q. J. Roy. Meteor. Soc., 143, 2210–2220,
https://doi.org/10.1002/qj.3079, 2017.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H.,
Hodgson, J., Kang, H. S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi,
P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean,
P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M.,
Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F.,
Waliser, D., Woolnough, S., Wu, T., Won, D. J., Xiao, H., Zaripov, R., and
Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database,
B. Am. Meteorol. Soc., 98, 163–173,
https://doi.org/10.1175/BAMS-D-16-0017.1, 2016.
Wang, C., Yang, K., Li, Y., Wu, D., and Bo, Y.: Impacts of Spatiotemporal
Anomalies of Tibetan Plateau Snow Cover on Summer Precipitation in Eastern
China, J. Climate, 30, 885–903, https://doi.org/10.1175/JCLI-D-16-0041.1,
2017.
Wang, T., Peng, S., Ottle, C., and Ciais, P.: Spring snow cover deficit
controlled by intraseasonal variability of the surface energy fluxes,
Environ. Res. Lett., 10, 024018,
https://doi.org/10.1088/1748-9326/10/2/024018, 2015.
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J. T., Lazo, J. K.,
Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V.,
Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B.,
Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J.,
McEvoy, D., Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R.,
Jones, L., Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R.,
Meinke, H., Arheimer, B., and Zebiak, S. E.: Potential applications of
subseasonal-to-seasonal (S2S) predictions, Meteorol. Appl., 24, 315–325,
https://doi.org/10.1002/met.1654, 2017.
Wu, R. G. and Kirtman, B. P.: Observed relationship of spring and summer
East Asian rainfall with winter and spring Eurasian snow, J. Climate, 20,
1285–1304, https://doi.org/10.1175/jcli4068.1, 2007.
Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., Zhang, Y., Zhang,
L., Li, J., Wu, F., Liu, Y., Zhang, F., Shi, X., Chu, M., Zhang, J., Fang,
Y., Wang, F., Lu, Y., Liu, X., Wei, M., Liu, Q., Zhou, W., Dong, M., Zhao,
Q., Ji, J., Li, L., and Zhou, M.: An overview of BCC climate system model
development and application for climate change studies, J. Meteorolog. Res.,
28, 34–56, https://doi.org/10.1007/s13351-014-3041-7, 2014.
Wu, T. and Wu, G.: An empirical formula to compute snow cover fraction in
GCMs, Adv. Atmos. Sci., 21, 529–535, https://doi.org/10.1007/BF02915720,
2004.
Wu, T. W. and Qian, Z. A.: The relation between the Tibetan winter snow and
the Asian summer monsoon and rainfall: An observational investigation, J.
Climate, 16, 2038–2051, https://doi.org/10.1175/1520-0442(2003)016<2038:trbttw>2.0.co;2, 2003.
Wulff, C. O. and Domeisen, D. I. V.: Higher Subseasonal Predictability of
Extreme Hot European Summer Temperatures as Compared to Average Summers,
Geophys. Res. Lett., 46, 11520–11529, https://doi.org/10.1029/2019GL084314,
2019.
Xiao, Z. X. and Duan, A. M.: Impacts of Tibetan Plateau Snow Cover on the
Interannual Variability of the East Asian Summer Monsoon, J. Climate, 29,
8495–8514, https://doi.org/10.1175/jcli-d-16-0029.1, 2016.
Yang, J., Jiang, L., Ménard, C. B., Luojus, K., Lemmetyinen, J., and
Pulliainen, J.: Evaluation of snow products over the Tibetan Plateau,
Hydrol. Process., 29, 3247–3260, https://doi.org/10.1002/hyp.10427, 2015.
Yang, J., Zhu, T., Gao, M., Lin, H., Wang, B., and Bao, Q.: Late-July
Barrier for Subseasonal Forecast of Summer Daily Maximum Temperature Over
Yangtze River Basin, Geophys. Res. Lett., 45, 12610–612615,
https://doi.org/10.1029/2018GL080963, 2018.
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang,
S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan
Plateau and its influence on the broad climate system, Earth Sci. Rev., 201,
103043, https://doi.org/10.1016/j.earscirev.2019.103043, 2020.
Zhang, F., Ren, H., Miao, L., Lei, Y., and Duan, M.: Simulation of Daily
Precipitation from CMIP5 in the Qinghai-Tibet Plateau, SOLA, 15, 68–74,
https://doi.org/10.2151/sola.2019-014, 2019.
Zhang, G., Xie, H., Yao, T., Liang, T., and Kang, S.: Snow cover dynamics of
four lake basins over Tibetan Plateau using time series MODIS data
(2001–2010), Water Resour. Res., 48, W10529,
https://doi.org/10.1029/2012wr011971, 2012.
Zhang, L. L., Su, F. G., Yang, D. Q., Hao, Z. C., and Tong, K.: Discharge
regime and simulation for the upstream of major rivers over Tibetan Plateau,
J. Geophys. Res.-Atmos., 118, 8500–8518,
https://doi.org/10.1002/jgrd.50665, 2013.
Zhang, T. J.: Influence of the seasonal snow cover on the ground thermal
regime: An overview, Rev. Geophys., 43, RG4002,
https://doi.org/10.1029/2004rg000157, 2005.
Zhang, Y. and Li, J.: Impact of moisture divergence on systematic errors in
precipitation around the Tibetan Plateau in a general circulation model,
Clim. Dynam., 47, 2923–2934, https://doi.org/10.1007/s00382-016-3005-y, 2016.
Zhang, Y., Zou, T., and Xue, Y.: An Arctic-Tibetan Connection on Subseasonal
to Seasonal Time Scale, Geophys. Res. Lett., 46, 2790–2799,
https://doi.org/10.1029/2018GL081476, 2019.
Short summary
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan...
