GIS在衢州市茶叶种植气候区划中的应用

建立了衢州市数字高程模型（DEM），利用气候要素推算模型和GIS的离散点插植，建立与DEM同分辨率的栅格图。由此利用小网格推算衢州农业气候资源的立体分布，分析研究茶叶种植气候区划，获得较好的效果，对茶叶种植有一定意义。     

乌海葡萄种植气候条件及风险决策分析

通过对乌海地区气候特征与葡萄种植所需气候条件对比，统计分析了葡萄种植的有利气象条件和不利因素。结果表明乌海地区气候条件非常适宜葡萄种植。     

新疆伊吾县沙棘气候品质认证技术研究

沙棘是新疆伊吾县的特色产业，沙棘的优质高产与气象因素密切相关。气候品质认证是一个新兴产品，通过开展沙棘气候品质认证工作，将有利于保护特色种质资源、提升企业的品牌价值和市场竞争力。利用伊吾县1991—2020年国家基本气象站和2010-2021年池镇区域自动站的气象观测数据和沙棘发育期资料，根据沙棘的生长习性、种植区气候适宜性和管理状况对品质影响的分析，构建沙棘气候品质认证模型，对伊吾县种植的特色气候品质的沙棘进行了认证和评价。结果表明，2021年伊吾县沙棘气候品质等级为特优。     

黔东南州种植蜂糖李气候适宜性研究

从气候角度分析黔东南州种植蜂糖李适宜性,为蜂糖李种植选址提供气候可行性依据。利用2011-2020年黔东南州200个区域气象站资料和蜂糖李品质最优的产地贵州省安顺市镇宁县六马镇区域气象站资料,用数理统计方法,计算黔东南州200个乡镇与六马镇气温、降水和日照气候指标相对应的气候值,利用适宜度模型计算各乡镇气候适宜度。结果表明：黔东南州有37.5%的乡镇处于蜂糖李种植气候适宜区,没有气候最适宜区域;蜂糖李开花坐果期的气候适宜度低于萌芽、果实生长以及果实成熟期,是制约黔东南州蜂糖李生长的关键期;气温综合适宜度＞日照综合适宜度＞降水综合适宜度,开花坐果期阶段性持续低温、雨日多、日照少是制约黔东南州蜂糖李生长的主要气候要素。黔东南州地形起伏大,气候差异明显,蜂糖李种植要根据当地地形和气候要素情况,选择日照多、雨日少、气温高、向阳、开阔等区域开展种植。     

融安县脐橙种植的气候条件分析

分析融安县的气候条件及脐橙物候期的特点，得出融安县脐橙种植的气候优势，提出适宜种植的区域。     

旺苍县核桃种植气候适宜性区划研究

利用旺苍县50个地面气象站的逐日观测资料,结合旺苍县核桃种植气候适宜性需求,选取年降水量、年平均气温、年日照时数和年≥10℃积温作为气候适宜性区划指标,建立气候因子空间模型,运用GIS技术开展旺苍县核桃种植气候适宜性区划研究。结果表明：旺苍县核桃种植气候适宜性可划分为3类,即最适宜区、适宜区和不适宜区;最适宜区主要分布在旺苍县北部低海拔的高山河谷区,气象条件完全满足核桃种植所需,最适宜核桃种植;适宜区主要分布在旺苍县海拔较高区域,此区域水分条件与日照适宜核桃生长,但热量条件不适宜核桃种植;不适宜区主要集中在旺苍县中部一线及北部高海拔地区,这些区域水分条件和日照同样适宜核桃生长,但热量条件完全不利于核桃种植。     

昭平县基于GIS技术的茶树种植气候区划

基于GIS技术,利用茶树的生长发育条件与气候之间的相关关系,研究昭平县内茶树种植的农业气候区划。利用昭平县1:25万基础地理数据和昭平县气象站近30a的气候资料,建立区划指标的高层模型,并采用GIS技术对研究区的茶树种植进行气候区划,可划分为四个区,即最适宜种植区、适宜种植区、次适宜种植区以及不适宜种植区。针对各个区不同的气候特征,提出相对应的茶树种植建议,为昭平茶叶的种植和生产发展提供科学参考。     

基于GIS的精河县枸杞种植气候适应性精细化区划

摘要：研究分析精河县枸杞种植的气候适宜性，为科学规划枸杞种植布局，充分合理地利用气候资源优势具有实际意义。利用精河县及周边范围内15个气象站1981-2020年逐日平均气温和最低气温资料，采用数理统计分析和GIS空间插值技术方法，在分析枸杞种植气候生态条件的基础上，筛选出≥10 ℃积温、≥10 ℃日数和≥10 ℃期间降水量作为枸杞气候适宜性区划指标，进而对气候要素指标进行栅格化。根据区划指标等级进行重分类，将各气候要素指标图层进行等权重叠加，获得精河县枸杞种植气候适宜性区划，结果表明，精河县枸杞种植区可分为最适宜区、适宜区、次适宜区和不适宜区4个分区，最适宜种植区分布在精河县中部海拔400～600 m的带状平原区，该区综合气候条件非常利于枸杞优质高产；适宜种植区分布在沿最适宜种植区两侧海拔250～400 m和海拔600～800 m的中部平原地带，综合气候条件稍逊于最适宜区。建议在最适宜区和适宜区内发展枸杞不但产量高而且品质好。。     

贵州省冬季错季蔬菜气候适宜性区划研究

利用贵州85个气象观测站1987—2016年逐日气温资料,根据冬季错季蔬菜种植的气候适宜性指标,应用GIS技术和气候要素空间分布回归模型对冬季错季蔬菜种植进行气候精细化区划。结果表明:适宜区主要分布在黔西南州东部、南部边缘及罗甸县、赤水市等低海拔地区;较适宜区主要分布在黔西南州东部和南部、黔南州南部的海拔1 000 m以下地区及黔东南州南部、遵义市北部的海拔600 m以下地区;其余地区均不适宜种植冬季错季蔬菜。     

基于MaxEnt模型的薄壳山核桃气候适宜性区划

基于最大熵模型（MaxEnt）和GIS技术,提出一种薄壳山核桃气候适宜性区划方法。利用美国本土274个种植点,结合美国本土和中国云南省1981—2010年气候数据开展薄壳山核桃气候适宜性区划研究。结果表明:7月平均气温、年平均气温、30年极端最低气温、年降水量、3—5月降水量、年日照时数和4—5月日照时数为影响薄壳山核桃气候适宜性的主要气候因子。基于美国本土种植点构建的MaxEnt模型在该区域具有较高精度,但将模型直接外推用于中国云南省可靠性不足。因此,利用模拟区域和训练样本气候因子值域的偏离程度改进气候适宜性指数,并将云南省薄壳山核桃适宜性划分为最适宜、适宜、次适宜和不适宜4个等级。其中,最适宜区和适宜区分布于热量资源丰富、日照相对充足并具备较好冬季低温条件的亚热带地区和热带地区边缘。受云南省复杂地形和气候条件影响,区划结果呈现出破碎化分布。     

陆面过程模式研发中的问题

陆面过程研究是充分理解天气/气候/地球系统过程不可或缺的重要主题。本文全面梳理了当前用于数值天气/气候/地球系统模式的陆面过程模式研制的问题,建议了当前陆面过程模式研制中需加强和改进完善的关键内容。特别强调在新一代模式研发中建立包含人类活动的高分辨率全球陆面过程模式;特别强调与其他学科相结合,形成不同行业的预报预测系统或研究方法和工具。建议建设中国的集模式发展、数据分析、模拟方法、高性能计算、数据可视化和应用示范为一体的陆面模拟综合集成平台,为天气/气候/地球系统模式提供陆面过程模式,为开展精细化的全球和区域陆面水文-气象-生态的预报预测提供科技支撑。     

Progress towards better weather forecasts for city dwellers: from short range to climate change

Summary The current resolution of operational weather forecast model is not sufficient in general to explicitly resolve even the major cities of the World. As a consequence, urban areas have traditionally been neglected in such models. The introduction of tiled land surface models has enabled sub-gridscale landuse to be modelled, and hence has provided the opportunity to model cities within weather forecast models. However, to date there has been little effort made within the operational weather forecast community. At present there is only one operational centre that explicitly resolves urban areas. This centre includes a simple urban scheme within its mesoscale and global models, which has been shown to have a positive impact on the forecast. However, with the recent developments within urban meteorology there are now a variety of urban schemes, which vary in their complexity and parameter requirements, that would be suitable for operational weather forecast models. So it is likely that more operational models, and in particular mesoscale models, will include urban areas in the near future. With the majority of the World‘s population living in cities, the resilience of these cities to the impacts of climate change is also becoming of increasing interest. This means that urban areas will have to be included within climate change simulations, as well as weather forecast simulations, in the future. At present, only one climate change model has included a parametrisation for urban areas. However, this is likely to increase if work in this area grows rapidly.     

An essay on the impact of climate change on US agriculture: weather fluctuations, climatic shifts, and adaptation strategies

The impact of climate change on US agriculture has been debated for more than two decades, but the estimates ranged from no damage at the lower end to 80 % losses of grain yields at the higher end. This essay aims to help understand such divergent predictions by clarifying the concepts of weather and climate. First, the widely-read panel fixed effects models capture only the impacts of weather fluctuations but not of climate normals. Random weather fluctuations and climatic shifts are two different meteorological events and they have distinct implications on farming decisions. The former is perceived as random while the latter is perceived as non-random by the farmers. Using the historical corn yield data in the US, I explain the differences between the impact of random weather and that of climate change. Second, adaptation strategies to climatic changes and increased climate risks cannot be accounted for by the panel fixed effects models. Using the farm household data collected in sub-Saharan Africa and Latin America, I discuss quantitative significance of modeling adaptation strategies in the estimates of climate damage. Distinction between random weather fluctuations and climatic shifts is critical in modeling farming decisions, as they are fundamental to climate science, but is poorly understood by the impact researchers.     

Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

Climate change affects major biophysical processes in agricultural crop production (e.g. evaporation of plants and soils, nutrient cycles, and growth of plants). This analysis aims to assess some of these effects by simulating regional climate projections that are integrated in the biophysical process model EPIC (Environmental Policy Integrated Climate). Statistical climate models have been developed for six weather parameters based on daily weather records of a weather station in the Austrian Marchfeld region from 1975 to 2006. These models have been used to estimate daily weather parameters for the period 2007–2038. The resulting projections have been compared to climate scenarios provided from the TYNDALL Centre for Climate Change Research, which are based on General Circulation Models (GCMs). The comparison indicates some differences, namely a smaller temperature increase and a higher precipitation amount in the TYNDALL data. Both climate datasets have been used to simulate impacts of climate change on crop yields, topsoil organic carbon content, and nitrate leaching with EPIC and thus to perform a sensitivity analysis of EPIC. Yield impacts have been assessed for four simulated crops, i.e. 6.2?t/ha for winter wheat for statistical climate projections compared to 5.7?t/ha for TYNDALL scenarios, 10.6?t/ha for corn compared to 10.5?t/ha, 3.9?t/ha for sunflower compared to 3.7?t/ha, and 4.5?t/ha for spring barley compared to 4.3?t/ha—all values as an average over the period 2007–2038. Smaller differences have been simulated for topsoil organic carbon content i.e. 55.1?t/ha for the statistical climate projections compared to 55.3?t/ha for the TYNDALL scenarios and nitrate leaching i.e. 7.1?kg/ha compared to 11.1?kg/ha. All crop yields as well as topsoil organic carbon content and nitrate leaching show highest sensitivity to temperature and solar radiation.     

Possible impacts of climate change on extreme weather events at local scale in south–central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.     

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.     

Attribution matters: Revisiting the link between extreme weather experience and climate change mitigation responses

The literature suggests that extreme weather experiences have potential to increase climate change engagement by influencing the way people perceive the proximity and implications of climate change. Yet, limited attention has been directed at investigating how individual differences in the subjective interpretation of extreme weather events as indications of climate change moderate the link between extreme weather experiences and climate change attitudes. This article contends that subjective attribution of extreme weather events to climate change is a necessary condition for extreme weather experiences to be translated into climate change mitigation responses, and that subjective attribution of extreme weather to climate change is influenced by the psychological and social contexts in which individuals appraise their experiences with extreme weather. Using survey data gathered in the aftermath of severe flooding across the UK in winter 2013/2014, personal experience of this flooding event is shown to only directly predict perceived threat from climate change, and indirectly predict climate change mitigation responses, among individuals who subjectively attributed the floods to climate change. Additionally, subjective attribution of the floods to climate change is significantly predicted by pre-existing climate change belief, political affiliation and perceived normative cues. Attempts to harness extreme weather experiences as a route to engaging the public must be attentive to the heterogeneity of opinion on the attributability of extreme weather events to climate change.     

数值预报AI气象大模型国际发展动态研究

数值预报是研究地球系统的重要工具,有助于加深科学家对大气、海洋、气候和环境等复杂系统之间相互作用和变化过程的理解,在防灾减灾、气候变化和环境治理等方面发挥着不可或缺的作用。随着模式复杂度和分辨率的提高,传统数值模式在气候变化研究和气候预测方面取得了迅速的进展,但也面临一些挑战,需要得到数据同化、集合耦合、高性能计算和不确定性分析等多方面的支持。而近年来,“AI+气象”的交叉研究在气象领域引起了广泛关注。基于多种深度学习架构的人工智能大模型,依托强大的计算资源和海量的数据进行训练,能够以新的科学范式进行高效数值预报。气象大模型不断涌现,一些科技公司如华为、英伟达、DeepMind、谷歌、微软等,以及国内外高校如清华大学、复旦大学、密歇根大学、莱斯大学等发布了多个涵盖临近预报、短时预报、中期预报和延伸期预报等不同领域的气象大模型。这标志着人工智能与气象领域的交叉融合已经达到新的高度。尽管气象大模型在现阶段取得了较大突破,但其发展仍然面临弱可解释性、泛化能力不足、极端事件预报强度偏低、智能预报结果过平滑、深度学习框架能力需要拓展等诸多挑战。     

The impact of weather and climate on tourist demand: the case of Chester Zoo

Warmer, drier summer weather brought by global climate change should encourage use of outdoor leisure facilities. Yet few studies assess the effect of current weather and climate conditions upon visits to leisure attractions. Statistical time series models are used to analyse the short-run impact of weather and the long-run impact of climate upon visits to Chester Zoo, England. Temperature has a non-linear effect on visit levels. Daily visits rise with temperature up to a threshold around 21 °C. Thereafter visitor numbers drop on hot days. Visits are redistributed over time in accordance with the weather. Visitors discouraged by rainy weather one day turn up later when the weather improves. Otherwise, visitor behaviour is mainly influenced by the annual rhythm of the year and the pattern of public and school holidays. Out-of-sample tests suggest almost 70 % of the variation in visit levels can be explained by the combination of weather and time of year. Climate change is likely to redistribute visitors across the year. But it does not follow that “summer” visitor behaviour will transfer to spring and autumn. Day length, existing patterns of human activity and availability of leisure time constrain visit levels regardless of better weather. The main implication of potential climate change is the need for physical adaptation of the tourist environment as temperatures rise and rainfall diminishes in summer.