Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods

Guo, Lianyi; Jiang, Zhihong; Chen, Deliang; Treut, Hervé Le; Li, Laurent

doi:10.1007/s10584-020-02841-z

Cited by 20 publications

(8 citation statements)

References 44 publications

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“…The bias correction approach in this study is one of the uncertainties. Although we have analyzed different probability functions to fit the daily temperature and precipitation distributions and selected the most suitable function to project the frequency of extreme events, the experiments are still limited and it is very necessary to explore the optimal statistical downscaling method in further study, including the other probability functions, improved QM approaches, etc (Guo et al., 2020; Zhu et al., 2022). On the other hand, the methods for defining different warming levels and the thresholds of very extreme or ultra‐extreme events also are factors of uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…Generally, the QM has a premise that the statistical relationship (i.e., CDF) between the observation and the simulation will remain stable in the future period (Yang et al., 2018). However, it ignores potential changes in the CDF of meteorological variables under the background of future climate change (Guo et al., 2020). Thus, improved QM methods have been emerging to remediate this issue in recent years.…”

Section: Introductionmentioning

confidence: 99%

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How Extreme Events in China Would Be Affected by Global Warming—Insights From a Bias‐Corrected CMIP6 Ensemble

et al. 2023

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The Paris Agreement adopted by 196 Parties at COP21 in 2015 set a global warming goal, which is to keep global warming to well below 2°C, preferably to 1.5°C, relative to the pre-industrial period (IPCC, 2018). To achieve this goal, every participating country has proposed its emission reduction roadmap and made the corresponding policies. As the largest developing country and one of the most carbon emitters, China proposed its target in

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Extreme Events in China Would Be Affected by Global Warming—Insights From a Bias‐Corrected CMIP6 Ensemble

et al. 2023

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“…The findings indicate a significant improvement in the results of regional climate models with respect to observations for wind speed, temperature, and precipitation distributions (Vrac et al 2012). The CDF-t method is an advancement of the quantile mapping (QM) bias correction method (Guo et al 2020). The QM method was shown to have the potential to reduce biases in palaeoclimate simulations (Beyer et al 2020).…”

Section: Introductionmentioning

confidence: 95%

Improving biome and climate modelling for a set of past climate conditions: evaluating bias correction using the CDF-t approach

Zapolska

Vrac

Quiquet

et al. 2023

Environ. Res.: Climate

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Climate model simulations are inherently biased. It is a notably difficult problem when dealing with climate impact assessments and model-data integration. This is especially true when looking at derived quantities such as biomes, where not only climate but also vegetation dynamics biases come into play. To overcome such difficulties, we evaluate the performance of an existing methodology to correct climate model outputs, applied here for the first time to long past climate conditions. The proposed methodology relies on the “Cumulative Distribution Function - transform” (CDF-t) technique, which allows to account for climate change within the bias-correction procedure. The results are evaluated in two independent ways: i- using forward modelling, so that model results are directly comparable to reconstructed vegetation distribution; ii- using climatic reconstructions based on an inverse modelling approach. The modelling is performed using the intermediate complexity model iLOVECLIM in the standard global and interactively downscaled over the Europe version. The combined effects of dynamical downscaling and bias correction resulted in significantly stronger agreement between the simulated results and pollen-based biome reconstructions (BIOME6000) for the pre-industrial (0.18 versus 0.44) and mid-Holocene (0.31 versus 0.40). Higher correlation is also observed between statistically modelled global gridded potential natural distribution and modelled biomes (0.36 versus 0.41). Similarly, we find higher correlation between the reconstructed and the modelled temperatures for the mid-Holocene (0.02 versus 0.21). No significant difference is found for the Last Glacial Maximum when using temperature reconstructions, due to the low number of data points available. Our findings show that the application of the CDF-t method on simulated climate variables enables us to simulate palaeoclimate and vegetation distribution in better agreement with independent reconstructions.

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“…When investigating future changes in such events, three methods have generally been undertaken. First, hydrology‐related indices (e.g., 95th percentile of rainday amounts (P95) and the standardized precipitation‐evapotranspiration index) using model simulations have been used to explore future changes in floods, droughts and extreme precipitation (Guo et al., 2020; Khan et al., 2020; Zhao et al., 2021). Second, the changing nature of the RPs of extreme precipitation in climate projections, using the generalized extreme value (GEV) distribution, has been evaluated (Frei et al., 2006; Zhang et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Future Projections of Extreme Integrated Water Vapor Transport and Population Exposure Over the Asian Monsoon Region

Guo,

Shi,

Zhao

2023

Earth's Future

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Global warming leads to an intensification of the water cycle and an increase in extreme climate events. Most previous researchers have used precipitation to project extreme hydrological changes. However, compared with precipitation, global climate models (GCMs) have a better performance in simulating the integrated vapor transport (IVT). IVT is thus a reliable tool for understanding how hydrological extremes may change in the future. In this study, we first discuss the relationship between IVT and precipitation over the Asian monsoon (AM) region during 1979–2014. We then assess the climatology, variability and extremes of IVT over the AM region and subregions using 16 GCMs from CMIP6. We further apply GCMs to project changes in the magnitude and probability of dangerous extreme IVT events (e.g., 20‐, 50‐ and 100‐year events) in the future. Finally, we quantify the population exposure to extreme IVT events, and explain change in exposure and its uncertainty. Results show that CMIP6 GCMs capture the spatial distribution of IVT well; the intensity of simulated IVT is lower than the ERA5 IVT. Under global warming of 1.5, 2, and 3°C, the magnitude of extreme events, which is independent of the return period (RP), increases over the AM region with relative changes of about 7%, 12%, and 21%, respectively. The probability increases consistently with global warming and the RP. The increase in exposure for the South Asian monsoon (East Asian monsoon) region is both contributed by more extreme IVT events and higher (fewer) population counts; the former has a larger proportion.

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Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods

Cited by 20 publications

References 44 publications

How Extreme Events in China Would Be Affected by Global Warming—Insights From a Bias‐Corrected CMIP6 Ensemble

How Extreme Events in China Would Be Affected by Global Warming—Insights From a Bias‐Corrected CMIP6 Ensemble

Improving biome and climate modelling for a set of past climate conditions: evaluating bias correction using the CDF-t approach

Future Projections of Extreme Integrated Water Vapor Transport and Population Exposure Over the Asian Monsoon Region

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