| 1. |
- Chen, H., et al.
(författare)
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Intercomparison of ten ISI-MIP models in simulating discharges along the Lancang-Mekong River basin
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 765
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Tidskriftsartikel (refereegranskat)abstract
- Water resources are of strategic importance for socioeconomic development. Many hydrological models (HMs) and land surface models (LSMs) have been developed for water resources assessment. However, systematic evaluation of discharge simulation from multiple models is still lacking in the Lancang-Mekong River basin. Here, we evaluated the performances of ten HMs and LSMs by evaluating their simulated discharge against observations at the basin scale. The selected models were within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2a) framework driven by Global Soil Wetness Project 3 (GSWP3) climate forcing data. Five discharge percentile series were used to evaluate the model performances for low, mean, and high flows. The intercomparison according to four statistical criteria revealed considerable differences exist in model performances for different discharge percentiles, indicating a large uncertainty caused by the choice of models with different degree of physical complexity and sensitivity to the quality of the input data. The models generally performed better for high flow than for low flow. Furthermore, the models generally performed better in downstream than in upstream, with the exception of close to the estuary, where complex processes involving interactions between freshwater and saline water are present. It is not surprising that the two calibrated model (WaterGAP2 and WAYS) are superior over the other models. This systematic intercomparison provides insights into the model behaviours and accuracies in discharges predicting with varying intensities, which can aid in quantifying uncertainties in water resources simulation at the basin scale. (C) 2020 Elsevier B.V. All rights reserved.
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| 2. |
- Chen, Shiyin, et al.
(författare)
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Tree-ring recorded variations of 10 heavy metal elements over the past 168 years in southeastern China
- 2021
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Ingår i: Elementa: Science of the Anthropocene. - : University of California Press. - 2325-1026. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Heavy metal pollution is a serious concern in the urban area of China. Understanding metal pollution history is crucial for setting up appropriate measures for pollution control. Herein, we report a record of concentrations of 10 heavy metals (Fe, Mn, Cu, Zn, Ni, Cr, Cd, Pb, Co, and Sr) in Pinus massoniana tree rings from Fuzhou City over the past 168 years, which represents the longest tree-ring chronology of heavy metals in China. The studied metals displayed contrasting distribution patterns. Among them, Mn and Sr showed the strongest migration trend with peak concentrations at the pith. Co, Cd, and Pb also showed distinctively high concentrations near the boundary between heartwood and sapwood. Ni, Cu, Cr, and Fe showed an increasing trend possibly due to migration toward bark caused by physiological activities and increasing tourism activities and traffic pollution. The other elements (Cr, Fe, and Zn) with low migration revealed the historical pollution possibly discharged by the Fuzhou Shipping Bureau and other anthropogenic activities. Strong correlations between Cu content and temperature were found, which provides an alternative tree-ring proxy for climate reconstruction. This study provides a long-term perspective of the joint impacts of physiological, environmental, and climatological factors on the concentrations of heavy metals in southeastern China.
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| 3. |
- Cai, Ziyi, et al.
(författare)
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Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties
- 2021
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Ingår i: Journal of Climate. - 0894-8755. ; 34:12, s. 4871-4892
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Tidskriftsartikel (refereegranskat)abstract
- The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.
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| 4. |
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| 5. |
- Cai, Z. Y., et al.
(författare)
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Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties
- 2021
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Ingår i: Journal of Climate. - : American Meteorological Society. - 0894-8755 .- 1520-0442. ; 34:12, s. 4871-4892
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Tidskriftsartikel (refereegranskat)abstract
- The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979-2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models' simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.
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| 6. |
- Chen, Aifang, 1990, et al.
(författare)
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Flood impact on Mainland Southeast Asia between 1985 and 2018 — The role of tropical cyclones
- 2020
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Ingår i: Journal of Flood Risk Management. - : Wiley. - 1753-318X. ; 13:2
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Tidskriftsartikel (refereegranskat)abstract
- Floods are disastrous natural hazards accused of human live losses. As a flood‐prone area, Mainland Southeast Asia (MSEA) has often been hit by floods, resulting in the highest fatality in the world. Despite the destructive flood impacts, how has flood occurrence changed over the past decades, and to what extent did floods affect the MSEA are not yet clear. Using the Dartmouth Flood Observatory large flood data archive, we aim to assess the trend of flood occurrence in the MSEA in 1985–2018, and quantify the associated impacts on humans. Particularly, the contribution of tropical cyclone (TC) landfall induced floods (TCFloods) is quantified, because of the frequent TC landfalls. Results show that (a) occurrence and maximum magnitude of floods by all causes (ALLFloods) significantly increased (p < .01), but not for TCFloods; (b) On average, TCFloods accounted for 24.6% occurrence of ALLFloods; (c) TCFloods caused higher mortality and displacement rate than ALLFloods did. As low flood protection standards in Cambodia and Myanmar is considered a reason for high flood‐induced mortalities, building higher flood protection standards should be taken as a priority for mitigating potential flood impacts. With quantifying flood occurrence and impacts, this study offers scientific understandings for better flood risk management.
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| 7. |
- Chen, Aifang, 1990, et al.
(författare)
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Multidecadal variability of the Tonle Sap Lake flood pulse regime
- 2021
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Ingår i: Hydrological Processes. - : Wiley. - 0885-6087 .- 1099-1085. ; 35:9
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Tidskriftsartikel (refereegranskat)abstract
- Tonle Sap Lake (TSL) is one of the world's most productive lacustrine ecosystems, driven by the Mekong River's seasonal flood pulse. This flood pulse and its long-term dynamics under the Mekong River basin's (MRB) fast socio-economic development and climate change need to be identified and understood. However, existing studies fall short of sufficient time coverage or concentrate only on changes in water level (WL) that is only one of the critical flood pulse parameters influencing the flood pulse ecosystem productivity. Considering the rapidly changing hydroclimatic conditions in the Mekong basin, it is crucial to systematically analyse the changes in multiple key flood pulse parameters. Here, we aim to do that by using observed WL data for 1960-2019 accompanied with several parameters derived from a Digital Bathymetry Model. Results show significant declines of WL and inundation area from the late 1990s in the dry season and for the whole year, on top of increased subdecadal variability. Decreasing (increasing) probabilities of high (low) inundation area for 2000-2019 have been found, in comparison to the return period of inundation area for 1986-2000 (1960-1986). The mean seasonal cycle of daily WL in dry (wet) season for 2000-2019, compared to that for 1986-2000, has shifted by 10 (5) days. Significant correlations and coherence changes between the WL and large-scale circulations (i.e., El Nino-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Indian Ocean Dipole (IOD)), indicate that the atmospheric circulations could have influenced the flood pulse in different time scales. Also, the changes in discharge at the Mekong mainstream suggest that anthropogenic drivers may have impacted the high water levels in the lake. Overall, our results indicate a declining flood pulse since the late 1990s.
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| 8. |
- Chen, Aifang, 1990, et al.
(författare)
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Rising future tropical cyclone-induced extreme winds in the Mekong River Basin
- 2020
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Ingår i: Science Bulletin. - : Elsevier BV. - 2095-9273 .- 2095-9281. ; 65:5, s. 419-424
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Tidskriftsartikel (refereegranskat)abstract
- © 2019 Science China Press The societal impact of extreme winds induced by tropical cyclones (TCs) is a major concern in the Mekong River Basin (MRB). Though no clear trend of landfalling TC intensity along the Vietnam coastline has been observed since the 1970s, climate models project an increasing TC intensity in the 21st century over the Western North Pacific, which is the primary TC source region influencing the MRB. Yet, how future TC activities will affect extreme winds quantitatively in the MRB remains unclear. By employing a novel dynamical downscaling technique using a specialized, coupled ocean-atmospheric model, shorter return periods of maximum wind speed in the MRB for 2081–2100 compared with 1981–2000 are projected based on five global climate models under the RCP8.5 scenario, suggesting increases in the future tropical cyclone intensity. The results point to consistently elevated future TC-related risks that may jeopardize sustainable development, disrupt food supply, and exacerbate conflicts in the region and beyond.
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| 9. |
- 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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| 10. |
- 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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