Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction

Duan, Chunfeng; Wang, Pengling; Cao, Wen; Wang, Xu‐Jia; Wu, Rong; Cheng, Zhi

doi:10.3390/atmos12091143

Cited by 4 publications

(6 citation statements)

References 53 publications

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“…Currently, the improvement of the model simulation ability is mainly focused on reducing uncertainty in climate change prediction by establishing some relationship between observations and simulations [67,68]. However, this method is vulnerable to the influence of the quality of observed data, physical processes and even the subjective consciousness of researchers [64].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Lei

Liu

et al. 2023

Remote Sensing

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Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

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Section: Discussionmentioning

confidence: 99%

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Lei

Liu

et al. 2023

Remote Sensing

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“…Firstly, the model data are interpolated to the station by inverse distance weighting (IDW). The control point number of neighbors of IDW is 4, and the weighting function is the inverse power of the distance [41]. Secondly, the interpolated data of the model are bias-corrected based on the station observation data using the quantile mapping approach, which focuses not only on the mean of the distribution but also on correcting the quantiles of the distribution [40][41][42][43].…”

Section: Bias Correction Methodsmentioning

confidence: 99%

“…The control point number of neighbors of IDW is 4, and the weighting function is the inverse power of the distance [41]. Secondly, the interpolated data of the model are bias-corrected based on the station observation data using the quantile mapping approach, which focuses not only on the mean of the distribution but also on correcting the quantiles of the distribution [40][41][42][43]. The cumulative probability distribution function (CDF) of the daily maximum air temperature is first estimated from the simulation data in the calibration period , and then the corresponding percentile values of the model projections are found.…”

Section: Bias Correction Methodsmentioning

confidence: 99%

Higher Heat Stress Increases the Negative Impact on Rice Production in South China: A New Perspective on Agricultural Weather Index Insurance

et al. 2022

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Rice is a major staple food grain for more than half of the world’s population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production.

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“…In another operational climate modelling system, NMME, the warming trends in monthly mean temperature forecasts for both multi-model ensemble means and individual model forecasts were weaker than the trends in observations at all lead times (Krakauer, 2019). More recently, Duan et al (2021) evaluated the trends in spring temperature forecasts produced from the BCC-CSM1.1 (m) seasonal forecasting model operated by BCC. The significant warming trends in the observations were under-estimated by the model forecasts over 1991-2020 across most parts of China.…”

Section: Trend Mismatch Issuementioning

confidence: 99%

“…Jia et al (2014) demonstrated that the climate trend in the atmosphere enhanced seasonal forecast skill in the Northern Hemisphere, particularly across the Eurasian continent. Duan et al (2021) revealed that the skill of spring temperature forecasts over China was improved after trend bias was corrected. In contrast, precipitation skill was found to rarely benefit from the explicit representation of the observed trends in the climate model.…”

Section: Trend Mismatch Issuementioning

confidence: 99%

Post-processing sub-seasonal to seasonal climate forecasts under climate change

Shao¹

2022

Water e-Journal

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For managing the impacts of climate variability and change, climate outlooks on sub-seasonal and seasonal timescales are attracting more interest from climate-sensitive communities, such as water resource management, agriculture, and energy. With a profound knowledge of the sources of climate predictability, modelling techniques are rapidly developed for forecasting future climate conditions. Recent advancements are dynamical global climate models (GCMs), which typically integrate atmosphere, land surface, ocean, and sea ice components to comprehensively simulate earth climate system and output a wide array of climate forecasts. However, GCMs usually suffer from long-standing modelling issues, such as systematic errors and the failure of reproducing the observed trends in seasonal climate forecasts. Statistical post-processing techniques are frequently employed to improve forecast performance. Many commonly used methods are found to be effective at removing biases, maximising forecast skill, and improving forecast reliability in terms of ensemble spread, but they are seldom designed to resolve the trend disparity issue in the post-processed climate forecasts. This issue should not be neglected as global and regional land surface temperature and precipitation have shown discernible temporal trends over recent decades. To address this gap, the overarching objective of this thesis is to develop and demonstrate the merit of a new, trend-aware forecast post-processing method that eliminates the trend disparity between climate forecasts and observations while making the forecasts bias-free, skillful, and reliable.The first part of this research aims to develop a new statistical post-processing method to embed the observed trend into seasonal temperature forecasts. I extend the capability of a calibration method, the Bayesian joint probability (BJP) modelling approach, by introducing a new trend component into the algorithm. The new model (named BJP-t) is applied to calibrate January mean forecasts of daily maximum temperatures from the SEAS5 seasonal forecasting system, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) in three test stations in Australia. In these cases, the BJP-t calibrated forecasts are shown to accurately reproduce the observed trends, and are more skillful, more reliable, and sharper than raw and BJP calibrated forecasts.ii In the BJP-t model, the trend is entirely inferred from the training data. In practice, given limited available periods of retrospective forecasts for model training, these inferred trends are subject to large sampling errors, and may not reflect true underlying trends in the observations. Accordingly, the second part of my thesis further develops the BJP-t model to account for trend uncertainty.The extended trend-aware forecast post-processing method is applied to SEAS5 seasonal mean minimum and maximum temperature forecasts, and the evaluations are upscaled to the Australian continent. After trend-aware post-processing that deals with trend uncertainty...

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Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction

Cited by 4 publications

References 53 publications

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Higher Heat Stress Increases the Negative Impact on Rice Production in South China: A New Perspective on Agricultural Weather Index Insurance

Post-processing sub-seasonal to seasonal climate forecasts under climate change

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