NUIST ESM v3 Data Submission to CMIP6

Cao, Jian; Ma, Libin; Liu, Fei; Chai, Jing; Zhao, Haikun; He, Qiong; Wang, Bo; Bao, Yan; Li, Juan; Yang, Young‐Min; Deng, Hua; Wang, Bin

doi:10.1007/s00376-020-0173-9

“…Compared with the CESM low-warming simulations, the additional scenario of the transient 2 °C can also be used to assess the difference and similarity of climate responses under transient and stabilized global warming. Besides, the NESM3 is configured with a horizontal resolution of 1.9° by 1.9° in the atmosphere and 1° by 1° in the ocean 37 . The used variables include the five upper air variables (i.e., air temperature, geopotential height, specific humidity, zonal wind, and meridional wind) and six surface variables (i.e., surface pressure, sea level pressure, sea surface temperature, soil moisture, soil temperature, and 2 m temperature).…”

Section: Methodsmentioning

confidence: 99%

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Zhang,

Han,

Xu

et al. 2024

Sci Data

0

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Dynamical downscaling is vital for generating finer-scale climate projections. Recently, a set of simulations under four types of 1.5/2 °C global warming scenarios are available with Nanjing University of Information Science and Technology Earth System Model (NESM). However, NESM3’s bias in large-scale driving variables would degrade downscaled simulations. We corrected NESM3 bias in terms of climate mean and inter-annual variance against ERA5 using a novel bias correction method and then produced a set of bias-corrected datasets for dynamical downscaling. The bias-corrected NESM3 spans the historical period for 1979–2014 and four future scenarios (i.e., 1.5 °C overshoot for 2070–2100, stabilized 1.5/2 °C for 2070–2100, and transient 2 °C for 2031–2061) with 1.25° × 1.25° horizontal resolution at six-hourly intervals. Our evaluation suggests that bias-corrected NESM3 outperforms the original NESM3 in the climatological mean of seasonal mean and variability, as well as climate extreme events during the historical period. This bias-corrected dataset is expected to generate more reliable projections for regional climate and environment under 1.5/2 °C global warming.

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“…Apart from observed data, the simulations of 20 the Coupled Model Intercomparison Project phase 6 (CMIP6) models, with data available at the time of analysis, were used in the analyses of the IPWP expansion in future climate conditions, including ACCESS-CM2 44 , CAMS-CSM1-0 45 , CAS-ESM2-0 46 , CESM2-WACCM 47 , CMCC-CM2-SR5 48 , CMCC-ESM2 49 , EC-Earth3 50 , EC-Earth3-Veg 50 , FGOALS-f3-L 51 , FGOALS-g3 52 , GFDL-ESM4 53 , INM-CM4-8 54 , INM-CM5-0 55 , IPSL-CM6A-LR 56 , MIROC6 57 , MPI-ESM1-2-HR 58 , MPI-ESM1-2-LR 58 , MRI-ESM2-0 59 , NESM3 60 , and TaiESM1 61 (Table 1). The choices of models depend on their availability of both SST and precipitation data of at the time of analysis.…”

Section: Datamentioning

confidence: 99%

Differential expansion speeds of Indo-Pacific warm pool and deep convection favoring pool under greenhouse warming

Leung¹,

Zhang

²

,

Gan

³

et al. 2022

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The Indo-Pacific warm pool (IPWP), which affects the global climate system through supporting tropical convection, has been reported to expand significantly under greenhouse warming. Although early research revealed that the sea surface temperature (SST) threshold for deep convection (σconv) increases with global warming, many latest relevant works were still conducted based on the traditional IPWP definition (e.g., static SST = 28 °C threshold, and is referred to as the oceanic warm pool, OWP28). Here, we claim that the OWP28 expansion differs from the deep convection favoring pool (DCFP) area change and may not reflect the direct impacts of Indo-Pacific warming on the climate system. Results show that, because of the long-term increase in σconv, the DCFP expands at a rate 2.6 times slower than the OWP28 from 1979 to 2020. The difference reaches 12–27 times from 2015–2100 under different emission scenarios, based on CMIP6 model simulations. While the OWP28 expands to the eastern Pacific, the DCFP will remain within the Indian Ocean and western Pacific Ocean regardless of emission levels. This study emphasizes the necessity of considering the response of the relationship between deep convection and SST to climate change when studying the long-term variability of the IPWP.

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“…Apart from observed data, the simulations of 20 the Coupled Model Intercomparison Project phase 6 (CMIP6) models, with data available at the time of analysis, were used in the analyses of the IPWP expansion in future climate conditions, including ACCESS-CM2 44 , CAMS-CSM1-0 45 , CAS-ESM2-0 46 , CESM2-WACCM 47 , CMCC-CM2-SR5 48 , CMCC-ESM2 49 , EC-Earth3 50 , EC-Earth3-Veg 50 , FGOALS-f3-L 51 , FGOALS-g3 52 , GFDL-ESM4 53 , INM-CM4-8 54 , INM-CM5-0 55 , IPSL-CM6A-LR 56 , MIROC6 57 , MPI-ESM1-2-HR 58 , MPI-ESM1-2-LR 58 , MRI-ESM2-0 59 , NESM3 60 , and TaiESM1 61 (Table 1). The choices of models depend on their availability of both SST and precipitation data of at the time of analysis.…”

Section: Datamentioning

confidence: 99%

On the differential expansion speeds of Indo-Pacific warm pool and deep convection favoring pool under greenhouse warming

Leung

¹

,

Zhang

²

,

Gan

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

The Indo-Pacific warm pool (IPWP), which affects the global climate system through supporting tropical convection, has been reported to expand significantly under greenhouse warming. Although early research revealed that the sea surface temperature (SST) threshold for deep convection (σSST_conv) increases with global warming, many latest works on the IPWP expansion were still conducted based on the traditional IPWP definition (e.g., static SST=28°C threshold, and is referred to as the oceanic warm pool, OWPSST=28). Here, using the latest observations and climate model simulations, by considering the long-term variability σSST_conv, we define the deep convection favoring pool (DCFP) over the Indo-Pacific Ocean, which reflects the region where with local SST favors occurrence of deep convections, under past and future climate change. Result based on observations from 1979–2020 shows that because of the increase of σSST_conv under climate change, the DCFP expands at a rate 2.6 times slower than the OWPSST=28, and is more consistent with the change in deep convection area. In the future climate (2015–2100), CMIP6 models projected that the σSST_conv¬ will further increase, with a larger trend under higher emission scenarios. As a result, the increasing trend of the DCFP area is 12–27 times smaller than that of OWPSST=28 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. While the OWPSST=28 expands to the eastern Pacific, the DCFP will remain within the Indian Ocean and western Pacific Ocean regardless of the emission levels. The consistency between the area changes of the DCFP and deep convection area implies that the IPWP expansion estimated based on the fixed σSST_conv may not reflect the direct impacts of Indo-Pacific warming on the climate system under greenhouse warming. This study emphasizes the necessity of considering the response of the relationship between deep convection and SST to climate change when studying the long-term variability of the IPWP, and claims that the DCFP serves as a more reasonable reference of IPWP expansion.

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NUIST ESM v3 Data Submission to CMIP6

Cited by 12 publications

References 52 publications

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Differential expansion speeds of Indo-Pacific warm pool and deep convection favoring pool under greenhouse warming

On the differential expansion speeds of Indo-Pacific warm pool and deep convection favoring pool under greenhouse warming

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