scholarly journals Climate Change Will Reduce the Carbon Use Efficiency of Terrestrial Ecosystems on the Qinghai-Tibet Plateau: An Analysis Based on Multiple Models

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yue Wang ◽  
Jinming Hu ◽  
Yanzheng Yang ◽  
Ruonan Li ◽  
Changhui Peng ◽  
...  
Keyword(s):  
Climate Change ◽  
Terrestrial Ecosystems ◽  
Tibet Plateau ◽  
Model Intercomparison ◽  
Upward Trend ◽  
The Future ◽  
Long Time ◽  
Intercomparison Project ◽  
Use Efficiency ◽  
Qinghai Tibet Plateau

The carbon use efficiency (CUE) of ecosystems, expressed as the ratio of net primary production (NPP) and gross primary production (GPP), is extremely sensitive to climate change and has a great effect on the carbon cycles of terrestrial ecosystems. Climate change leads to changes in vegetation, resulting in different CUE values, especially on the Qinghai-Tibet Plateau, one of the most climate-sensitive regions in the world. However, the change trend and the intrinsic mechanism of climate effects on CUE in the future climate change scenario are not clear in this region. Based on the scheme of the coupled model intercomparison project (CMIP6), we analyze the simulation results of the five models of the scenario model intercomparison project (ScenarioMIP) under three different typical future climate scenarios, including SSP1-2.6, SSP3-7.0 and SSP5-8.5, on the Qinghai-Tibet Plateau in 2015–2100 with methods of model-averaging to average the long-term forecast of the five several well-known forecast models for three alternative climate scenarios with three radiative forcing levels to discuss the CUE changes and a structural equations modeling (SEM) approach to examine how the trends in GPP, NPP, and CUE related to different climate factors. The results show that (1) GPP and NPP demonstrated an upward trend in a long time series of 86 years, and the upward trend became increasingly substantial with the increase in radiation forcing; (2) the ecosystem CUE of the Qinghai-Tibet Plateau will decrease in the long time series in the future, and it shows a substantial decreasing trend with the increase in radiation forcing; and (3) the dominant climate factor affecting CUE is temperature of the factors included in these models, which affects CUE mainly through GPP and NPP to produce indirect effects. Temperature has a higher comprehensive effect on CUE than precipitation and CO2, which are negative effects on CUE on an annual scale. Our finding that the CUE decreases in the future suggests that we must pay more attention to the vegetation and CUE changes, which will produce great effects on the regional carbon dynamics and balance.

scholarly journals Carbon Balance of Grasslands on the Qinghai-Tibet Plateau under Future Climate Change: A Review

2020 ◽  
Vol 12 (2) ◽  
pp. 533
Author(s):  
Rong Leng ◽  
Quanzhi Yuan ◽  
Yushuang Wang ◽  
Qian Kuang ◽  
Ping Ren
Keyword(s):  
Climate Change ◽  
Carbon Balance ◽  
Future Climate ◽  
Tibet Plateau ◽  
Future Climate Change ◽  
Balance Study ◽  
Model Projection ◽  
Climatic Environment ◽  
The Future ◽  
Qinghai Tibet Plateau

Climate change has brought significant impacts upon the natural ecological environment and human social development. The future carbon balance study has become an important part of research on the impacts of climate change. The Qinghai-Tibet Plateau (QTP) is a key area for studying climate change. Grassland, as a typical ecosystem of the QTP, embodies the sensitivity of the plateau to the climatic environment, so the carbon balance of grassland under future climate change conditions is important for studying global change. This paper reviewed the literature on carbon balance projection of grassland on the QTP under climate change. Two types of research methods were used to analyze and discuss the studies’ results, including experimental scenario projection and model projection. The experiment projected that appropriate temperature and moisture could enhance the carbon sink capacity of a grassland ecosystem, where moisture played a leading role. The model projection results showed that the carbon balance under different spatial and temporal scales were different. Although both can project the carbon balance of the study area, there are still some uncertainties. In addition, this research area should also consider the influence of human activity and plateau pikas to more accurately project the future carbon balance.

scholarly journals Demographic history and genomic response to environmental changes in a rapid radiation of wild rats

2021 ◽  
Author(s):  
Deyan Ge ◽  
Anderson Feijó ◽  
Zhixin Wen ◽  
Alexei V Abramov ◽  
Liang Lu ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Demographic History ◽  
Terrestrial Ecosystems ◽  
Tibet Plateau ◽  
Global Changes ◽  
Rapid Radiation ◽  
Protein Functions ◽  
Wild Rats ◽  
Qinghai Tibet Plateau ◽  
Genomic Response

Abstract For organisms to survive and prosper in a harsh environment, particularly under rapid climate change, poses tremendous challenges. Recent studies have highlighted the continued loss of megafauna in terrestrial ecosystems and the subsequent surge of small mammals, such as rodents, bats, lagomorphs, and insectivores. However, the ecological partitioning of these animals will likely lead to large variation in their responses to environmental change. In the present study, we investigated the evolutionary history and genetic adaptations of white-bellied rats (Niviventer Marshall, 1976), which are widespread in the natural terrestrial ecosystems in Asia but also known as important zoonotic pathogen vectors and transmitters. The southeastern Qinghai-Tibet Plateau (QHTP) was inferred as the origin center of this genus, with parallel diversification in temperate and tropical niches. Demographic history analyses from mitochondrial and nuclear sequences of Niviventer demonstrated population size increases and range expansion for species in Southeast Asia, and habitat generalists elsewhere. Unexpectedly, population increases were seen in N. eha, which inhabits the highest elevation among Niviventer species. Genome scans of nuclear exons revealed that among the congeneric species, N. eha has the largest number of positively selected genes. Protein functions of these genes are mainly related to olfaction, taste and tumor suppression. Extensive genetic modification presents a major strategy in response to global changes in these alpine species.

scholarly journals Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change

2021 ◽  
Vol 13 (4) ◽  
pp. 669
Author(s):  
Hanchen Duan ◽  
Xian Xue ◽  
Tao Wang ◽  
Wenping Kang ◽  
Jie Liao ◽  
...  
Keyword(s):  
Climate Change ◽  
Alpine Meadow ◽  
Global Climate ◽  
Growing Season ◽  
Tibet Plateau ◽  
Vegetation Types ◽  
Analysis Method ◽  
Alpine Steppe ◽  
Qinghai Tibet Plateau ◽  
Variation Trends

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

scholarly journals The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

2016 ◽  
Vol 9 (9) ◽  
pp. 3461-3482 ◽  
Author(s):  
Brian C. O'Neill ◽  
Claudia Tebaldi ◽  
Detlef P. van Vuuren ◽  
Veronika Eyring ◽  
Pierre Friedlingstein ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
21St Century ◽  
Integrated Assessment ◽  
Climate Model ◽  
Model Intercomparison ◽  
Wide Range ◽  
Scenario Model ◽  
Intercomparison Project

Abstract. Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals The Impacts of Human Activities on Ecosystems within China’s Nature Reserves

2019 ◽  
Vol 11 (23) ◽  
pp. 6629
Author(s):  
Ping Zhu ◽  
Wei Cao ◽  
Lin Huang ◽  
Tong Xiao ◽  
Jun Zhai
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Arid Zone ◽  
Ecosystem Dynamics ◽  
Tibet Plateau ◽  
Warm Temperate Zone ◽  
Nighttime Light ◽  
Qinghai Tibet Plateau ◽  
Warm Temperate ◽  
Semi Arid

Protected areas (PAs) provide refuges for threatened species and are considered to be the most important approach to biodiversity conservation. Besides climate change, increasing human population is the biggest threat to biodiversity and habitats in PAs. In this paper, the temporal and spatial variations of land cover changes (LCC), vegetation fraction (VFC), and net primary productivity (NPP) were studied to present the ecosystem dynamics of habitats in 6 different types of national nature reserves (NNRs) in 8 climate zones in China. Furthermore, we used Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light datasets and the human disturbance (HD) index estimated from LCC to quantify the living and developing human pressures within the NNRs in the period 2000–2013. The results showed that (1) the living human activities of NNRs increased apparently in the humid warm-temperate zone, Qinghai-Tibet Plateau, mid-temperate semi-arid zone, and mid-temperate humid zone, with the highest increase of nighttime light observed in inland wetlands; (2) the developing human activities in NNRs indicated by the HD index were higher in the humid warm-temperate zone and mid-temperate semi-arid zone as a result of increasing areas of agricultural and built activities, and lower in the sub-tropics due to improved conservation of forest ecosystems; (3) the relationship between HD and VFC suggests that ecosystems in most NNRs of south-subtropics, mid-temperate arid zone and Qinghai-Tibet Plateau were predominantly impacted by climate change. However, HDs were the prevalent factor of ecosystem dynamics in most NNRs of north-subtropics, mid-temperate semi-arid and humid zones.

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