Effects of Australian Summer Monsoon on Sea Surface Temperature Diurnal Variation Over the Australian North‐Western Shelf

Wang, Xiaohua; Zhang, Haifeng

doi:10.1002/2017gl075008

Cited by 7 publications

(7 citation statements)

References 47 publications

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“…Long-term trends of Tq99th, Tmean, LE, H, EF, and Tq99th relative to Tmean are quantified using the linear estimation method. The significance of the trend is tested using the Mann-Kendall (MK) method, a nonparametric diagnosis method (Wang et al 2017). The point-computed trends in various variables are spatially interpolated to 0.58 3 0.58 latitude/ longitude resolution using the kriging spatial interpolation method.…”

Section: Methodsmentioning

confidence: 99%

“…Although southern Australia also exhibits a faster rate of increase in hot extreme than the average, the corresponding change in EF is relatively weak. Therefore, recently reported hot weather or heatwave events in Australia may be associated with atmospheric circulation (Wang and Zhang 2017), rather than induced or dominated by drought event and local land-atmosphere interactions.…”

Section: B Trends In Tq99th Relative To Tmeanmentioning

confidence: 99%

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Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

Ren

Gentine

et al. 2022

Journal of Hydrometeorology

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Land-atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land-atmosphere coupling. One of the linkages in land-atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, Northeast Asia, and Southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hotspot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidences for the extreme climate change accelerated by surface energy partitioning.

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Section: Methodsmentioning

confidence: 99%

Section: B Trends In Tq99th Relative To Tmeanmentioning

confidence: 99%

Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

Ren

Gentine

et al. 2022

Journal of Hydrometeorology

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“…Wang and Zhang () found that these variables have good quality. ACCESS‐R SSI and winds have a 0.85 and 0.88 correlation coefficient with ECMWF SSI and CCMP winds, respectively (Wang & Zhang, ).…”

Section: Models Data and Methodsmentioning

confidence: 99%

“…It can predict a series of surface parameters, including wind speed, wind stress, shortwave and longwave radiative fluxes, and latent and sensible heat fluxes (Puri et al, ). The ACCESS‐R wind speed and daily maximum SSI have been compared with the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis SSI data and Cross‐Calibrated Multiplatform (CCMP) sea surface wind data, respectively, over the Australian North‐West Shelf from 2010 to 2014 (Wang & Zhang, ). Wang and Zhang () found that these variables have good quality.…”

Section: Models Data and Methodsmentioning

confidence: 99%

“…The ACCESS‐R wind speed and daily maximum SSI have been compared with the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis SSI data and Cross‐Calibrated Multiplatform (CCMP) sea surface wind data, respectively, over the Australian North‐West Shelf from 2010 to 2014 (Wang & Zhang, ). Wang and Zhang () found that these variables have good quality. ACCESS‐R SSI and winds have a 0.85 and 0.88 correlation coefficient with ECMWF SSI and CCMP winds, respectively (Wang & Zhang, ).…”

Section: Models Data and Methodsmentioning

confidence: 99%

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Comparison of SST Diurnal Variation Models Over the Tropical Warm Pool Region

et al. 2018

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Four sea surface temperature (SST) diurnal variation (DV) models have been compared against Multifunctional Transport Satellite‐1R (MTSAT‐1R) SST measurements over the Tropical Warm Pool (TWP) region (90°E–170°E, 25°S–15°N) for 4 months from January to April 2010. The four models include one empirical model formulated by Chelle Gentemann (hereafter CG03), one physical model proposed by Zeng and Beljaars in 2005 (ZB05) and its updated version (ZB + T), and one air‐sea coupled model (the Met Office Unified Model Global Coupled configuration 2, GC2) with ZB05 warm layer scheme added on top of the standard configuration. The sensitivity of the v3 MTSAT‐1R data to the “true” changes in SST is first investigated using drifting buoys and is estimated to be 0.60 ± 0.05. This being significantly different from 1, the models are validated against MTSAT‐1R data and the same data scaled by the inverse of the sensitivity (representing an estimate of the true variability). Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features. Specifically, CG03 and ZB + T underestimate strong (>2 K) DV events' amplitudes especially if we assume that sensitivity‐scaled MTSAT‐1R variability is most realistic. ZB05 can effectively capture the DV cycles under most DV and wind conditions, as well as the DV spatial distribution. GC2 tends to overestimate small‐moderate (< 2 K) DV events but can reasonably predict large DV events. One to three hour lags in warming start and peak times are found in GC2.

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Impact of high‐resolution ocean–atmosphere coupling on fog formation over the North Sea

Fallmann

Lewis

Sanchez

et al. 2019

Quart J Royal Meteoro Soc

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Sea‐surface temperature (SST) is a key driver for various interactions and feedbacks between components of the Earth System and can control local weather and climate. The formation of marine fog, for example, can be sensitive to small changes in SST at a scale of a few kilometres. As a contribution to understanding processes at the interface between air and sea, this article discusses results from a state‐of‐the‐art fully coupled regional atmosphere–land–ocean–wave prediction system for the UK at km scale. This study focuses on the impact of the changes in surface forcing resulting from coupling SST in the marine boundary layer and formation of summertime coastal fog over the North Sea. A study from July 2013 provided a good case to evaluate the role of SST in fog evolution. The benefit of an evolving SST in the coupled simulation is shown in capturing a warming trend in observed SST over the five‐day case study period, with a root‐mean‐square error (RMSE) against in situ observations of 1.1 K. In contrast, in uncoupled atmosphere‐only simulations, the initial‐condition SST is persisted for the duration of the case, as is more typical in current operational numerical weather prediction (NWP). In the uncoupled simulations, a cold bias develops over the modelling period and the RMSE against observed SST is 2.4 K. The impact of coupling is shown to propagate into the overlying marine boundary layer and therefore affect the formation of coastal fog. Increased heat flux from a relatively warmer sea surface in the coupled simulations led to near‐surface atmospheric instability, hampering stratus lowering and destroying the fog‐promoting inversion layer. This significantly reduced fog fractions in selected regions. The value of model coupling was assessed by comparing coupled and uncoupled simulations initialized at different times ahead of fog development.

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Effects of Australian Summer Monsoon on Sea Surface Temperature Diurnal Variation Over the Australian North‐Western Shelf

Cited by 7 publications

References 47 publications

Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

Comparison of SST Diurnal Variation Models Over the Tropical Warm Pool Region

Impact of high‐resolution ocean–atmosphere coupling on fog formation over the North Sea

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