Search

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPMIMERG V06 Final Run products in different climatic and topographical regions of China for the 2014-20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R .0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April-September) (RBias 5 7.41%) than during the dry season (RBias 5 13.65%). 2) GPMIMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world. [ABSTRACT FROM AUTHOR]

This paper presents a large data set on carbon isotope composition (δ13C) of modern soils which were collected under the main vegetation communities along an altitude of 1250–5500 m above sea level in the Qinghai-Tibetan Plateau. The δ13C values of 198 samples range from -28.6 to -15.1‰ versus PDB and exhibit a clean relation to different vegetation communities from forest (-25.9 ± 1.2‰) to shrub (-24.7 ± 1.4‰), steppe (-23.1 ± 1.3‰), alpine meadow (-23.6 ± 0.7‰), alpine desert steppe (-21.3 ± 1.6‰), and alpine desert (-18.9 ± 2.5‰). We attributed the observed variability in δ13C values to that the mean annual precipitation (MAP) and the mean annual temperature (MAT) are the main factors controlling the distribution of vegetation types in the Tibetan Plateau, which causes the change in carbon isotope composition of modern soils at any given altitude. The result of both linear and nonlinear regression analyses also confirms that MAP and MAT are the major factors affecting the δ13C values of surface soils. In the absence of favorable moisture and temperature conditions, low pCO2 alone is not sufficient to cause the distinct changes in carbon isotope composition of modern soils in the Tibetan Plateau. This study provides some fundamental information on the carbon isotope composition of terrestrial carbon pools and bears some practical significance for the use of carbon isotope data to document vegetation changes and environmental conditions of the high plateau in the past. [ABSTRACT FROM AUTHOR]

Measurements of precipitation isotopic composition have been conducted on a daily basis for 1 yr at Bomi, in the southeast Tibetan Plateau, an area affected by the interaction of the southwest monsoon, the westerlies, and Tibetan high pressure systems, as well as at Lhasa, situated west of Bomi. The measured isotope signals are analyzed both on an event basis and on a seasonal scale using available meteorological information and airmass trajectories. The processes driving daily and seasonal isotopic variability are investigated using multidecadal climate simulations forced by twentieth-century boundary conditions and conducted with two different isotopic atmospheric general circulation models [the isotopic version of the Laboratoire de Méétééorologie Dynamique GCM (LMDZiso) and the ECHAM4iso model]. Both models use specific nudging techniques to mimic observed atmospheric circulation fields. The models simulate a wet and cold bias on the Tibetan Plateau together with a dry bias in its southern part. A zoomed LMDZ simulation conducted with ~50-km local spatial resolution dramatically improves the simulation of isotopic compositions of precipitation on the Tibetan Plateau. Simulated water isotope fields are compared with new data and with previous observations, and regional differences in moisture origins are analyzed using back-trajectories. Here, the focus is on relationships between the water isotopes and climate variables on an event and seasonal scale and in terms of spatial and altitudinal isotopic gradients. Enhancing the spatial resolution is crucial for improving the simulation of the precipitation isotopic composition. [ABSTRACT FROM AUTHOR]