A Markov chain method for weighting climate model ensembles

Kulinich, Max; Fan, Yanan; Penev, Spiridon; Evans, Jason P.; Olson, Roman

doi:10.5194/gmd-14-3539-2021

Cited by 6 publications

(3 citation statements)

References 28 publications

(35 reference statements)

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“…Nevertheless, the constraints associated with simple averages in comprehensively representing all three aspects of our study in certain regions suggest that the straightforward ensemble mean method might not be appropriate for evaluating particular areas. This underscores the potential effectiveness of more intricate multi-model ensemble approaches, such as Bayesian averages [98] or weighted averages [99], as alternatives to simple averages.…”

Section: Discussionmentioning

confidence: 97%

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

Xie,

Guo,

Meng

et al. 2023

Remote Sensing

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Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios.

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Section: Discussionmentioning

confidence: 97%

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

Xie,

Guo,

Meng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, the deteriorated performance of simple averages in capturing all three aspects of our study in some areas suggests that the simple ensemble mean method may not be suitable for assessing some specific regions. This suggests that more sophisticated multi‐model ensemble methods, such as Bayesian averages (Raftery et al, 2005) or weighting averages (Kulinich et al, 2021), may prove to be more helpful than simple averages.…”

Section: Discussionmentioning

confidence: 99%

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

Guo

Sun

Meng

et al. 2023

Intl Journal of Climatology

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Assessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. Here, we provide a comprehensive evaluation of simulated spring SCF from 23 and 20 ESMs participating CMIP5 and CMIP6, against satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) and a long‐term Northern Hemisphere daily 5‐km snow cover extent product (JASMES) over the Northern Hemisphere (NH) and its 13 subregions during the period of 1982–2005 and 2072–2095. Our results show that (a) no model performs consistently better in simulating spring SCF from all aspects (mean annual, long‐term trend and climatological monthly of spring SCF); (b) the mean annual of spring SCF that was simulated by CMIP5 and CMIP6 model simulations was underestimated in most of the NH and overestimated over the Tibetan Plateau and eastern Asia; (c) most of the model simulations showed a stronger reduction trend for mean annual of spring SCF; (d) the climatological monthly variation of spring SCF is reasonably captured by all model simulations, except for overestimation over the TIB and EAS regions, and there is underestimation in other regions; (e) compared to CMIP5, most of the model simulations in CMIP6 exhibit an improved ability to simulate spring SCF across all aspects; (f) we confirm that the multi‐model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. Finally, the spring SCF values predicted by models with better simulation abilities over the NH and its 13 subregions under different scenarios show decreasing trends. Specifically, the highest decreasing trend of spring SCF was found under high emission scenarios (RCP8.5 and SSP5‐8.5).

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“…However, the deteriorated performance of simple averages in capturing all three aspects of our study in some areas suggests that the simple ensemble mean method may not be suitable for assessing some specific regions. This suggests that more sophisticated multimodel ensemble methods, such as Bayesian averages (Raftery et al, 2005) or weighting averages (Kulinich et al, 2021), may prove to be more helpful than simple averages.…”

Section: Assessment Of Overall Model Performancementioning

confidence: 99%

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

Guo

Sun²,

Meng³

et al. 2022

Preprint

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Assessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. In this work, we studied the model performance of 23 and 20 ESMs participating CMIP5 and CMIP6 by comparing satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) over the Northern Hemisphere (NH) and its 13 sub-regions and further evaluating potential model improvement from CMIP5 to CMIP6. We found that the mean annual spring SCF that was simulated by CMIP5 and CMIP6 models was underestimated in most of the NH and overestimated on the Tibetan Plateau and in eastern Asia, and most of the model simulations showed a stronger reduction trend as well as an underestimated monthly climatological SCF. However, compared with those in CMIP5, most of the model simulations in CMIP6 had an improved ability to simulate spring SCF in terms of the annual mean, long-term trend and intra-annual variability. We also confirmed that the multi-model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. The spring SCF values predicted by the CMIP5 and CMIP6 MMEs over the NH and its 13 sub-regions under different scenarios showed decreasing trends. The decreasing spring SCF trends differed under different scenarios, and the SCF under high emissions scenarios (RCP 8.5 and SSP5-8.5) continued to decrease.

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A Markov chain method for weighting climate model ensembles

Cited by 6 publications

References 28 publications

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

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