The relative performance of Australian CMIP5 models based on rainfall and ENSO metrics

Smith, Ian; Syktus, Jozef; Rotstayn, Leon; Jeffrey, Stephen

doi:10.22499/2.6301.013

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…The correlation coefficients between the seasonal ERA5 NINO34* and rainfall are given in Table 2. The strongest link is in SON, with r ¼ −0.61 (with a variation through the year like that of the monthly results from Smith et al, 2013). However, only in MAM does the regression line provide a substantial percentage (55%) of the (relatively small) 2019 pr anomaly (as seen in Figure S2d).…”

Section: Standard Driver Indices 331 Nino34mentioning

confidence: 80%

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Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

Watterson

2020

JGR Atmospheres

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The 2019 and 2018–2019 periods had record low All‐Australia rainfall in both observations and the ECMWF's Reanalysis 5, or ERA5, data set over 1979–2019. An analysis of the relationships between interannual variability of rainfall and atmospheric circulation, vertically integrated moisture flux, and temperature anomalies using ERA5 alone is undertaken. Both standard driver indices and those that combine the Pacific and Indian Ocean influences are used. Regression fields for low annual Australian rainfall show a widespread negative rainfall anomaly extending into the east Indian Ocean or IND region and a positive anomaly in the western Pacific PAC region. A moisture flux anomaly takes moisture eastwards toward PAC. This pattern largely persists in the four seasons. In March–May 2019 it can be partially linked to a positive Niño 3.4 anomaly and in June–August to a positive Indian Ocean Dipole. For the annual case, the detrended Niño 4 index explains 24% of the 2019 ERA5 Australian rainfall deficit. This rises to 38% for the 2018–2019 deficit (46% for observations), for the Pacific‐Indian Dipole index that combines the PAC and IND regions, whose sea surface temperature anomalies have opposite effects. The correlation of the index with 2‐year rainfall is −0.7. There is much variability, in the Australian monsoon rainfall especially, that is not linked to simple indices, although rainfall is well matched with moisture flux convergence. This extends to the equatorial zone where the climate of the Maritime Continent can be better examined with ERA5's 0.25° data.

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Section: Standard Driver Indices 331 Nino34mentioning

confidence: 80%

“…An ENSO index previously linked to All-Australia rainfall by Smith et al (2013), NINO34, is based on the temperature for the central Pacific region shown in Figure 1. For the purpose of assessing links in ERA5, this and other indices are determined directly from the ERA5 fields.…”

Section: Standard Driver Indices 331 Nino34mentioning

confidence: 99%

Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

Watterson

2020

JGR Atmospheres

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“…The CAPTIVATE tests are then applied, targeting features of the mean state climatology (CLIM, retaining the short names used extensively in the project), variability (VAR) and teleconnections (TELE) that are important to Australian climate. A further assessment of the CMIP5 models by Smith et al (2013) can be found in this issue.…”

Section: Introductionmentioning

confidence: 99%

A skill score based evaluation of simulated Australian climate

Watterson¹,

Hirst²,

Rotstayn³

2013

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The three Australian submissions to CMIP5, based on two versions of the AC-CESS coupled climate model and the upgraded CSIRO model Mk3.6, are evaluated using skill scores for both global fields and for features of Australian climate. Global means of surface air temperature and precipitation are similar to those from observational datasets, except for a cool bias in Mk3.6. The agreement of the climatological seasonal mean fields for these and eleven other quantities, as measured by the M score, is comparable to the Australian CMIP3 models, in the case of Mk3.6, but mostly improved on by both ACCESS1.0 and ACCESS1.3. The AC-CESS mean sea-level pressure and winds are notably better. An overall skill score, calculated for both global and Australian land, shows that the ACCESS models are among the best performing of 25 CMIP5 models. ACCESS1.0 improves slightly on the U.K. Met Office submissions. A suite of tests developed for the CAPTIVATE project is applied, and again ACCESS1.0 matches the U.K. Met Office reference model. ACCESS1.3 has improved cloud cover and a better link between equatorial sea surface temperatures (SSTs), using the Pacific-Indian Dipole index, and Australian rainfall. Mk3.6 has excessive variability in the SST index. In all, both versions of ACCESS are highly successful, while the ensembles of simulations of Mk3.6 make it also a very worthwhile submission to CMIP5.

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“…We leave out the discussion of the simulated surface climate, as this will be discussed elsewhere. For example, the global surface climate is described in Bi et al (2013), Dix et al (2013), Smith et al (2013) and , the land-surface climate in Kowalczyk et al (2013), while the interannual variability associated with the El Niño−Southern Oscillation (ENSO) events is documented in Rashid et al (2013). Our main focus here is to document the fidelity of ACCESS-CM in simulating the key atmospheric circulation systems.…”

Section: Model Experiments and Validation Datamentioning

confidence: 99%

Atmospheric circulation features in the ACCESS model simulations for CMIP5: historical simulation and future projections

Rashid¹,

Hirst²

2013

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We evaluate the performance of two versions of the ACCESS model (1.0 and 1.3) in simulating both the historical (1979−2008) and projected (2071−2100) atmospheric circulations during, principally, the austral winter under two CMIP5 emission scenarios (RCP4.5 and RCP8.5). The model biases are estimated relative to two recent reanalysis datasets, while the projected circulation changes are assessed against the simulated historical circulations. Overall, both ACCESS models display model biases comparable in magnitude to other CMIP5 model biases. The most significant biases include the upper-tropospheric cold and polar warm biases, a westerly wind bias in the tropical upper troposphere and easterly wind biases in the southern and northern mid-latitudes, a narrower than observed Hadley circulation cell, a stronger Walker circulation cell, and drying (moistening) near the outer edges of the ascending (descending) branch of the Hadley cell. The projected circulation changes for the late 21st century in ACCESS simulations are largely similar to those found in the previous generation climate models including upper-tropospheric and polar warmings, a stronger subtropical jet, a poleward shifted mid-latitude jet, a deeper Hadley cell with its descending branch expanding poleward, and a weakened Walker circulation. However, our analysis also reveals a moderate intensification of the projected Hadley cell in the RCP4.5, but not the RCP8.5, simulations. Most of the projected changes are similar in ACCESS1.0 and ACCESS1.3, except that the Walker circulation change in ACCESS1.3 is essentially an eastward shift of its ascending branch to the east of the dateline, while that in ACCESS1.0 is an in-place weakening of the circulation.

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The relative performance of Australian CMIP5 models based on rainfall and ENSO metrics

Cited by 9 publications

References 26 publications

Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

A skill score based evaluation of simulated Australian climate

Atmospheric circulation features in the ACCESS model simulations for CMIP5: historical simulation and future projections

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