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Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018 期刊论文
NATURE, 2020, 581 (7808) : 294-+
作者:  Ibrahim, Nizar;  Maganuco, Simone;  Dal Sasso, Cristiano;  Fabbri, Matteo;  Auditore, Marco;  Bindellini, Gabriele;  Martill, David M.;  Zouhri, Samir;  Mattarelli, Diego A.;  Unwin, David M.;  Wiemann, Jasmina;  Bonadonna, Davide;  Amane, Ayoub;  Jakubczak, Juliana;  Joger, Ulrich;  Lauder, George V.;  Pierce, Stephanie E.
收藏  |  浏览/下载：33/0  |  提交时间：2020/05/25

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover(1-3). These changes in snow cover affect Earth'  s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere(4-6). In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking(7-9). Here we use the new GlobSnow 3.0 dataset to show that the 1980-2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 +/- 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40 degrees N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433-3,380 gigatonnes (mean 2,867) to 2,846-3,062 gigatonnes (mean 2,938)-a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia  both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth'  s energy, water and carbon budgets.


Applying a bias correction to a state-of-the-art dataset covering non-alpine regions of the Northern Hemisphere and to three other datasets yields a more constrained quantification of snow mass in March from 1980 to 2018.


  
Improved protein structure prediction using potentials from deep learning 期刊论文
NATURE, 2020, 577 (7792) : 706-+
作者:  Ma, Runze;  Cao, Duanyun;  Zhu, Chongqin;  Tian, Ye;  Peng, Jinbo;  Guo, Jing;  Chen, Ji;  Li, Xin-Zheng;  Francisco, Joseph S.;  Zeng, Xiao Cheng;  Xu, Li-Mei;  Wang, En-Ge;  Jiang, Ying
收藏  |  浏览/下载：156/0  |  提交时间：2020/07/03

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)  however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).


  
Mass balance of the Greenland Ice Sheet from 1992 to 2018 期刊论文
NATURE, 2020, 579 (7798) : 233-+
作者:  Scudellari, Megan
收藏  |  浏览/下载：31/0  |  提交时间：2020/04/16

The Greenland Ice Sheet has been a major contributor to global sea-level rise in recent decades(1,2), and it is expected to continue to be so(3). Although increases in glacier flow(4-6) and surface melting(7-9) have been driven by oceanic(10-12) and atmospheric(13,14) warming, the magnitude and trajectory of the ice sheet'  s mass imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet'  s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. The ice sheet was close to a state of balance in the 1990s, but annual losses have risen since then, peaking at 345 +/- 66 billion tonnes per year in 2011. In all, Greenland lost 3,902 +/- 342 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.8 +/- 0.9 millimetres. Using three regional climate models, we show that the reduced surface mass balance has driven 1,964 +/- 565 billion tonnes (50.3 per cent) of the ice loss owing to increased meltwater runoff. The remaining 1,938 +/- 541 billion tonnes (49.7 per cent) of ice loss was due to increased glacier dynamical imbalance, which rose from 46 +/- 37 billion tonnes per year in the 1990s to 87 +/- 25 billion tonnes per year since then. The total rate of ice loss slowed to 222 +/- 30 billion tonnes per year between 2013 and 2017, on average, as atmospheric circulation favoured cooler conditions(15) and ocean temperatures fell at the terminus of Jakobshavn Isbr AE(16). Cumulative ice losses from Greenland as a whole have been close to the rates predicted by the Intergovernmental Panel on Climate Change for their high-end climate warming scenario(17), which forecast an additional 70 to 130 millimetres of global sea-level rise by 2100 compared with their central estimate.


  
Response of the anomalous western North Pacific anticyclone during El Nino mature winter to global warming 期刊论文
CLIMATE DYNAMICS, 2019
作者:  Wang, Yuhao;  He, Chao;  Li, Tim
收藏  |  浏览/下载：22/0  |  提交时间：2020/02/17
Global warming  El Nino  Western North Pacific anticyclone  Diabatic heating anomaly  Mean state static stability  Moist enthalpy advection  
Weather noise leading to El Nino diversity in an ocean general circulation model 期刊论文
CLIMATE DYNAMICS, 2019, 52 (12) : 7235-7247
作者:  Lee, Jong-Won;  Yeh, Sang-Wook;  Jo, Hyun-Su
收藏  |  浏览/下载：19/0  |  提交时间：2019/11/26
El Nino diversity  Weather noise  MOM4  Mean state  CP and EP El Nino  
Do We Need to Parameterize Mesoscale Convective Organization to Mitigate the MJO-Mean State Trade-Off? 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2019, 46 (4) : 2293-2301
作者:  Ahn, Min-Seop;  Kim, Daehyun;  Park, Sungsu;  Ham, Yoo-Geun
收藏  |  浏览/下载：18/0  |  提交时间：2019/04/09
GCM  mesoscale convective organization parameterization  MJO simulation  mean state simulation  
Ecosystem context illuminates conflicting roles of plant diversity in carbon storage 期刊论文
ECOLOGY LETTERS, 2018, 21 (11) : 1604-1619
作者:  Adair, E. Carol;  Hooper, David U.;  Paquette, Alain;  Hungate, Bruce A.
收藏  |  浏览/下载：29/0  |  提交时间：2019/04/09
biodiversity  community-weighted mean  ecosystem functioning  ecosystem services  functional diversity  functional traits  interactive factors  phylogenetic diversity  species richness  state factors  
Cause of interdecadal change of tropical cyclone controlling parameter in the western North Pacific 期刊论文
CLIMATE DYNAMICS, 2018, 51: 719-732
作者:  Hu, Feng;  Li, Tim;  Liu, Jia;  Peng, Melinda
收藏  |  浏览/下载：15/0  |  提交时间：2019/04/09
Interdecadal change of TC interannual frequency  Large-scale factors  Mean state change  
Low-Frequency Variability and the Unusual Indian Ocean Dipole Events in 2015 and 2016 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (2) : 1040-1048
作者:  Zhang, Lianyi;  Du, Yan;  Cai, Wenju
收藏  |  浏览/下载：15/0  |  提交时间：2019/04/09
IOD  thermocline  low-frequency variability  Indian Ocean  mean state  
Why 2015 was a strong El Nino and 2014 was not 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2017, 44 (16)
作者:  Wang, Guomin;  Hendon, Harry H.
收藏  |  浏览/下载：14/0  |  提交时间：2019/04/09
El Nino dynamics  Pacific mean state  atmosphere-ocean interaction  El Nino 2014  El Nino 2015