Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2475-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-2475-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 Jun 2018
Development and technical paper |
| 21 Jun 2018
| 21 Jun 2018
PIC v1.3: comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau
State Key Laboratory of Frozen Soils Engineering, Cold and Arid
Regions Environmental and Engineering Research Institute, Chinese Academy of
Sciences, Lanzhou, Gansu Province 730000, China
Zhongqiong Zhang
State Key Laboratory of Frozen Soils Engineering, Cold and Arid
Regions Environmental and Engineering Research Institute, Chinese Academy of
Sciences, Lanzhou, Gansu Province 730000, China
Wei Ma
State Key Laboratory of Frozen Soils Engineering, Cold and Arid
Regions Environmental and Engineering Research Institute, Chinese Academy of
Sciences, Lanzhou, Gansu Province 730000, China
Shuhua Yi
State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions
Environmental and Engineering Research Institute, Chinese Academy of
Sciences, Lanzhou, Gansu Province 730000, China
Yanli Zhuang
Linze Inland River Basin Research Station, Key Laboratory of Inland
River Basin Ecohydrology, Cold and Arid Regions Environmental and
Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu
Province 730000, China
Related authors
Jizu Chen, Wentao Du, Shichang Kang, Xiang Qin, Weijun Sun, Yang Li, Yushuo Liu, Lihui Luo, and Youyan Jiang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-179, https://doi.org/10.5194/tc-2022-179, 2022
Preprint withdrawn
Short summary
Short summary
This study developed a dynamic deposition model of light absorbing particles (LAPs), which coupled with a surface energy and mass balance model. Based on the coupled model, we assessed atmospheric deposited BC effect on glacier melting, and quantified global warming and increment of emitted black carbon respective contributions to current accelerated glacier melting.
Lihui Luo, Yanli Zhuang, Mingyi Zhang, Zhongqiong Zhang, Wei Ma, Wenzhi Zhao, Lin Zhao, Li Wang, Yanmei Shi, Ze Zhang, Quntao Duan, Deyu Tian, and Qingguo Zhou
Earth Syst. Sci. Data, 13, 4035–4052, https://doi.org/10.5194/essd-13-4035-2021, https://doi.org/10.5194/essd-13-4035-2021, 2021
Short summary
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We implement a variety of sensors to monitor the hydrological and thermal deformation between permafrost slopes and engineering projects in the hinterland of the Qinghai–Tibet Plateau. We present the integrated observation dataset from the 1950s to 2020, explaining the instrumentation, processing, data visualisation, and quality control.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Jizu Chen, Wentao Du, Shichang Kang, Xiang Qin, Weijun Sun, Yang Li, Yushuo Liu, Lihui Luo, and Youyan Jiang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-179, https://doi.org/10.5194/tc-2022-179, 2022
Preprint withdrawn
Short summary
Short summary
This study developed a dynamic deposition model of light absorbing particles (LAPs), which coupled with a surface energy and mass balance model. Based on the coupled model, we assessed atmospheric deposited BC effect on glacier melting, and quantified global warming and increment of emitted black carbon respective contributions to current accelerated glacier melting.
Lihui Luo, Yanli Zhuang, Mingyi Zhang, Zhongqiong Zhang, Wei Ma, Wenzhi Zhao, Lin Zhao, Li Wang, Yanmei Shi, Ze Zhang, Quntao Duan, Deyu Tian, and Qingguo Zhou
Earth Syst. Sci. Data, 13, 4035–4052, https://doi.org/10.5194/essd-13-4035-2021, https://doi.org/10.5194/essd-13-4035-2021, 2021
Short summary
Short summary
We implement a variety of sensors to monitor the hydrological and thermal deformation between permafrost slopes and engineering projects in the hinterland of the Qinghai–Tibet Plateau. We present the integrated observation dataset from the 1950s to 2020, explaining the instrumentation, processing, data visualisation, and quality control.
Yu Qin, Shuhua Yi, Yongjian Ding, Wei Zhang, Yan Qin, Jianjun Chen, and Zhiwei Wang
Biogeosciences, 16, 1097–1109, https://doi.org/10.5194/bg-16-1097-2019, https://doi.org/10.5194/bg-16-1097-2019, 2019
Shuhua Yi, Yujie He, Xinlei Guo, Jianjun Chen, Qingbai Wu, Yu Qin, and Yongjian Ding
The Cryosphere, 12, 3067–3083, https://doi.org/10.5194/tc-12-3067-2018, https://doi.org/10.5194/tc-12-3067-2018, 2018
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Coarse-fragment soil on the Qinghai–Tibetan Plateau has different thermal and hydrological properties to soils commonly used in modeling studies. We took soil samples and measured their physical properties in a laboratory, which were used in a model to simulate their effects on permafrost dynamics. Model errors were reduced using the measured properties, in which porosity played an dominant role.
Shuhua Yi, Jianjun Chen, Yu Qin, and Gaowei Xu
Biogeosciences, 13, 6273–6284, https://doi.org/10.5194/bg-13-6273-2016, https://doi.org/10.5194/bg-13-6273-2016, 2016
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Plateau pika is common on the Qinghai-Tibet Plateau (QTP). Since pika dig burrows and graze on grassland to compete with yaks and sheep, they are believed to be a pest. They have been killed by humans since the 1950s. However, there are no serious studies that quantitatively evaluate the grazing and excavating effects of pika on grassland. With the advancement of UAV technology, we did a pilot study to evaluate the grazing and burying effects of pika.
Qingbai Wu, Zhongqiong Zhang, Siru Gao, and Wei Ma
The Cryosphere, 10, 1695–1706, https://doi.org/10.5194/tc-10-1695-2016, https://doi.org/10.5194/tc-10-1695-2016, 2016
S. Yi, K. Wischnewski, M. Langer, S. Muster, and J. Boike
Geosci. Model Dev., 7, 1671–1689, https://doi.org/10.5194/gmd-7-1671-2014, https://doi.org/10.5194/gmd-7-1671-2014, 2014
S. Yi, J. Chen, Q. Wu, and Y. Ding
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-4703-2013, https://doi.org/10.5194/tcd-7-4703-2013, 2013
Revised manuscript not accepted
Related subject area
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Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Graphics processing unit accelerated ice flow solver for unstructured meshes using the Shallow Shelf Approximation (FastIceFlo v1.0)
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Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
EGUsphere, https://doi.org/10.5194/egusphere-2023-2010, https://doi.org/10.5194/egusphere-2023-2010, 2023
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In this, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature and melt, while preserving a reasonable computation time.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better quality maps. The correction can then be extended backwards and forwards in time for periods when better quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-32, https://doi.org/10.5194/gmd-2023-32, 2023
Revised manuscript accepted for GMD
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPU) and compare it with a standard central processing units (CPU) implementation. We justify the GPU implementation by applying the price and power consumption to performance metrics for up to a million grid point spatial resolutions. The study is a first step toward leveraging GPU processing power for accurate polar ice discharge predictions.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-43, https://doi.org/10.5194/gmd-2023-43, 2023
Revised manuscript accepted for GMD
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires to represent small-scale processes such as wind-induced snow transport. Here we present SnowPappus, a simple scheme coupled with Crocus snow model computing blowing snow fluxes and redistributing snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
EGUsphere, https://doi.org/10.5194/egusphere-2023-635, https://doi.org/10.5194/egusphere-2023-635, 2023
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
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Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
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We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
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We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
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In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
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We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
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Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
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Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
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We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
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In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
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Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Jonas Van Breedam, Philippe Huybrechts, and Michel Crucifix
Geosci. Model Dev., 14, 6373–6401, https://doi.org/10.5194/gmd-14-6373-2021, https://doi.org/10.5194/gmd-14-6373-2021, 2021
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Ice sheets are an important component of the climate system and interact with the atmosphere through albedo variations and changes in the surface height. On very long timescales, it is impossible to directly couple ice sheet models with climate models and other techniques have to be used. Here we present a novel coupling method between ice sheets and the atmosphere by making use of an emulator to simulate ice sheet–climate interactions for several million years.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
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This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison
Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, https://doi.org/10.5194/gmd-14-5843-2021, 2021
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Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
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High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
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The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Daniel R. Shapero, Jessica A. Badgeley, Andrew O. Hoffman, and Ian R. Joughin
Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, https://doi.org/10.5194/gmd-14-4593-2021, 2021
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This paper describes a new software package called "icepack" for modeling the flow of ice sheets and glaciers. Glaciologists use tools like icepack to better understand how ice sheets flow, what role they have played in shaping Earth's climate, and how much sea level rise we can expect in the coming decades to centuries. The icepack package includes several innovations to help researchers describe and solve interesting glaciological problems and to experiment with the underlying model physics.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
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Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev., 14, 3697–3714, https://doi.org/10.5194/gmd-14-3697-2021, https://doi.org/10.5194/gmd-14-3697-2021, 2021
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We present the technical implementation of a coarse-resolution coupling between an ice sheet model and an ocean model that allows one to simulate ice–ocean interactions at timescales from centuries to millennia. As ice shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. It is shown that the framework is computationally efficient, conserves mass and energy, and can produce a stable coupled state under present-day forcing.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, https://doi.org/10.5194/gmd-14-3487-2021, 2021
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This paper presents recent developments in the drifting-snow scheme of the regional climate model MAR and its application to simulate drifting snow and the surface mass balance of Adélie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and surface mass balance estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Thiago Dias dos Santos, Mathieu Morlighem, and Hélène Seroussi
Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, https://doi.org/10.5194/gmd-14-2545-2021, 2021
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Numerical models are routinely used to understand the past and future behavior of ice sheets in response to climate evolution. As is always the case with numerical modeling, one needs to minimize biases and numerical artifacts due to the choice of numerical scheme employed in such models. Here, we assess different numerical schemes in time-dependent simulations of ice sheets. We also introduce a new parameterization for the driving stress, the force that drives the ice sheet flow.
Constantijn J. Berends, Heiko Goelzer, and Roderik S. W. van de Wal
Geosci. Model Dev., 14, 2443–2470, https://doi.org/10.5194/gmd-14-2443-2021, https://doi.org/10.5194/gmd-14-2443-2021, 2021
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The largest uncertainty in projections of sea-level rise comes from ice-sheet retreat. To better understand how these ice sheets respond to the changing climate, ice-sheet models are used, which must be able to reproduce both their present and past evolution. We have created a model that is fast enough to simulate an ice sheet at a high resolution over the course of an entire 120 000-year glacial cycle. This allows us to study processes that cannot be captured by lower-resolution models.
Cited articles
Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc,
M., Bontemps, S., Leroy, M., Achard, F., and Herold, M.: GLOBCOVER: products
description and validation report, Foro Mundial De La Salud, 17, 285–287,
2008.
Chen, B. X., Zhang, X. Z., Tao, J., Wu, J. S., Wang, J. S., Shi, P. L.,
Zhang, Y. J., and Yu, C. Q.: The impact of climate change and anthropogenic
activities on alpine grassland over the Qinghai-Tibet Plateau, Agr. Forest
Meteorol., 189, 11–18, 2014.
Chen, H., Zhu, Q. A., Peng, C. H., Wu, N., Wang, Y. F., Fang, X. Q., Gao, Y.
H., Zhu, D., Yang, G., Tian, J. Q., Kang, X. M., Piao, S. L., Ouyang, H.,
Xiang, W. H., Luo, Z. B., Jiang, H., Song, X. Z., Zhang, Y., Yu, G. R., Zhao,
X. Q., Gong, P., Yao, T. D., and Wu, J. H.: The impacts of climate change and
human activities on biogeochemical cycles on the Qinghai-Tibetan Plateau,
Glob. Change Biol., 19, 2940–2955, 2013.
Chen, S., Liu, W., Qin, X. Y., Liu, Y., Zhang, T., Chen, K., Hu, F., Ren,
J., and Qin, D.: Response characteristics of vegetation and soil environment
to permafrost degradation in the upstream regions of the Shule River Basin,
Environ. Res. Lett., 7, 045406, https://doi.org/10.1016/j.agrformet.2014.01.002,
2012.
Cheng, G. and Wu, T.: Responses of permafrost to climate change and their
environmental significance, Qinghai-Tibet Plateau, J. Geophys. Res.-Earth,
112, https://doi.org/10.1111/gcb.12277, 2007a.
Sneyers, R.: On the statistical analysis of series of observations,
World Meteorological Organization, Geneva, Switzerland, Technical note No. 143, p. 192, 1990.
Gisnås, K., Etzelmüller, B., Farbrot, H., Schuler, T. V., and
Westermann, S.: CryoGRID 1.0: Permafrost Distribution in Norway estimated by
a Spatial Numerical Model, Permafrost Periglac., 24, 2–19, 2013.
Harris, C., Arenson, L. U., Christiansen, H. H., Etzemuller, B.,
Frauenfelder, R., Gruber, S., Haeberli, W., Hauck, C., Holzle, M., Humlum,
O., Isaksen, K., Kaab, A., Kern-Lutschg, M. A., Lehning, M., Matsuoka, N.,
Murton, J. B., Nozli, J., Phillips, M., Ross, N., Seppala, M., Springman, S.
M., and Muhll, D. V.: Permafrost and climate in Europe: Monitoring and
modelling thermal, geomorphological and geotechnical responses, Earth-Sci.
Rev., 92, 117–171, 2009.
Hilbich, C., Hauck, C., Hoelzle, M., Scherler, M., Schudel, L., Voelksch,
I., Muehll, D. V., and Maeusbacher, R.: Monitoring mountain permafrost
evolution using electrical resistivity tomography: A 7-year study of
seasonal, annual, and long-term variations at Schilthorn, Swiss Alps, J.
Geophys. Res.-Earth, 113, F01S90
https://doi.org/10.1029/2007JF000799, 2008.
Jafarov, E. E., Marchenko, S. S., and Romanovsky, V. E.: Numerical modeling
of permafrost dynamics in Alaska using a high spatial resolution dataset, The
Cryosphere, 6, 613–624, https://doi.org/10.5194/tc-6-613-2012, 2012.
Jin, H., Yu, Q., Wang, S., and Lü, L.: Changes in permafrost
environments along the Qinghai–Tibet engineering corridor induced by
anthropogenic activities and climate warming, Cold Reg. Sci. Technol., 53,
317–333, https://doi.org/10.1016/j.coldregions.2007.07.005, 2008.
Johansen, O.: Thermal Conductivity of Soils, 1977, University of Trondheim,
Norway, 1977.
Juliussen, H. and Humlum, O.: Towards a TTOP ground temperature model for
mountainous terrain in central-eastern Norway, Permafrost Periglac., 18,
161–184, 2007.
Kahle, D. and Wickham, H.: ggmap: Spatial Visualization with ggplot2, R. J.,
5, 144–161, 2013.
Kudryavtsev, V. A., Garagulya, L. S., Yeva, K. A. K., and Melamed, V. G.:
Fundamentals of Frost Forecasting in Geological Engineering Investigations, Hanover, New Hampshire, Cold Regions Research and Engineering Lab, 489, 1977.
Larsen, P. H., Goldsmith, S., Smith, O., Wilson, M. L., Strzepek, K.,
Chinowsky, P., and Saylor, B.: Estimating future costs for Alaska public
infrastructure at risk from climate change, Global Environ. Chang., 18,
442–457, 2008.
Legates, D. R. and McCabe, G. J.: Evaluating the use of
“goodness-of-fit” measures in hydrologic and hydroclimatic model
validation, Water Resour. Res., 35, 233–241, 1999.
Lewkowicz, A. G. and Bonnaventure, P. R.: Interchangeability of mountain
permafrost probability models, northwest Canada, Permafrost Periglac., 19,
49–62, 2008.
Li, L., Yang, S., Wang, Z. Y., Zhu, X. D., and Tang, H. Y.: Evidence of
Warming and Wetting Climate over the Qinghai-Tibet Plateau, Arct. Antarct.
Alp. Res., 42, 449–457, 2010.
Li, R., Zhao, L., Ding, Y., Wu, T., Xiao, Y., Du, E., Liu, G., and Qiao, Y.:
Temporal and spatial variations of the active layer along the Qinghai-Tibet
Highway in a permafrost region, Chinese Sci. Bull., 57, 4609–4616, https://doi.org/10.1657/1938-4246-42.4.449,
2012.
Lu, J., Niu, F., Cheng, H., Lin, Z., Liu, H., and Luo, J.: The Permafrost
Distribution Model and Its Change Trend of Qinghai-Tibet Engineering
Corridor, J. Mt. Sci.-Engl., 31, 226–233, 2013.
Luo, L.: PIC: Comprehensive R package for permafrost indices
computing, Zenodo, https://doi.org/10.5281/zenodo.1254848, 2018.
Luo, L., Zhang, Y., and Zhu, W.: E-Science application of wireless sensor
networks in eco-hydrological monitoring in the Heihe River basin, China, Iet
Sci. Meas. Technol., 6, 1–8,
https://doi.org/10.1049/iet-smt.2011.0211,
2012.
Luo, L., Ma, W., Zhang, Z., Zhuang, Y., Zhang, Y., Yang, J., Cao, X., Liang,
S., and Mu, Y.: Freeze/Thaw-Induced Deformation Monitoring and Assessment of
the Slope in Permafrost Based on Terrestrial Laser Scanner and GNSS, Remote
Sens., 9, 198, https://doi.org/10.3390/rs9030198,
2017.
Luo, L., Ma, W., Zhuang, Y., Zhang, Y., Yi, S., Xu, J., Long, Y., Ma, D.,
and Zhang, Z.: The impacts of climate change and human activities on alpine
vegetation and permafrost in the Qinghai-Tibet Engineering Corridor, Ecol.
Indic., 93, 24–35, 2018.
Luo, S., Lü, S., and Zhang, Y.: Development and validation of the frozen
soil parameterization scheme in Common Land Model, Cold Reg. Sci. Technol.,
55, 130–140, 2009.
McNutt, M.: Journals unite for reproducibility, Science, 346, 679–679, 2014.
Michna, P. and Woods, M.: RNetCDF – A Package for Reading and Writing NetCDF
Datasets, R. J., 5, 29–36, 2013.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), FAO, Rome, Italy and IIASA, Laxenburg, Austria, available at: http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/,
last access: 18 June 2018.
Nan, Z.: Prediction of permafrost distribution on the Qinghai-Tibet Plateau
in the next 50 and 100 years, Sci. China Ser. D, 48, 797–804 https://doi.org/10.1360/03yd0258,
2005.
Nelson, F. E., Shiklomanov, N. I., Mueller, G. R., Hinkel, K. M., Walker, D.
A., and Bockheim, J. G.: Estimating active-layer thickness over a large
region: Kuparuk River Basin, Alaska, USA, Arct. Antarct. Alp. Res., 29,
367–378, 1997.
Niu, F., Luo, J., Lin, Z., Fang, J., and Liu, M.: Thaw-induced slope
failures and stability analyses in permafrost regions of the Qinghai-Tibet
Plateau, China, Landslides, 13, 55–65, 2016.
Oelke, C. and Zhang, T.: Modeling the Active-Layer Depth over the Tibetan
Plateau, Arct. Antarct. Alp. Res., 39, 714–722, 2007.
Pang, Q., Cheng, G., Li, S., and Zhang, W.: Active layer thickness
calculation over the Qinghai–Tibet Plateau, Cold. Reg. Sci. Technol., 57,
23–28, 2009.
Ran, Y., Li, X., and Cheng, G.: Climate warming over the past half century
has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau,
The Cryosphere, 12, 595–608, https://doi.org/10.5194/tc-12-595-2018, 2018.
Ran, Y. H., Li, X., Cheng, G. D., Zhang, T. J., Wu, Q. B., Jin, H. J., and
Jin, R.: Distribution of Permafrost in China: An Overview of Existing
Permafrost Maps, Permafrost Periglac., 23, 322–333, 2012.
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria, 2017.
Riseborough, D.: Permafrost Modeling, in: Encyclopedia of Snow, Ice and
Glaciers, edited by: Singh, V. P., Singh, P., and Haritashya, U. K., Springer
Netherlands, Dordrecht, 2011.
Riseborough, D., Shiklomanov, N., Etzelmuller, B., Gruber, S., and
Marchenko, S.: Recent advances in permafrost modelling, Permafrost Periglac.,
19, 137–156, 2008.
Romanovsky, V. E. and Osterkamp, T. E.: Thawing of the active layer on the
coastal plain of the Alaskan Arctic, Permafrost Periglac., 8, 1–22, 1997.
Sazonova, T. S. and Romanovsky, V. E.: A model for regional-scale estimation
of temporal and spatial variability of active layer thickness and mean annual
ground temperatures, Permafrost Periglac., 14, 125–139, 2003.
Shanley, J. B. and Chalmers, A.: The effect of frozen soil on snowmelt
runoff at Sleepers River, Vermont, Hydrol. Process., 13, 1843–1857, 1999.
Shen, M. G., Piao, S. L., Jeong, S. J., Zhou, L. M., Zeng, Z. Z., Ciais, P.,
Chen, D. L., Huang, M. T., Jin, C. S., Li, L. Z. X., Li, Y., Myneni, R. B.,
Yang, K., Zhang, G. X., Zhang, Y. J., and Yao, T. D.: Evaporative cooling
over the Tibetan Plateau induced by vegetation growth, P. Natl. Acad. Sci.
USA, 112, 9299–9304, 2015.
Smith, M. W. and Riseborough, D. W.: Permafrost monitoring and detection of
climate change, Permafrost Periglac., 7, 301–309, 1996.
Smith, S. L. and Riseborough, D. W.: Modelling the thermal response of
permafrost terrain to right-of-way disturbance and climate warming, Cold.
Reg. Sci. Technol., 60, 92–103, 2010.
Stow, D., Daeschner, S., Hope, A., Douglas, D., Petersen, A., Myneni, R.,
Zhou, L., and Oechel, W.: Variability of the seasonally integrated normalized
difference vegetation index across the north slope of Alaska in the 1990s,
Int. J. Remote Sens., 24, 1111–1117, 2003.
Wickham, H., Lawrence, M., Cook, D., Buja, A., Hofmann, H., and Swayne, D.
F.: The plumbing of interactive graphics, Computation Stat., 24, 207–215,
2009.
Wu, Q., Hou, Y., Yun, H., and Liu, Y.: Changes in active-layer thickness and
near-surface permafrost between 2002 and 2012 in alpine ecosystems,
Qinghai–Xizang (Tibet) Plateau, China, Global Planet. Change, 124, 149–155,
2015.
Wu, Q. B. and Zhang, T. J.: Changes in active layer thickness over the
Qinghai-Tibetan Plateau from 1995 to 2007, J. Geophys. Res.-Atmos., 115, D09107,
https://doi.org/10.1029/2009jd012974,
2010.
Wu, Q. B., Zhang, T. J., and Liu, Y. Z.: Permafrost temperatures and
thickness on the Qinghai-Tibet Plateau, Global Planet. Change, 72, 32–38,
2010.
Xie, Y. H.: animation: An R Package for Creating Animations and
Demonstrating Statistical Methods, J. Stat. Softw., 53, 1–27, 2013.
Yang, M. X., Nelson, F. E., Shiklomanov, N. I., Guo, D. L., and Wan, G. N.:
Permafrost degradation and its environmental effects on the Tibetan Plateau:
A review of recent research, Earth-Sci. Rev., 103, 31–44, 2010.
Yao, T., Pu, J., Lu, A., Wang, Y., and Yu, W.: Recent glacial retreat and
its impact on hydrological processes on the tibetan plateau, China, and
sorrounding regions, Arct. Antarct. Alp. Res., 39, 642–650, 2007.
Yi, S., Wang, X., Qin, Y., Xiang, B., and Ding, Y.: Responses of alpine
grassland on Qinghai-Tibetan plateau to climate warming and permafrost
degradation: a modeling perspective, Environ. Res. Lett., 9, 074014,
https://doi.org/10.1088/1748-9326/9/7/074014,
2014a.
Yi, S., Wischnewski, K., Langer, M., Muster, S., and Boike, J.: Freeze/thaw
processes in complex permafrost landscapes of northern Siberia simulated
using the TEM ecosystem model: impact of thermokarst ponds and lakes, Geosci.
Model Dev., 7, 1671–1689, https://doi.org/10.5194/gmd-7-1671-2014, 2014b.
Yi, S., Woo, M. K., and Arain, M. A.: Impacts of peat and vegetation on
permafrost degradation under climate warming, Geophys. Res. Lett., 34,
L16504,
https://doi.org/10.1029/2007GL030550,
2007.
Zhang, T., Barry, R. G., Knowles, K., Heginbottom, J. A., and Brown, J.:
Statistics and characteristics of permafrost and ground-ice distribution in
the Northern Hemisphere, Polar Geography, 31, 132–154, 2008.
Zhang, T., Frauenfeld, O. W., Serreze, M. C., Etringer, A., Oelke, C.,
McCreight, J., Barry, R. G., Gilichinsky, D., Yang, D., Ye, H., Ling, F., and
Chudinova, S.: Spatial and temporal variability in active layer thickness
over the Russian Arctic drainage basin, J. Geophys. Res.-Atmos., 110, D16101,
https://doi.org/10.1029/2004JD005642,
2005.
Zhang, Y., Carey, S. K., and Quinton, W. L.: Evaluation of the algorithms
and parameterizations for ground thawing and freezing simulation in
permafrost regions, J. Geophys. Res.-Atmos., 113, D17116,
https://doi.org/10.1029/2007JD009343, 2008.
Zhang, Y., Olthof, I., Fraser, R., and Wolfe, S. A.: A new approach to
mapping permafrost and change incorporating uncertainties in ground
conditions and climate projections, The Cryosphere, 8, 2177–2194,
https://doi.org/10.5194/tc-8-2177-2014, 2014.
Short summary
Based on the current situation of permafrost modeling in the Qinghai–Tibet Plateau (QTP), a software PIC was developed to evaluate the temporal–spatial change trends of permafrost, which allows us to automatically compute permafrost indices with daily weather and atmospheric forcing datasets. The main features include computing, visualization, and statistics. The software will serve engineering applications and can be used to assess the impact of climate change on permafrost over the QTP.
Based on the current situation of permafrost modeling in the Qinghai–Tibet Plateau (QTP), a...
