High‐resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical‐statistical approach

Wang, Xiuquan; Huang, Guohe; Lin, Qianguo; Nie, Xianghui; Liu, Jinliang

doi:10.1002/qj.2421

Cited by 51 publications

(23 citation statements)

References 42 publications

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“…PRECIS was designed with high‐resolution for both operational forecasting and atmospheric research. The regional climate model (RCM) was widely used to produce high‐resolution climate simulations (Feng et al, ; Wang et al, ; Wang, Huang, Lin, et al, ; Xu et al, ). The model's driving GCM was the Hadley Centre Global Environment Model version 2‐Earth Systems (HadGEM2‐ES), which was developed by the Met Office Hadley Centre for the Coupled Model Intercomparison Project Phase 5 (CMIP5; Collins et al, ).…”

Section: Methods and Data Setsmentioning

confidence: 99%

“…The chosen HadGEM2‐ES has the highest resolution among the 11 models (Jiang et al, ; Martin et al, ). In addition, previous studies have demonstrated the ability of HadGEM2‐ES to well represent most variables, especially for surface conditions and atmospheric circulation (Wang et al, ; Wang, Huang, Lin, et al, ). Overall, the HadGEM2‐ES exhibits relatively high skills in providing the lateral boundary conditions to drive the PRECIS model for high‐resolution climate change projections over China.…”

Section: Methods and Data Setsmentioning

confidence: 99%

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Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties

2019

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It has been suggested that global warming impacts on human thermal comfort will cause an increase in the heat stress and a decrease in the cold stress in the future. A recent study has shown elevated increases in human‐perceived equivalent temperature (HPET) by using a single index for summer and winter seasons (Li et al., 2018, https://doi.org/10.1038/s41558-017-0036-2). However, they have not considered multiple indices with combined effects on deriving HPET, which can result in large uncertainties in assessing climate change impacts on HPET and related extremes. Therefore, we develop a new framework with high‐resolution projections and an ensemble of 10 indices to quantify the impacts of climate change on HPET and related perceived extremes as well as to address uncertainties in both empirical indices and emission scenarios over China. Our findings reveal that different combinations of climatic variables can lead to two opposite conclusions for both normal and extreme conditions. For example, by using indices only considering the combined effect of temperature and relative humidity, China is projected to have an elevated increase in the HPET and in the frequency of high‐temperature extremes. By taking into account wind speed, the country expects to have the HPET even lower than the surface air temperature and an increase in the frequency of low‐temperature extremes. In addition, the resulting range of HPET due to uncertainty in indices is greater than the uncertainty range derived from different emission scenarios for the entire country. Therefore, it is necessary to conduct a comprehensive assessment that explicitly addresses uncertainties in the HPET in order to improve the robustness and reliability of assessing climate change impacts on human‐perceived temperature extremes.

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Section: Methods and Data Setsmentioning

confidence: 99%

Section: Methods and Data Setsmentioning

confidence: 99%

Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties

2019

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“…Changes in climate are not expected to be uniform across the globe due to the regional variations in topography, land cover/land use, economic development and so on (Hamilton and Keim, 2009;Paeth et al, 2009;Jordan et al, 2014;Wang et al, 2014a), implying that the subsequent consequences caused by climatic changes will not be equally distributed. Investigating the regional effects of global warming is thus of great concern for decision makers and resources managers to help develop scientifically informed policies and strategies against the changing climate (Alexander and Arblaster, 2009;Liepert and Previdi, 2009;Stott et al, 2010;Wang et al, 2013;Wang et al, 2014c;Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Observed regional climatic changes over Ontario, Canada, in response to global warming

Wang

Huang

Liu

2015

Meteorological Applications

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Human-induced climatic changes are not expected to be uniform across the globe due to the regional variations in topography, land cover/land use, economic development and so on. Investigating the regional effects of climate change is thus of great concern for decision makers and resource managers to develop scientifically informed policies and strategies against the changing climate. In the present study, regional climatology over Ontario, Canada, and its temporal trends in the past century are analysed based on the historical observations at the gauged stations, aiming to investigate how the local climate has been affected by human-induced global warming. The analysis shows that the annual mean temperature over Ontario varies mainly between 1.6 and 7 ∘ C with a median of 3.8 ∘ C, while its annual total precipitation usually ranges between 836 and 1004 mm with a median of 896 mm. Further correlation analysis suggests that no or negligible correlations between total precipitation and mean temperature are found at the vast majority of stations (accounting for over 80% of the total), except for summer when significant negative correlations (with a correlation co-efficient varying between −0.7 and −0.2) are reported at over 54% stations. As for the temporal trends, significant warming trends are detected throughout the province and the overall trend in annual mean temperature varies largely between 0.01 and 0.02 ∘ C year -1 . Increasing trends in annual rainfall (by 1-3 mm year -1 ) and total precipitation (by 1-4 mm year -1 ) are detected at the vast majority of gauged stations, but no significant trends in annual snowfall are identified at most of the stations. The results of this study can help better understand the regional climate of Ontario and provide important references for developing future climate scenarios that can be used for impact studies.

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“…By contrast, RCMs are developed using the same laws of physics as described in GCMs to account for the sub-GCM grid scale processes with more regional details (such as mountain ranges, coastal zones, inland waters, and details of soil properties) in a physically-based way (Feser et al 2011). By nesting RCMs into GCMs, dynamical downscaling can be used to develop the improved simulation of the local climate system with provision of a large number of climate variables at fine spatial scales, and is thus increasingly attracting the attention of climate impact researchers in recent years (e.g., Caldwell et al 2009;Castro et al 2005;Chan et al 2014;Fujihara et al 2008;Gao et al 2013;Gao and Giorgi 2008;Kanamitsu et al 2010;Nobre et al 2001;Rockel et al 2008;Sánchez et al 2004;Wang et al 2014b). …”

mentioning

confidence: 99%

“…With the advancement of numerical modeling and simulation, global climate models (GCMs) has been widely used to project future climate under different emission scenarios (Nakicenovic 2000;Van Vuuren et al 2011). Because the spatial resolution of GCM outputs is typically too coarse for regional impact studies, further downscaling is required for deriving regional climate details from the coarse-resolution outputs (Giorgi et al 1993a, b;Maurer et al 2007;Wang et al 2014b). Downscaling techniques are usually classified into two categories: (1) dynamical downscaling through nesting fine-resolution regional climate models (RCMs) into GCMs, and (2) statistical downscaling involving the development of quantitative relationships between large-scale atmospheric variables and local weather variables such as temperature and precipitation (Hewitson and Crane 1996;Wilby and Wigley 1997).…”

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confidence: 99%

Projected increases in near-surface air temperature over Ontario, Canada: a regional climate modeling approach

2014

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i.e., HadCM3Q0, Q3, Q10, Q13, and Q15). The ensemble simulations are then synthesized through a Bayesian hierarchical model to develop probabilistic projections of future temperature outcomes with consideration of some uncertain parameters involved in the regional climate modeling process. The results suggest that there would be a consistent increasing trend in the near-surface air temperature with time periods from 2030s to 2080s. The most likely mean temperature in next few decades (i.e., 2030s) would be [−2, 2] °C in northern Ontario, [2, 6] °C in the middle, and [6, 12] °C in the south, afterwards the mean temperature is likely to keep rising by ~ 2 °C per 30-years period. The continuous warming across the Province would drive the lowest mean temperature up to 2 °C in the north and the highest mean temperature up to 16 °C in the south. In addition, the spread of the most likely ranges of future outcomes shows a consistent widening trend from 2030s to 2080s, implying that long-term climate change is more difficult to predict than near-term change because many more uncertain or unknown factors may continue to emerge during the long-term simulation.

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High‐resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical‐statistical approach

Cited by 51 publications

References 42 publications

Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties

Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties

Observed regional climatic changes over Ontario, Canada, in response to global warming

Projected increases in near-surface air temperature over Ontario, Canada: a regional climate modeling approach

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