An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

Bannister, Daniel; Herzog, Michael; Graf, Hans‐F.; Hosking, J. R. M.; Short, C. Alan

doi:10.1175/jcli-d-16-0536.1

Cited by 56 publications

(32 citation statements)

References 66 publications

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“…Similar results also been shown by Huang et al [35] that the CMIP5 GCMs underestimated the annual mean surface air temperature relative to the Climate Research Unit temperature data (CRU TS 3.21) in Mekong River Basin. Other study in monsoon region of Sichuan Basin conducted by Bannister et al [17] showed that mean temperature was underestimated by CMIP5 GCMs, especially during the winter, with bias exceeding −3 • C. Furthermore, another study in the Qinghai-Tibetan Plateau also showed that CMIP5 GCMs underestimated annual and seasonal temperatures, with bias at −2.3 • C for the annual mean, and larger cold biases for autumn and winter [47]. However, a study by Miao et al [5] showed that most of the CMIP5 GCMs overestimated the annual mean surface air temperature in Northern Eurasia, especially during the winter.…”

Section: Discussionmentioning

confidence: 96%

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Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

et al. 2019

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In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based method was used to rank the performance of the GCMs over the LMB. Two methods of multi-model ensemble (MME), sub-ensemble from the top 25% ranked GCMs and full ensemble from the entire GCMs, were calculated using arithmetic mean (AM) method and downscaled using the Delta method to project future temperature change during two future time periods, the near future (2006–2049) and the far future (2050–2093), under representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5 scenarios) over the LMB. The improved score-based method combining multiple criteria showed a robust assessment of the GCMs performance over the LMB, which can provide good information for projecting future temperature change. The results showed a significant increase in temperature over the LMB under the two ensembles. However, there were differences in the magnitudes of the future temperature increase between the two ensemble methods, with a higher mean annual temperature increase from full ensemble and sub-ensemble at 1.26 °C (1.09 °C), 1.90 °C (1.70 °C), and 2.97 °C (2.78 °C) during 2050–2093 under the RCP2.6, RCP4.5, and RCP8.5 scenarios compared to the values at 0.93 °C (0.87 °C), 0.99 °C (0.95 °C), and 1.09 °C (1.06 °C) during 2006–2049, respectively, relative to the reference time period of 1961–2004. In the future (2006–2093), the temperature is likely to increase at 0.04 °C, 0.16 °C, and 0.37 °C decade-1 under the RCP2.6, RCP4.5, and RCP8.5 scenarios by the sub-ensemble, while a higher temperature increase at 0.05 °C, 0.17 °C, and 0.39 °C was found by the full ensemble over the LMB, relative to the reference time period of 1961–2004. On the whole, the higher warming mainly occurred in the northern and central areas over the LMB, while the lower warming mainly occurred in the southeast and the southwest, especially under the RCP4.5 and RCP8.5 scenarios, with the warming increased with increasing RCP for both ensembles. Moreover, in order to reduce the uncertainty of temperature projection in further studies in the LMB, multiple methods of GCMs ensemble should be considered and compared.

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Section: Discussionmentioning

confidence: 96%

“…Moreover, Hawkins and Sutton [16] suggested that model uncertainty was more important than internal variability for decadal time scales and regional spatial scales (~2000 km). Therefore, a GCM that can simulate observed temperature reasonably well should be selected before a climate change projection is made [5,17,18].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

et al. 2019

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“…Considering that the global climate models are useful tools for investigating climate change, we utilized the CMIP5 models to reveal the key role of DMO in SAT. Note that multi-model ensemble strategy is traditionally used to exploit the diversity of skilful predictions by different models (Xu and Xu, 2012;Zhang, 2012), but this method does not consider the relative strength and weakness of each model as an ensemble invariably hides the substantial variation among individual models (Bannister et al, 2017). In addition, the internal variability of different models might be offset or strengthened through ensemble mean.…”

Section: Resultsmentioning

confidence: 99%

“…The CMIP5 experiments (Taylor et al, 2011) include simulations of 20th-century climate (referred as historical experiments) and of 21st-century climate under new greenhouse gas emission scenarios (referred as representative concentration pathways [RCPs]; Meinshausen et al, 2011). The RCPs represent different emission pathways according to assumed policy decisions, which would influence the future emissions of greenhouse gases, aerosols, ozone, and land-use changes (Bannister et al, 2017). In this study, the outputs for temperature from the historical simulations of 19 models were selected (Table 1), together with their RCP4.5 outputs runs from 2005 to 2015, to compare with the observational data set.…”

Section: Data Sourcesmentioning

confidence: 99%

The key role of decadal modulated oscillation in recent cold phase

Luo

Guan

Xie

et al. 2019

Intl Journal of Climatology

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Global temperature change is strongly affected by internal climate variability (ICV). The temporal change of the ICV on the decadal to multi‐decadal scales is referred as the decadal modulated oscillation (DMO) that plays a dominated role in the occurrence of enhanced warming and warming hiatus. However, investigation on the DMO in modern historical period has received limited attention. In this study, the ensemble empirical mode decomposition (EEMD) method was applied to the surface air temperature (SAT) during the boreal cold season to extract the DMO signal in the past century. Two most sensitive areas of DMO trend over northern Eurasia and northwestern North America were identified and used to build a time series of regionally enhanced DMO. It showed an obvious decadal periodic oscillation at 11–23 years and exhibited increasing amplitude. In addition, regression analysis using Niño3.4, Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal Oscillation (AMO), and Arctic Oscillation (AO) revealed a major role of the AO in DMO over the mid‐to‐high latitudes in the Northern Hemisphere (NH). However, such strong oscillation signal has not been detected in most of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the extracted regionally enhanced DMO are capable of improving the predictability of SAT over the mid‐to‐high latitudes in the NH.

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“…These studies all demonstrate that understanding and gauging the uncertainty of GCMs is especially crucial for future projections of climate change, which is a widely discussed and acknowledged subject (Hawkins & Sutton, 2011;Najafi et al, 2011). Therefore, it is imperative to continue assessments of GCM performance, in order to provide reliable projections on future climate patterns (Bannister et al, 2017;Miao et al, 2014).…”

Section: Introductionmentioning

confidence: 96%

Assessment of CMIP5 GCM Simulation Performance for Temperature Projection in the Tibetan Plateau

Kang

Ruan

Yang

et al. 2019

Earth and Space Science

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The performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) in temperature simulations in the Tibetan Plateau (TP) was comprehensively assessed by an improved score-based and multiple-criteria method using data collected from 1961 to 2005. Future temperatures were also simulated based on a multimodel ensemble coupled with the Delta downscaling method for near-term (2006-2050) and long-term (2051-2095) projections under Representative Concentration Pathways (RCP, scenarios RCP4.5 and RCP8.5). Our results demonstrated that all the GCMs evaluated in our study could capture the seasonal temperature patterns. However, most GCMs tended to underestimate temperatures by an average of −2.0°C. All the GCMs could effectively simulate temporal distribution, with a mean correlation coefficient of 0.997. However, they did not perform well in reproducing spatial distribution. Different assessment criteria lead to inconsistent results; however, the improved rank score method with multiple criteria provided a robust assessment of GCMs performance. MPI-ESM-LR, CMCC-CMS, and GFDL-ESM 2M showed a better temperature simulation performance compared to the other GCMs that we have assessed. Topographic correction could effectively enhance spatial distribution simulation; however, this increased temperature underestimation. Future temperatures were projected to increase by 1.4°C and 1.6°C in near-term, and by 2.4°C and 4.0°C in long-term under RCP4.5 and RCP8.5 scenarios, respectively. High temperatures mainly occurring in the southeastern end of the Himalayas, as well as the northern and southeastern margins of the TP. The results are expected to provide valuable information on climate change and its impact on hydrology, ecology, and socioeconomics of the TP.

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An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

Cited by 56 publications

References 66 publications

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

The key role of decadal modulated oscillation in recent cold phase

Assessment of CMIP5 GCM Simulation Performance for Temperature Projection in the Tibetan Plateau

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