Abstract. China is experiencing the largest and fastest urbanization process in the world (Kneebone E., 2013). At the same time, its current rapid urbanization process is almost simultaneously accompanied by urban sprawl. Since the 1990s, the sprawl process of Chinese cities has begun to reach a climax (Shan Baoguo, 2018). As the largest developing country in the world, China is also one of the countries most vulnerable to the coercion of climate change. This research takes the four municipalities that are China's urban development orientation as typical representatives, and uses multiple indicators to measure urban sprawl and climate change in them. Finally, models are established to explore the impact of urban sprawl on climate warming. The results showed that all four cities experienced sprawl, but to varying degrees. Shanghai is very compact. Also, the climate warming is definitely, yet urban sprawl doesn't always contribute to its deterioration. The proportion of arable land had the least impact on global warming, but it was the only factor that could improve temperatures, albeit conditionally. Fragmented built-up land heating the climate is most critical.
<p>The inverse S function can not only fit the spatial attenuation of construction land density, but also fit the spatial attenuation characteristics of various urban characteristics. Therefore, we assume that the inverse S-function curve is also applicable to the spatial variation law of urban LST. We hope to conduct an inverse S function model fitting analysis of the surface temperature of the three major cities in Beijing, Tianjin, Hebei, China's capital economic circle in 2001 and 2020 in winter, summer, day and night in eight periods&#160;to verify that they all conform to the characteristics of the function curve, and use the fitting parameters to analyze the urban development process and its impact on the thermal environment.</p> <p>First, we draw concentric circles at intervals of 1KM from the center points of the three cities, and then extract the land surface temperature (LST) of each circle and process it dimensionlessly. Finally, the inverse S function model is fitted to all LST data, and the expression of the inverse S function is as follows. And combined with the characteristics of LST, the fitting parameters in the function are given corresponding meanings.</p> <p><img src="" alt="" width="171" height="66" /></p> <p><strong>Analyzing the results of fitting parameters, LST conforms to the law of the reverse S-curve model in most cases.</strong></p> <p>Since the LST in the most periods can be simulated by the inverse S model, it is proved that their change law is that they first decrease slowly with the increase of the radius of the concentric circle, then decrease rapidly, and finally decelerate to zero.</p> <p><strong>The fit parameter "a" controls the slope of the curve. The larger "a" is, the faster the curve decays, indicating that the urban thermal environment is more compact.</strong></p> <p>The "a" of each city of winter is greater than that of summer.</p> <p>Except for the smallest "a" in winter night in Beijing in 2020, the "a" in summer in 2001 was the smallest in other cities. The distribution of urban thermal environment in this period is the most scattered.</p> <p>The "a" results for Beijing and Tianjin are similar every time, but Beijing has a wider range of values. Tianjin's is generally larger than them.</p> <p><strong>The fitting parameter "c" is the mean value of surface temperature at the city fringes.</strong></p> <p>The&#160;most cities are distributed between 0 and 0.2.</p> <p>Only Tianjin Xiaye in 2020 reached 0.53. It shows that the temperature around Tianjin is on the high side during this period.</p> <p><strong>The fitting parameter "D" reflects the radius of the urban thermal environment.</strong></p> <p>The "D" of each city sample has increased to varying degrees, indicating that the urban high-temperature thermal environment has also expanded.</p> <p>The thermal environment radii of Beijing and Shijiazhuang are the smallest at night in winter, while Tianjin is the smallest at night in summer.</p> <p>The fastest growth rate was during summer nights, with each city adding more than 10 kilometers.</p> <p>The slowest growth in Beijing is during the daytime in summer, while that in Tianjin and Shijiazhuang is during the night in winter.</p>
<p>According to NASA's temperature record, Earth in 2021 was about 1.1 degrees Celsius warmer than the late 19th century average, the start of the industrial revolution. The rate of Global Warming (GW), however, differs across different regions of the planet. The Mediterranean is one of the "hotspots" of climate change, with more prominent temperature increases throughout the 20th and 21st centuries (Giorgi 2006). Since the mid-20th century, the average temperature over the Mediterranean has been increasing above the global average. The recent temperature record reveals an annual mean temperature for the entire basin that is approximately 0.4&#176;C above the global mean (Lange 2021). This increase is even higher on the Spanish coast, which has experienced increases of more than 2&#176;C (Arellano 2022).</p> <p>The aim of this paper is to analyze the warming process in the main Spanish urban areas since unified records were kept in the early 1970s. For this purpose, the evolution experienced by temperatures between 1971 and 2022 in 21 meteorological stations representative of all the Spanish Autonomous Communities is analyzed. Barcelona, Madrid, Valencia, Zaragoza, Seville, Malaga, Bilbao, Valladolid, Ciudad Real, Badajoz, Asturias, Corunya, Ourense, Murcia, Logro&#241;o, Palma de Mallorca, Las Palmas de Gran Canaria and Santa Cruz de Tenerife, are studied.</p> <p>The results show that, if on a global scale temperatures have risen 0.94&#176;C since 1971, the increase in the main cities of peninsular Spain has been 2.17&#176;C. And 2022 will be the warmest year on record. The research carried out differentiates the evolution experienced by maximum and minimum temperatures, showing that the continental influence is mainly manifested in the increase of maximum temperatures, while in the area of Mediterranean influence, the increase of minimum temperatures is more pronounced. On the other hand, the Cantabrian and Atlantic coasts, as well as, above all, the Canary Islands, show less pronounced increases, below 2&#176;C.</p> <p>The study also presents the heat and cold waves (Serra 2022) experienced by the cities studied. Diurnal heat waves (DHW) have increased from 0.6 per year per weather station in the decade 1971-1980, to 1.71 in 1981-1990, 1.81 in 1991-2000, 2.72 in 2001-2010, and 3.84 in 2011-2020. 2022, with 7.11 DHW per station, is the year with the highest number of diurnal heat waves in the entire series. Regarding nocturnal heat waves (NHW) they have increased from 0.47 per station per year in the decade 1971-1980, to 1.53 (1981-1990), 1.57 (1991-2000), 3.55 (2001-2010), and 4.63 (2011-2020). Again 2022 is the year with the highest number of NHW, with 7.61 per weather station.</p> <p>2022 appears, therefore, as the warmest year since records have been kept, and the one in which a greater number of NHW has been experienced.</p>
<p>In the context of global warming, frequent extreme climate events, especially high temperature heat waves and global warming, lead to an increase in the frequency and intensity of heat waves. At the same time, due to changes in climatic and hydrological characteristics, extreme precipitation and drought events closely related to people's lives frequently occur. This research studies the heat waves and extreme precipitation events from 1971 to 2020 in the Mediterranean coast of Spain, mainly in the Barcelona metropolitan area, and analyzes their main causes and influencing factors. It is of great significance to formulate improved policies and protection mechanisms in the future to promote sustainable urban development. We selected 8 different meteorological observatories as primary climate data sources in the provinces of Barcelona and Valencia, Alicante, Murcia and Almeria respectively. Using the OLS model, we estimated the global warming at each temperature by the cosine formula <strong><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.81b0277afdd167257391461/sdaolpUECMynit/22UGE&app=m&a=0&c=c47a45e6fc9b33354d9479cd81e3d03b&ct=x&pn=gnp.elif&d=1" alt="" width="122" height="43">&#160; &#160;</strong>&#160;from the analysis of the daily average temperature, maximum temperature, and minimum temperature for each observation point. As a result, stations with higher average temperatures had lower estimates of their warming. The performance of global warming varies greatly between day and night, and is more pronounced at night than during the day. Raval is the only sample with negative values. We taken 1971-2000 as the observation period, and use the 95% percentile to judge extreme climate. It was found that the frequency of heat waves increased year by year, and the number of heat waves occurred at night was significantly higher than that during the day. The precipitation on a heat wave night is generally higher than that on a heat wave day, but the heat wave is usually accompanied by drought. However high humidity is high during the heatwave in central Barcelona. The occurrence of extreme precipitation decreases, with a higher density of heavy rainfall in the southern region than in Barcelona. In addition, extreme precipitation has made an outstanding contribution to the annual precipitation, up to 88.47%. Finally, various regression models are established to analyze the possible factors affecting extreme climate. High latitudes and long distances from the sea promote heatwaves during the day and can also prolong the number of days that they last. Heatwave nights are more frequent in high latitudes, but staying away from the ocean and high altitude can improve it. In addition, global warming and precipitation are supporting factors for high temperature heat waves. The frequency of extreme precipitation is directly proportional with latitude and mean precipitation, and is inversely correlated with distance and altitude from the sea and daily maximum temperature. There is no obvious relationship between extreme precipitation and daily maximum precipitation.</p>
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