What Controls the Skill of General Circulation Models to Simulate the Seasonal Cycle in Water Isotopic Composition in the Tibetan Plateau Region?

Shi, Xiaoyi; Risi, Camille; Li, Laurent; Wang, Xuejie; Pu, Tao; Zhang, Guotao; Zhang, Yuan; Wang, Zhiyuan; Kong, Yanlong

doi:10.1029/2022jd037048

Cited by 6 publications

(5 citation statements)

References 135 publications

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“…The difference in the range of variations (between observed and model values) is clearly evident in the frequency distribution of δD values (Figure 5). The negative isotope biases in the models during the ISM have also been reported in a few earlier studies [26,28,50]. In our earlier study [27], we identified that the underestimation of the mean isotope values can be due to (a) an inaccurate vapour isotope profile and (b) a lower estimate of the raindrop evaporation.…”

Section: Comparison Between Observed and Model Rain Isotope Valuessupporting

confidence: 84%

“…The limited available studies dealing with the performance of the GCMs over the Asian monsoon region show that two nudged models-LMDZ4 and IsoGSM Nudged [26][27][28] -perform better over these regions. The other models, in contrast, show large biases in various physical fields like temperature, humidity, wind etc., both in monthly and seasonal scales.…”

Section: Study Area and Choice Of Gcmsmentioning

confidence: 99%

“…However, due to the paucity of longterm rain isotope data, studies evaluating the performances of GCMs in this region are limited [26,27]. A recent study by Shi et al [28] documented a strong seasonality in the rain isotope ratios over the Tibetan Plateau (TP). Their analyses indicate that most GCMs underestimate seasonality of the isotopic composition in the precipitation and vapour.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Raindrop Evaporation Deficit in General Circulation Models from Observed and Model Rain Isotope Ratios on the West Coast of India

Sengupta,

Bhattacharya,

Sunil

et al. 2023

Atmosphere

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Raindrop evaporation is an important sub-cloud process that modifies rainfall amount and rainwater isotope values. Earlier studies have shown that various general circulation models (GCMs) do not incorporate this process properly during the simulation of water isotope ratios (oxygen and hydrogen). Our recent study has demonstrated that an inadequate estimation of this process for the Indian Summer Monsoon (ISM) results in significant biases (model-observed values) in the simulation of various GCMs on a monthly scale. However, a quantitative estimation was lacking. The magnitude of raindrop evaporation depends upon ambient humidity and temperature, which vary considerably during the ISM. Consequently, the isotope biases would also vary over various time scales. The present study aims to investigate the magnitude of the monthly scale variation in raindrop evaporation in the simulations and its causal connection with the corresponding variation in isotope biases. Towards this, we compare an 11-year-long (1997–2007) dataset of rain isotope ratios (both oxygen and hydrogen) from an Indian station, Kozhikode (Kerala), obtained under the Global Network of Isotopes in Precipitation (GNIP) programme of the International Atomic Energy Agency (IAEA) with the corresponding outputs of two isotope-enabled nudged GCMs—ISOGSM and LMDZ4. The raindrop evaporation fractions are estimated for 44 ISM months (June–September) of the study period using the Stewart (1975) formalism. Using a simple condensation–accretion model based on equilibrium fractionation from vapour, obtained from two adopted vapour isotope profiles, we estimate the liquid water isotope ratios at the cloud base. Considering this water as the initial rain, the raindrop evaporation fractions are estimated using the observed oxygen and hydrogen isotope ratios of Kozhikode surface rain samples. The estimated fractions show strong positive correlations with the isotope biases (R2 = 0.60 and 0.66). This suggests that lower estimates of raindrop evaporation could be responsible for the rain isotope biases in these two GCMs.

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Section: Comparison Between Observed and Model Rain Isotope Valuessupporting

confidence: 84%

Section: Study Area and Choice Of Gcmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying Raindrop Evaporation Deficit in General Circulation Models from Observed and Model Rain Isotope Ratios on the West Coast of India

Sengupta,

Bhattacharya,

Sunil

et al. 2023

Atmosphere

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“…The latest and most commonly used intercomparison simulation data set is the SWING phase 2 (Risi et al., 2012). This data set has supported numerous studies that interpret measured isotope observations or compare those findings with other models (Conroy et al., 2013; S. Dee et al., 2015; Hu et al., 2018; Shi et al., 2022). However, simulations under this framework have not been nudged using consensus reanalysis data sets, and some results were free‐running simulations that followed the experiment design for the comparison of Atmospheric Model Intercomparison Project simulations (Gates, 1992).…”

Section: Introductionsupporting

confidence: 76%

Process‐Based Intercomparison of Water Isotope‐Enabled Models and Reanalysis Nudging Effects

Bong,

Cauquoin,

Okazaki

et al. 2024

JGR Atmospheres

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The products from the Stable Water Isotope Intercomparison Group, Phase 2, are currently used for numerous studies, allowing water isotope model‐data comparisons with various isotope‐enabled atmospheric general circulation model (AGCMs) outputs. However, the simulations under this framework were performed using different parameterizations and forcings. Therefore, a uniform experimental design with state‐of‐the‐art AGCMs is required to interpret isotope observations rigorously. Here, we evaluate the outputs from three isotope‐enabled numerical models nudged by three different reanalysis products and investigate the ability of the isotope‐enabled AGCMs to reproduce the spatial and temporal patterns of water isotopic composition observed at the surface and in the atmospheric airborne water. Through correlation analyses at various spatial and temporal scales, we found that the model's performance depends on the model or reanalysis we use, the observations we compare, and the vertical levels we select. Moreover, we employed the stable isotope mass balance method to conduct decomposition analyses on the ratio of isotopic changes in the atmosphere. Our goal was to elucidate the spread in simulated atmospheric column δ18O, which is influenced by factors such as evaporation, precipitation, and horizontal moisture flux. Satisfying the law of conservation of water isotopes, this budget method is expected to explain various fractionation phenomena in atmospheric meteorological and climatic events. It also aims to highlight the spreads in modeled isotope results among different experiments using multiple models and reanalyses, which are primarily dominated by uncertainties in moisture flux and precipitation, respectively.

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“…However, the spatial resolutions of climate models are usually coarse, and many topography-related information is smoothened. The performances of prediction models vary greatly in large spatial scale, with usually good performance in plain areas and poor simulation in complex terrains [18,19], indicating the importance of altitude correction of precipitation isotopes especially in a complex topography with large altitude fluctuation [20,21]. Compared to some global or regional long-term mean products of precipitation isotopes [22][23][24], the climate-model-derived simulation should be improved in spatial resolution, especially in small-scale cases with complex terrain, where altitude correction or downscaling is crucial for climate-model-simulated isotope data.…”

Section: Introductionmentioning

confidence: 99%

Altitude Correction of GCM-Simulated Precipitation Isotopes in a Valley Topography of the Chinese Loess Plateau

Xiao,

Yang,

Yoshimura

et al. 2023

Sustainability

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Altitude is one of the important factors influencing the spatial distribution of precipitation, especially in a complex topography, and simulations of isotope-enabled climate models can be improved by altitude correlation. Here we compiled isotope observations at 12 sites in Lanzhou, and examined the relationship between isotope error and altitude in this valley in the Chinese Loess Plateau using isoGSM2 isotope simulations. Before altitude correction, the performance using the nearest four grid boxes to the target site is better than that using the nearest box; the root mean square error in δ18O using the nearest four grid boxes averagely decreases by 0.37‰ compared to that using the nearest grid boxes, and correlation coefficient increases by 0.05. The influences of altitude on precipitation isotope errors were examined, and the linear relationship between altitude error and isotope simulations was calculated. The strongest altitude isotopic gradient between δ18O mean bias error and altitude error is in summer, and the weakest is in winter. The regression relationships were used to correct the simulated isotope composition. After altitude correction, the root mean square error decreases by 1.21‰ or 0.86‰ using the nearest one or four grid boxes, respectively, and the correlation coefficient increases by 0.13 or 0.08, respectively. The differences between methods using the nearest one or four grids are also weakened, and the differences are 0.02‰ for root mean square error and −0.01 for the correlation coefficient. The altitude correction of precipitation isotopes should be considered to downscale the simulations of climate models, especially in complex topography.

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What Controls the Skill of General Circulation Models to Simulate the Seasonal Cycle in Water Isotopic Composition in the Tibetan Plateau Region?

Cited by 6 publications

References 135 publications

Quantifying Raindrop Evaporation Deficit in General Circulation Models from Observed and Model Rain Isotope Ratios on the West Coast of India

Quantifying Raindrop Evaporation Deficit in General Circulation Models from Observed and Model Rain Isotope Ratios on the West Coast of India

Process‐Based Intercomparison of Water Isotope‐Enabled Models and Reanalysis Nudging Effects

Altitude Correction of GCM-Simulated Precipitation Isotopes in a Valley Topography of the Chinese Loess Plateau

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