Consistent large-scale response of hourly extreme precipitation to temperature variability

2021 ◽  
Author(s):  
Haider Ali ◽  
Hayley Fowler ◽  
Geert Lenderink
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Surface Air Temperature ◽  
Absolute Humidity ◽  
Tropical Regions ◽  
Local Scaling ◽  
Hourly Precipitation ◽  
Dewpoint Temperature ◽  
Scale Response ◽  
Temperature Scaling

<p>Hourly precipitation extremes can intensify with higher temperatures at higher rates than theoretically expected from thermodynamic increases explained by the Clausius-Clapeyron (CC) relationship (~6.5%/K), but local scaling with surface air temperature is highly variable. Here, we use daily dewpoint temperature, a direct proxy of absolute humidity, as the scaling variable instead of surface air temperature. Using a global dataset of over 7000 hourly precipitation gauges, we estimate the at-gauge local scaling across six macro-regions; this ranges from CC to 2xCC for more than 60% of gauges. We find positive scaling in subtropical and tropical regions in contrast to previous work. Moreover, regional scaling rates show surprisingly universal behaviour at around CC, with higher scaling rates in Europe. Our results show a much greater consistency of scaling across the globe than previous work, usually at or above the CC rate, suggesting the relevance of dewpoint temperature scaling to understand future changes.   </p>

scholarly journals Urbanization effects on surface air temperature trends in Thailand during 1970-2019

2020 ◽  
Vol 26 (5) ◽  
pp. 200378-0
Author(s):  
Boonlue Kachenchart ◽  
Chaiyanan Kamlangkla ◽  
Nattapong Puttanapong ◽  
Atsamon Limsakul
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Urban Expansion ◽  
Great Influence ◽  
Surface Air Temperature ◽  
Urban Setting ◽  
Temperature Trends ◽  
Urban Warming ◽  
Wide Range ◽  
Increasing Trend

Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 <sup>o</sup>C per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thailand. The increasing trend of annual Tmean for Thailand as a whole after adjusting urban-warming bias is brought down to the same rate as the annual global mean temperature trend, reflecting a national baseline signal driven by large-scale anthropogenic-induced climate change. Our results provide a scientific reference for policy makers and urban planners to mitigate substantial fraction of the UHI warming.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

Trend of Surface Air Temperature in Eastern China and Associated Large-Scale Climate Variability over the Last 100 Years

2014 ◽  
Vol 27 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Ping Zhao ◽  
Phil Jones ◽  
Lijuan Cao ◽  
Zhongwei Yan ◽  
Shuyao Zha ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Tropical Indian Ocean ◽  
Population Data ◽  
Warming Trend ◽  
Climate Indices ◽  
The Past ◽  
Linear Trends

Abstract Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

scholarly journals A global empirical system for probabilistic seasonal climate prediction

2015 ◽  
Vol 8 (12) ◽  
pp. 3947-3973 ◽  
Author(s):  
J. M. Eden ◽  
G. J. van Oldenborgh ◽  
E. Hawkins ◽  
E. B. Suckling
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Climate Change Signal ◽  
Seasonal Forecasts ◽  
Physical Relationship ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

scholarly journals Comparison of Machine-Learning Algorithms for Near-Surface Air-Temperature Estimation from FY-4A AGRI Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ke Zhou ◽  
Hailei Liu ◽  
Xiaobo Deng ◽  
Hao Wang ◽  
Shenglan Zhang
Keyword(s):  
Machine Learning ◽  
Air Temperature ◽  
Large Scale ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Extreme Gradient Boosting

Six machine-learning approaches, including multivariate linear regression (MLR), gradient boosting decision tree, k-nearest neighbors, random forest, extreme gradient boosting (XGB), and deep neural network (DNN), were compared for near-surface air-temperature (Tair) estimation from the new generation of Chinese geostationary meteorological satellite Fengyun-4A (FY-4A) observations. The brightness temperatures in split-window channels from the Advanced Geostationary Radiation Imager (AGRI) of FY-4A and numerical weather prediction data from the global forecast system were used as the predictor variables for Tair estimation. The performance of each model and the temporal and spatial distribution of the estimated Tair errors were analyzed. The results showed that the XGB model had better overall performance, with R2 of 0.902, bias of −0.087°C, and root-mean-square error of 1.946°C. The spatial variation characteristics of the Tair error of the XGB method were less obvious than those of the other methods. The XGB model can provide more stable and high-precision Tair for a large-scale Tair estimation over China and can serve as a reference for Tair estimation based on machine-learning models.

Summer Nonconvective Severe Wind Frequency over Ontario and Its Correlation with Tropical Pacific Sea Surface Temperature

2014 ◽  
Vol 53 (5) ◽  
pp. 1170-1182 ◽  
Author(s):  
Da-Lin Zhang ◽  
Zuohao Cao ◽  
Jianmin Ma ◽  
Aiming Wu
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Regional Climate ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Kendall Test ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Positive Trend ◽  
Climate Conditions

AbstractThe summer nonconvective severe surface wind (NCSSW) frequency over Ontario, Canada, in relation to regional climate conditions and tropical Pacific Ocean sea surface temperatures (SSTs) during the period of 1979–2006 is examined using surface wind reports and large-scale analysis data. A statistically robust positive trend in Ontario summer NCSSW frequency is identified using three independent statistical approaches, which include the conventional linear regression that has little disturbance to the original time series, the Mann–Kendall test without a lag-1 autoregressive process, and the Monte Carlo simulation. A composite analysis of the large-scale monthly mean data reveals that the high- (low-) NCSSW occurrence years are linked to stronger (weaker) large-scale horizontal pressure gradients and more (less) intensive vector wind anomalies in the upper troposphere. Unlike the low-event years, anomalous anticyclonic circulations are found at 500 and 250 hPa in the high-event years, which are conducive to downward momentum transport and favorable for severe surface wind development. It is also found that the summer NCSSW occurs more frequently under the conditions of warmer surface air temperature over Ontario. Further analyses indicate that an increase in the summer NCSSW frequency is well correlated with an increase in the previous winter SSTs over the eastern equatorial Pacific, namely, in the Niño-1+2 and Niño-3 areas, through a decrease in sea level pressure over northern Ontario and an increase in surface air temperature over central and southern Ontario.

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