| 31. |
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| 32. |
- Chen, Tao, et al.
(författare)
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Comprehensive applicability evaluation of multi-source snow depth datasets over the Tibetan Plateau
- 2022
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Ingår i: Journal of Glaciology and Geocryology. - 1000-0240. ; 44:3, s. 795-809
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Tidskriftsartikel (refereegranskat)abstract
- Snow cover over the Tibetan Plateau has an important impact on the regional climate and water cycle. At present, the existing snow cover datasets have great uncertainty across this region, so the applicability assessment is indispensable in order to make best use of the advantages and bypass the disadvantages. In this study, a comprehensive quantitative evaluation of multiple variables and multiple evaluation indicators was carried out for three snow depth datasets over the Tibetan Plateau against the meteorological station observations（OBS）. The three snow depth datasets include one passive microwave remote sensing dataset（CHE）and two reanalysis datasets（ERA5-Land and MERRA2）. The variables are the annual mean snow depth, the annual maximum snow depth, and the annual snow cover days. In addition, the evaluation indicators are seasonal cycle, climatology, maximum value, standard deviation, interannual variation, and trend. A rank score（RS）value of 0~1 is computed for each evaluation indicator of each variable, the larger value of RS indicate relatively better performance of a snow depth dataset. Assessment results imply that, comprehensively considered, MERRA2 exhibits best agreement with OBS, followed by ERA5-Land, and finally CHE. Evaluate based on the RS of each variable, MERRA2 shows better performance on annual maximum snow depth and annual snow cover days, CHE shows better performance on annual mean snow depth. Evaluate based on the RS of each evaluation indicator, CHE shows advantages in describing trend, ERA5-Land exhibits better agreement with OBS on interannual variation, and MERRA2 show better performance on the rest of the indicators including seasonal cycle, climatology, maximum value and standard deviation. The RS statistics in terms of regional average and spatial distribution show that CHE performs better in the former, and ERA5-Land performs better in the latter. On the other hand, there are obvious deficiencies in all three snow depth datasets. MERRA2 has insufficient ability to characterize the interdecadal variation in snow cover, and its qualitative results for trend in snow cover is inconsistent with OBS, the reason for the first deficiency needs to be further studied and the second deficiency may be mainly related to its simulation capability to precipitation trend. ERA5-Land significantly overestimates the snow cover over the Tibetan Plateau, this may be mostly related to its data assimilation scheme. CHE has poor ability to characterize the spatial distribution of snow cover, coarse spatial resolution of passive microwave remote sensing may be the main reason. The conclusions are only applicable to the central and eastern part of the Tibetan Plateau due to the scarcity of meteorological station in west part of the Tibetan Plateau. Based on the remote sensing and reanalysis data, there is great uncertainty in the trend of snow cover in the western part of the Tibetan Plateau. These systematic classification evaluation of the three representative snow depth datasets provides information on data selection and data refinement.
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| 33. |
- Chen, Z., et al.
(författare)
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Deep learning projects future warming-induced vegetation growth changes under SSP scenarios
- 2022
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Ingår i: Advances in Climate Change Research. - : Elsevier BV. - 1674-9278. ; 13:2, s. 251-257
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Tidskriftsartikel (refereegranskat)abstract
- Climate warming has been projected to enhance vegetation growth more strongly in higher latitudes than in lower latitudes, but different projections show distinct regional differences. By employing big data analysis (deep learning), we established gridded, global-scale, climate-driven vegetation growth models to project future changes in vegetation growth under SSP scenarios. We projected no substantial trends of vegetation growth change under the sustainable development scenario (SSP1-1.9) by the end of the 21st century. However, the increase of vegetation growth driven by climate warming shows distinct regional variability under the scenario representing high carbon emissions and severe warming (SSP5-8.5), especially in Northeast Asia where growth could increase by (6.00% ± 4.21%). This may be attributed to the high temperature sensitivities of the deciduous needleleaf forests and permanent wetlands in these regions. When the temperature sensitivity that is defined as permutation importance in deep learning is greater than 0.05, the increase in vegetation growth will be more prominent. In addition, an extreme temperature increase across grasslands, as well as changing land-use management in northern China may also influence the vegetation growth in the future. The results suggest that the sustainable development scenario can maintain stable vegetation growth, and it may be a reliable way to mitigate global warming due to potential climate feedbacks driven by vegetation changes in boreal regions. Deciduous needleleaf forests will be a centre of greening in the future, and it should become the focus of future vegetation dynamics modelling studies and projections. © 2022 The Authors
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| 34. |
- Chen, Z., et al.
(författare)
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Modeling vegetation greenness and its climate sensitivity with deep-learning technology
- 2021
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Ingår i: Ecology and Evolution. - : Wiley. - 2045-7758. ; 11:12, s. 7335-7345
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Tidskriftsartikel (refereegranskat)abstract
- Climate sensitivity of vegetation has long been explored using statistical or process-based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short-term memory (LSTM) network, which is a powerful deep-learning algorithm for long-time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep-learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature. © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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| 35. |
- Du, W. T., et al.
(författare)
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Can summer monsoon moisture invade the Jade Pass in Northwestern China?
- 2020
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Ingår i: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- Heavy precipitation events are increasingly concerned because their significant contribution to annual precipitation in the Northwestern China, which might be related to invasion of summer monsoon moisture. It is interest whether or not the same is Jade Pass as being outside the control of the Asian summer monsoon. In this work, six heavy precipitation events were selected based on the 95 percentiles of the daily precipitation at the 12 weather stations around the Jade Pass from 1970-2000, with consideration of the influences of elevation. The event on June 19th, 2013 was chosen for a detailed examination due to the fact that the day has a large-scale precipitation as revealed by a gridded precipitation dataset over a large region. Using a Weather Research and Forecasting Model (WRF) simulation with high spatiotemporal resolution and in situ isotopic tracing (delta O-18, delta D), under a large-scale heavy precipitation event, this study provides ambitious view at the synoptic scale. A dramatic decrease in the delta O-18, delta D and deuterium (d)-excess of precipitation, very high relative humidity (98%), and reduced air temperature indicate that the precipitation was a result of long-distance-transported monsoon vapor. In addition, the slope of the local water meteoric line (LWML) of the precipitation for this event was very close to that of the global meteoric water line (GWML), indicating the source of moisture was from the ocean. Meanwhile, the WRF simulation confirms that the precipitation at the Jade Pass was not caused by local convection, but by summer monsoon. Both WRF simulation and isotopic tracing support the view that the monsoon moisture could invade Jade Pass at the synoptic scale and impact on precipitation, which need be further investigated.
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| 36. |
- Ehlers, Todd A., et al.
(författare)
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Past, present, and future geo-biosphere interactions on the Tibetan Plateau and implications for permafrost
- 2022
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Ingår i: Earth-Science Reviews. - : Elsevier BV. - 0012-8252. ; 234
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Tidskriftsartikel (refereegranskat)abstract
- Interactions between the atmosphere, biosphere, cryosphere, hydrosphere, and geosphere are most active in the critical zone, a region extending from the tops of trees to the top of unweathered bedrock. Changes in one or more of these spheres can result in a cascade of changes throughout the system in ways that are often poorly understood. Here we investigate how past and present climate change have impacted permafrost, hydrology, and ecosystems on the Tibetan Plateau. We do this by compiling existing climate, hydrologic, cryosphere, biosphere, and geologic studies documenting change over decadal to glacial-interglacial timescales and longer. Our emphasis is on showing present-day trends in environmental change and how plateau ecosystems have largely flourished under warmer and wetter periods in the geologic past. We identify two future pathways that could lead to either a favorable greening or unfavorable degradation and desiccation of plateau ecosystems. Both paths are plausible given the available evidence. We contend that the key to which pathway future generations experience lies in what, if any, human intervention measures are implemented. We conclude with suggested management strategies that can be implemented to facilitate a future greening of the Tibetan Plateau.
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| 37. |
- Fang, Keyan, et al.
(författare)
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Co-varying temperatures at 200hpa over the Earth’s three poles
- 2021
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Ingår i: Science China Earth sciences. - : Springer Science and Business Media LLC. - 1674-7313 .- 1869-1897. ; 64
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Tidskriftsartikel (refereegranskat)abstract
- The Earth’s three poles, the North Pole, South Pole, and Third Pole (i.e., the Tibetan Plateau and its surroundings), hold the largest amount of fresh water on Earth as glaciers, sea ice, and snow. They are sensitive to climate change. However, the linkages between climate variations of the three poles, particularly between the South Pole and Third Pole, remain largely unknown. The temperatures at 200 hPa over the three poles are the highest in the summer and are less affected by surface conditions, which could reflect large-scale dynamic linkages. Temperatures at 200 hPa peak the three poles during their respective hemispheric summer and exhibit in-phase variations on interdecadal timescales (10-100 years). The 200 hPa temperatures over the North Pole and South Pole were significantly correlated with the Brewer-Dobson circulation (BDC), which transports stratospheric ozone poleward, heating the air at 200 hPa. Tropopause warming over the Third Pole was found to enhance the poleward BDC, particularly to the South Pole, linking the Third Pole’s climate to the other two poles. Additionally, the Interdecadal Pacific Oscillation (IPO) also exhibits links with the 200 hPa temperatures of the three poles.
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| 38. |
- Fang, M., et al.
(författare)
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Arctic amplification modulated by Atlantic Multidecadal Oscillation and greenhouse forcing on multidecadal to century scales
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Enhanced warming in the Arctic (Arctic amplification, AA) in the last decades has been linked to several factors including sea ice and the Atlantic Multidecadal Oscillation (AMO). However, how these factors contributed to AA variations in a long-term perspective remains unclear. By reconstructing a millennial AA index combining climate model simulations with recently available proxy data, this work determines the important influences of the AMO and anthropogenic greenhouse gas forcing on AA variations in the last millennium, leading to identification of a significant downward trend of AA on top of a sustained strong AMO modulation at the multidecadal scales. The decreased AA during the industrial era was strongly associated with the anthropogenic forcing, proving the emerging role of the forcing in reducing the AA strength. Reconstructed Arctic amplification shows a significant downward trend over the past millennium which can to a large part be explained by the strength of the Atlantic Multidecadal Oscillation and recent anthropogenic greenhouse gas forcing.
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| 39. |
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| 40. |
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