Improved representation of atmospheric dynamics in <scp>CMIP6</scp> models removes climate sensitivity dependence on Hadley cell climatological extent

De, Bithi; Tselioudis, George; Polvani, Lorenzo M.

doi:10.1002/asl.1073

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…Investigating the Hadley cell (HC) extents in both CMIP6 and CMIP5 GCMs, De et al . (2022) surmised that CMIP6 GCMs simulate large‐scale atmospheric dynamics closer to the observations compared to CMIP5 GCMs.…”

Section: Introductionmentioning

confidence: 92%

“…Interestingly, the study executed by Bock et al (2020) and Xin et al (2020) demonstrated that CMIP6 GCMs were more capable of reproducing the observed mean precipitation and surface temperature pattern compared to CMIP3 and CMIP5 GCMs, on a global scale and continental (East Asia) scale, respectively. Investigating the Hadley cell (HC) extents in both CMIP6 and CMIP5 GCMs, De et al (2022) surmised that CMIP6 GCMs simulate large-scale atmospheric dynamics closer to the observations compared to CMIP5 GCMs.…”

Section: Introductionmentioning

confidence: 99%

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A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Dey

Sahoo

Kumar

et al. 2022

Intl Journal of Climatology

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Multimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basinscale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME-integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (T max ), and minimum temperature (T min ) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME-RF and MME-SVM approach has also been observed while capturing the spatial pattern as IMD-observed with well representation of climate indices for both wet and dry seasons. Future projections with MME-SVM and MME-RF suggested a possible rise in mean annual P in the range of 1.4-15% and 6.8-39% with an increasing trend in temperature (T max , T min ) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Dey

Sahoo

Kumar

et al. 2022

Intl Journal of Climatology

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“…Our main goal is to identify and compare the uncertainties and differences in CMIP6 projections of California rainfall to those of CMIP5, and point to possible underlying mechanisms. Generally, CMIP6 models have gone through multiple improvements, including better representation of clouds and aerosols (Zelinka et al, 2020), and better representation of atmospheric and jet dynamics, especially in the Southern Hemisphere, due to increased horizontal grid resolution (Curtis et al, 2020; De et al, 2022; di Luca et al, 2020), more detailed stratospheric processes (Eyring et al, 2016), and more accurate incorporation of volcanic forcing during the historical period (Zanchettin et al, 2016). In addition, the CMIP6 ensemble has reduced biases of tropical and subtropical precipitation relative to the observations for the historical simulations as compared to CMIP5, mainly due to the inclusion of falling ice radiative effects (FIREs) in some of the models (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Future precipitation changes in California: Comparison of CMIP5 and CMIP6 intermodel spread and its drivers

Petrova,

Tarin‐Carrasco,

Sekulic

et al. 2024

Intl Journal of Climatology

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California is one of the major uncertainty hotspots for climate change, as climate models have historically been split between projecting wetter and drier future conditions over the region. We analysed the future (mid‐century and end‐century) projections of California's winter precipitation changes from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), and studied its respective model agreement in comparison to the previous CMIP5 projections. Over northern California more than two thirds of the models in each ensemble agree on wetter future conditions. However, over southern California both ensembles show highly uncertain precipitation changes, with model projections almost equally divided between wetter or drier conditions. Projected end‐century precipitation changes range from −30% to +70% in CMIP5 and −20% to +80% in CMIP6. The CMIP6 ensemble mean changes are generally wetter and show larger model disagreement compared to CMIP5. Distribution of year‐to‐year precipitation indicates more extremely wet or dry years over southern California in CMIP6 compared to CMIP5, with some models suggesting that the five wettest years account for as much as ~55% of the 20‐year rainfall, and the five driest for as little as ~5%. Dynamically, both ensembles project weakened subsidence over Baja California that is stronger in CMIP6 than in CMIP5, in line with the wetter mean conditions in CMIP6. In the western tropical Pacific we find strengthening of the Hadley circulation in CMIP6 that is not seen in CMIP5, and more El Niño than La Niña conditions in the equatorial Pacific. More CMIP6 models also project an increase in ENSO events compared to CMIP5, and a stronger impact of ENSO on California's precipitation is found in CMIP6 than in CMIP5. These factors also contribute to larger model disagreement and more extremely wet or dry years over southern California in CMIP6.

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“…Important enhancements were made to the CMIP6 Global Climate Models (GCMs) leading to an overall better performance than previous CMIP generations as documented by e.g. Fan et al (2020); Brands (2022a), and a higher global climate sensitivity (Zelinka et al, 2020;De et al, 2022). The IPCC (2023) even states that "robust climate information is increasingly available at regional scales for impact and risk assessments", indicating that GCMs are becoming sufficiently advanced to be used for decision-making on a regional scale.…”

Section: Introductionmentioning

confidence: 99%

Optimizing climate model selection in regional studies using an adaptive weather type based framework: a case study for extreme heat in Belgium

Serras,

Vandelanotte,

Borgers

et al. 2024

Preprint

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Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, underperforming models are excluded. In the last step, metrics are introduced that quantify the climate change signals related to the impact of interest. Using these metrics, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. The developed method offers a framework for selecting periods within climate model datasets while considering the changes in the large-scale circulation patterns and the changes in the climate signal, each step optimized for a specific climate impact driver. This framework provides a comprehensive method for selecting periods from large ensemble GCM simulations based on weather types, ensuring relevant climate projections for subsequent research which can be applied in model ensemble-based research for different climate variables and climate impact drivers.

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Improved representation of atmospheric dynamics in CMIP6 models removes climate sensitivity dependence on Hadley cell climatological extent

Cited by 7 publications

References 28 publications

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Future precipitation changes in California: Comparison of CMIP5 and CMIP6 intermodel spread and its drivers

Optimizing climate model selection in regional studies using an adaptive weather type based framework: a case study for extreme heat in Belgium

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