基于GIS的乡村旅游气候舒适度评估与区划 ——以重庆市长寿区为例

该文通过计算温湿指数、风效指数,参照人体生理舒适指标分级标准和综合舒适度模型,获得综合舒适度指数,综合评估长寿乡村旅游气候舒适度。研究发现：①全区乡村旅游气候舒适度的空间差异主要来自地于“三山夹两槽”的地形差异。长寿地区除了夏季乡村旅游气候舒适度为不舒适外,其余大部分时候全区气候舒适度较高。②全区各地乡村旅游气候舒适期为4~6个月,不舒适期为2~3个月,可见全年舒适期长,不舒适期短,具有较强的乡村旅游气候资源优势。③乡村旅游气候舒适期较长的区域主要分布在长寿城区及以南和长寿中部地区。     

河源地区的旅游气候舒适度分析

根据河源市5个县区1960—2014年气象资料,利用综合考虑温湿指数、风寒指数、着衣指数等综合舒适度评价指数,采用线性趋势分析等方法,分析河源地区近55年旅游气候舒适度的时空分布特征,结果表明:河源地区旅游气候舒适度指数平均为5.5,旅游气候舒适月数平均达8个月,平均指数处于较舒适区,且整体上南部优于北部,最舒适期为10月,较舒适期为9、11月。55年来,河源地区年综合舒适指数总体呈上升趋势,旅游气候舒适期长度有所增加,对旅游业的发展有一定促进作用。     

呼包鄂地区气候舒适度时空变化及其关键气象因子分析

基于1970—2019年呼和浩特、包头、鄂尔多斯（呼包鄂）地区25个气象站日值,以温湿指数、风效指数、着衣指数及舒适度综合指数为气候舒适度评价指标,利用趋势分析法、M-K突变检验法、通径分析法,对该地区的气候舒适度指数变化特征、影响舒适度指数的关键气象因子及舒适日数的空间分布特征进行分析。结果表明: 呼包鄂地区近50 a的温湿指数、风效指数、舒适度综合指数呈显著上升趋势,着衣指数呈显著下降趋势; 综合舒适期为4—10月,舒适日数在5—9月达到68%—93%,其中6—8月全月为舒适或较舒适日,在20世纪90年代内蒙古中部气候变化背景下,温湿舒适性和风效舒适性向暖转变,人体综合舒适度越来越高,适宜居住、旅游和避暑; 影响该地区气候舒适度的关键气象因子是温度和风速,其次是日照; 舒适和较舒适日数整体呈“北少南多”分布,舒适日数分布整体呈波动变化,较舒适级日数整体持续增多。工业生产、人类活动、生态环境在一定程度上影响气候舒适和较舒适日数。     

汉中地区旅游气候舒适度评价

利用1981—2010年汉中地区11县(区)气温、风速、相对湿度、日照时数等气象要素的月平均值资料,采用温湿指数、风效指数、着衣指数及由3指数组成的综合气候舒适度指数,对汉中各区县全年各月的旅游气候舒适度进行了分析评价,结果显示:留坝、佛坪等北部山区旅游舒适期在4—9月,宁强、镇巴及略阳等南部、西部山区全年旅游舒适期多在4—6月及9—10月,汉台、勉县、南郑、城固、洋县以及西乡等平川地区的旅游舒适期在4、5、9、10月,全年旅游不舒适期较短,留坝为2个月,佛坪仅1个月,其它区域无旅游不舒适期;汉中各地区旅游气候舒适度差异较明显,南部及西部山区气候舒适度较高,其余地区气候舒适性相对较低;汉中各地区四季的气候舒适度以春、秋季最高,而留坝和佛坪等北部山区则表现为夏季最高,冬季均表现为最低;汉中地区旅游气候舒适度划分为北部山区、平川区、南部及西部山区3个区域。     

1971—2014年玉门市旅游气候舒适度评价分析

利用玉门市1971—2014年逐日气温、相对湿度、平均风速和日照时数等气象观测资料,运用温湿指数、风寒指数、着衣指数和人体舒适度指数4种旅游气候舒适度评价指数,计算玉门市旅游舒适度指数,确定了玉门市旅游气候舒适期及天数;构建综合舒适度指数模型,并对各指数进行了突变检验。结果表明,玉门市较舒适期为4月下旬中期至9月下旬末,约160 d;舒适期为5月中旬至9月中旬,约124 d。其中,5、6、8月最适宜旅游,7、9月为适宜旅游,11月—次年的3月,由于气候寒冷,不适宜旅游。玉门市的温湿指数舒适期天数从1983年开始明显增多;风寒指数舒适期天数从1990年开始明显增多;着衣指数舒适期天数从1991年开始明显增多;人体舒适度指数舒适期天数从1986年开始明显增多。     

兰州市旅游气候舒适度与客流量关系分析

基于兰州市1981—2010年的气象观测资料,采用温湿指数、风效指数和着衣指数3个指标,对兰州市旅游气候舒适度进行分析评价。结果表明,兰州市全年旅游气候舒适期可达7个月,旅游最舒适月份从5月持续到9月,不适宜期主要集中在冬季的1月、12月。结合2010—2014年兰州市旅游客流量年内变化数据,划分了兰州市旅游淡旺季和客流量月指数;综合分析客流量月指数与三个指数关系得出:客流量年内变化与气候舒适度呈明显的相关性,客流量月指数与温湿指数、风效指数呈正相关,与着衣指数呈负相关,且3个指数与客流量月指数相关性较好,都在0.7以上。     

甘肃平凉市的旅游气候舒适度评价

利用甘肃省平凉市7县（区）气象观测站1961—2010年50a气象资料,采用温湿指数、风寒指数和着衣指数指标,对平凉7县（区）各月旅游气候舒适度进行了分析。结果显示,平凉市7县（区）旅游气候最舒适期为每年的5～9月,较舒适期在4月和10月,不舒适期静宁、庄浪、华亭为4个月,主要分布在冬季及秋末（1～2月、11—12月）,崆峒、灵台、泾川、崇信为3个月,分布在冬季的1、2、12月。由于地理位置和海拔高度的不同,平凉旅游气候在东西南北方向上有着一定差异,在东西方向上差异尤其明显,一年中舒适度年指数自西向东呈升高趋势。     

吉林省消夏旅游期气候条件及舒适度分析

利用1951—2016年5—9月吉林省50个气象站气候资料,对消夏旅游期影响人们生活舒适度的主要气候要素进行统计,并构建旅游气候相关指数。结果表明:主要气候要素和旅游气候相关指数在1981年后均发生显著变化,各月平均气温升高,7—9月降水量减少,各月平均风速减小,6月、8月和9月的平均相对湿度略有减小,各月平均日照时长减小;各月温湿指数增大、避暑指数减小,6月、7月和9月旅游气候舒适度指数增大。吉林省消夏期具有"温湿适宜、日照适宜,多微风日,且白天少降水"的气候特点。消夏旅游期各地区的温湿指数处于"偏热、较舒适"水平,避暑指数较高,旅游气候舒适度指数处于"很舒适"及以上水平。     

安顺市1981—2015年旅游气候舒适度时空分布特征

利用安顺市6个县区气象观测站1981—2015年的气温、湿度、风速、日照等月平均资料,应用温湿指数（THI）、风效指数（K）、着衣指数（ICL）、综合舒适度指数（C）,分析安顺市旅游气候舒适度时空分布。结果表明:安顺市各县区四季分明,气温适宜,无严寒和酷热天气,综合舒适期均长达9个月以上,尤其关岭县全年均为舒适期,适合全年开展旅游。考虑到日照对舒适度的影响,对C进行了本地化调整,使其更客观地反应安顺市各县区的综合舒适度。安顺市舒适期长度西南部大于东北部。     

四川省旅游气候舒适度评价

为了更好地发展旅游事业,本文以四川省2006~2016年21个站点的气象数据为基础数据,运用奥利弗温湿指数和IDW空间插值法对四川省旅游气候舒适度进行评价。结果表明:(1)从总体上看,四川省旅游气候舒适度差异显著,呈现出西部较高、东部较低的特征;(2)从季节上看,四川省春、秋季舒适度较高,最适合人们旅游;夏、冬季舒适度较差,最不适合旅游;(3)从旅游地上看,除了甘孜州、阿坝州和凉山州的最舒适时期为7月,最不舒适时期为1月。四川省典型旅游地最舒适时期大都为4月和10月,最不舒适时期主要为1月和7月。因此,除甘孜州,阿坝州和凉山州外,4月和10月四川省适合旅游;相反,1月和7月四川省不适合旅游。     

湖南气候对全球气候变化的响应

利用湖南省96个台站1960—2010年逐日气象观测资料,在进行均一性检验和订正的基础上对湖南省气候变化事实进行检测分析。结果表明:湖南气候与全球气候变化一致,呈现以变暖为主要特征的变化,且变暖存在季节、地域上的差异,冬、春、秋气温变暖趋势显著,增暖幅度最大的区域在湘北地区;对气候变暖响应敏感的要素主要是与平均气温、冬季气温相关密切的要素,如季平均气温、年平均最低气温、活动积温等;湖南气温在突变时间上具有较好的时间逻辑关系;湖南降水量无显著趋势变化,但极端降水增加,地域性差异明显,湖南东部地区降水量呈现明显增加趋势,日降水量大于等于100 mm的日数呈显著增加趋势;湖南日照时数、风速、相对湿度均呈现显著减少的变化趋势。     

Politics eclipses climate extremes for climate change perceptions

Whether or not actual shifts in climate influence public perceptions of climate change remains an open question, one with important implications for societal response to climate change. We use the most comprehensive public opinion survey data on climate change available for the US to examine effects of annual and seasonal climate variation. Our results show that political orientation has the most important effect in shaping public perceptions about the timing and seriousness of climate change. Objective climatic conditions do not influence Americans’ perceptions of the timing of climate change and only have a negligible effect on perceptions about the seriousness of climate change. These results suggest that further changes in climatic conditions are unlikely to produce noticeable shifts in Americans’ climate change perceptions.     

BCC_CSM气候系统模式移植优化及其气候模拟验证

为了提高BCC_CSM气候系统模式运行效率,保障业务科研工作的顺利开展,进行BCC_CSM气候系统模式在IBM高性能计算系统的移植工作;通过性能优化使BCC_CSM模式运行效率显著提高,通过气候要素形势场分布和相对误差量化指标对BCC_CSM气候系统模式模拟性能进行验证。结果表明：移植优化后,BCC_CSM气候系统模式计算效率提高为原来的1.4倍;基于CMIP5 piControl试验,完成531-540年10 a的气候模拟,年平均地表气温形势场分布合理,相对误差小于0.5%,BCC_CSM气候系统模式计算和模拟性能均能满足应用需求。     

Perceptions of climate and climate change by Amazonian communities

The Amazon region has been undergoing profound transformations since the late ‘70s through forest degradation, land use changes and effects of global climate change. The perception of such changes by local communities is important for risk analysis and for subsequent societal decision making. In this study, we compare and contrast observations and perceptions of climate change by selected Amazonian communities particularly vulnerable to alterations in precipitation regimes. Two main points were analysed: (i) the notion of changes in the annual climate cycle and (ii) the notion of changes in rainfall patterns. About 72% of the sampled population reports perceptions of climate changes, and there is a robust signal of increased perception with age. Other possible predictive parameters such as gender, fishing frequency and changes in/planning of economic activities do not appear overall as contributing to perceptions. The communities’ perceptions of the changes in 2013–2014 were then compared to earlier results (2007–2008), providing an unprecedented cohort study of the same sites. Results show that climate change perceptions and measured rainfall variations differ across the basin. It was only in the southern part of the Amazon that both measured and perceived changes in rainfall patterns were consistent with decreased precipitation. However, the perception of a changing climate became more widespread and frequently mentioned, signalling an increase in awareness of climate risk.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Regional climate modelling

Summary Regional climate modelling is becoming increasingly popular. The most common technique employs high resolution limited-area models to economically produce detaited climatologies for selected regions. A short review is presented of the underlying principles, recent simulations limitations of the method and future prospects.With 4 Figures     

IPCC AR6报告解读：短寿命气候强迫因子的气候及环境效应

影响气候变化的大气成分,依据其在大气中存留的时间,分为长寿命的温室气体和短寿命的气候强迫因子（SLCFs)。考虑到SLCFs在气候变化和大气环境中的重要作用,IPCC第六次评估报告（AR6）首次有了专门针对SLCFs的章节（第六章)。本文解读IPCC报告关于SLCFs的主要结论,特别强调AR5以来的最新结论,包括：SLCFs的定义、SLCFs排放和大气含量的变化特征及其对辐射强迫和全球气候的影响、不同共享社会经济路径（SSP）情景下SLCFs对未来气候变化和空气质量可能的影响,以及COVID-19疫情期间减排对气候变化的影响。文末也讨论了结论的不确定性以及结论对我国的启示。     

Framing the way to relate climate extremes to climate change

The atmospheric and ocean environment has changed from human activities in ways that affect storms and extreme climate events. The main way climate change is perceived is through changes in extremes because those are outside the bounds of previous weather. The average anthropogenic climate change effect is not negligible, but nor is it large, although a small shift in the mean can lead to very large percentage changes in extremes. Anthropogenic global warming inherently has decadal time scales and can be readily masked by natural variability on short time scales. To the extent that interactions are linear, even places that feature below normal temperatures are still warmer than they otherwise would be. It is when natural variability and climate change develop in the same direction that records get broken. For instance, the rapid transition from El Ni?o prior to May 2010 to La Ni?a by July 2010 along with global warming contributed to the record high sea surface temperatures in the tropical Indian and Atlantic Oceans and in close proximity to places where record flooding subsequently occurred. A commentary is provided on recent climate extremes. The answer to the oft-asked question of whether an event is caused by climate change is that it is the wrong question. All weather events are affected by climate change because the environment in which they occur is warmer and moister than it used to be.