新旧气候平均值的差异及其对西南气候业务的影响

选取西南地区短期气候预测业务选定的81个气象站,将其1971~2000年30年气候平均值与1981~2010年30年气候平均值进行比较,结果发现,就西南大部分地区而言,前者所表征的气候较后者更冷湿。把两个平均值放在长序列中分析,发现冬季平均气温和汛期降水量平均值的变化幅度较大,这些变化已经对气候影响评价、气候预测业务产生影响。     

两个30年气候平均值的差异及其对气候业务的影响

对陕西短期气候预测业务使用的39个气象站气候要素1971～2000年平均值与1961～1990年平均值进行比较,发现大部分地区年平均气温升高,晴天日数增加;年降水量、阴天日数、降水日数、年日照时数减少;年大风日数、沙尘暴日数、冰雹日数、雷暴日数减少等。把两个平均值放在长序列中分析,发现某些气象要素最近的气候平均值的差值在20世纪80～90年代期间的5～6个气候平均值每两个相邻平均值的差值中是最大的,说明陕西20世纪90年代气候较60年代干燥、温暖,且90年代气温高、降水少的特点十分突出。还对某些要素平均值改变对气候影响评价、气候预测业务产生的影响进行简要分析。     

1991～2020年四川省气候平均值的变化及业务应用评估

利用1991～2020年与1981～2010年四川省156个国家气象站地面气候平均值资料,从年、月尺度对比分析了新旧2个30 a气候平均值的差值分布特征及业务应用差异。结果表明：（1）近30 a四川省年平均气温升高了0.3℃,年内各月平均气温均升高,全省93.7%站点增温0.1～0.5℃。（2）近30 a四川省平均年降水量增多3.8 mm,年内6～10月降水量差值变化大,川西高原年降水量大部地区以增多为主,盆地和攀西地区以减少为主。（3）启用新30 a气候平均值后,四川省大部地区年、月平均气温距平有所降低,全省平均年降水量距平变化不大,但各地年、月降水量距平变化区域差异显著。（4）四川地区冬季平均气温升高,近42 a中（1981～2022年）暖冬减少6 a,冷冬增加5 a;春季平均气温升幅大,盆地常年春季开始日期提早,持续天数增加。（5）气候平均值的改变对气象要素及气候事件监测评价等业务应用有较大影响。     

气候变暖背景下气候平均值更替对中国气候业务的影响

气候平均值的改变意味着气候平均态(以下简称气候态)的改变,由于不同气候态之间的差异,当气候态更替时,相应会改变各种气象要素、环流系统等变化特征的判识结果.不同气候态之间的差异越大,对判识结果的影响就会更加明显,这种影响即是气候态更替的影响.对于2022年1月1日执行的气候平均值由1981—2010年改为1991—202...     

近30年均值的改变对四川省气候评估结果的影响

基本气象要素在一段时期内统计值的变化,对描述某一地区干、湿、冷、暖等气候态的影响很大.本文旨在根据世界气象组织的建议以及统一标准的要求,对气象变量用1971～2000年(以下称新30年)的平均值代替1961～1990年(以下称旧30年)的平均值,分析四川省新旧30年日照、温度、降水等基本气象要素平均值的变化及其对气候评价结果的影响后发现:1、四川省大部分地区日照时数距平值上升,2、气温距平值以下降为主,3、降水距平值以盆地大部分地区上升、川西高原下降为主要特征,4、偏暖年及暖冬年相对减少,夏季盆地东(西)部降水偏少(多)年相对增多,导致在新30年标准下,干旱、洪涝需重新评估,从而不同程度地影响了四川省今后气候评价的结果.     

新旧气候态的差异及其对江西气候业务的影响

利用1961—2022年江西74个气象站平均气温、最高气温、最低气温、降水量、相对湿度、平均风速和日照时数资料,对比分析了1991—2020年和1981—2010年新、旧气候态下气象要素差异,探讨气候平均值改变对气候影响评价和预测业务的影响。结果表明：新气候态下,江西省三类气温的年和季节平均值均上升,年降水量总体增加将弱化气温偏高、降水偏多的变化特征。年和季节平均风速距平山区减小而平原地区增大;年日照时数距平总体增加。极端高温年份减少,极端低温年份增多,其中平均气温和最低气温的极端高（低）温年发生概率的降幅（增幅）比最高气温更大。极端强降水年发生概率在赣西北、赣中大部、赣南西北部等地区夏季减少,赣南中南部地区冬季增大。全省历年极端日高温、低温和强降水事件发生站次总体减少。新、旧气候态的更替会对气候业务产生影响,如冬季气温偏冷的年份增加,偏暖的年份减少,需对冷、暖冬事件进行重新评估,夏季降水增多的变化特征减弱,将导致夏季降水预测量级和趋势发生改变。     

新、旧气候态的差异及对东北地区气候业务的影响

选取参与东北地区短期气候预测业务质量评估的53个气象站的月平均气温、降水资料,NCEP/NCAR再分析的月平均500 hPa位势高度场资料,以及由NOAA重构的海温场资料,对比了新、旧气候平均态下,冬、夏季东北地区气温、降水及全球500 hPa位势高度场及海温场差异, 并分析了气候平均值改变对气候变化、影响评价和预测业务的影响。结果表明：就东北大部分地区而言,新气候态（1981-2010年）表征的气候较旧气候态（1971-2000年）更暖湿;新气候态的全球500 hPa位势高度值和海温值较旧气候态均有所增大;新气候态下东亚大槽强度和西伯利亚高压强度变弱是造成东北冬季气温升高的主要原因;西太副高和鄂霍次克海阻塞高压强度增强,是造成东北地区大部分月份降水量增加的主要原因;而东北冷涡强度减弱是造成东北地区6月降水量减少的主要原因;9月和10月降水量减少可能与海温的变化有关。气候平均值的改变会对气候业务产生影响,如需对冷冬事件和ENSO事件重新评估,对极端事件重新分析,及对要素预报量级和趋势产生影响。     

2006年春季南北疆沙尘暴的气候预测

利用1961～2005年北疆9个气象站和南疆15个气象站逐日沙尘暴日数、平均气温、气温日较差、平均地面温度、地气温差、平均风速、降水量、平均相对湿度,计算得到北疆和南疆春季沙尘暴日数和前期气候要素,通过相关分析得到北疆和南疆春季沙尘暴的气候影响因子。利用前期气候影响因子分别建立了北疆和南疆春季沙尘暴日数气候预测方程6个。预计2006年春季,北疆沙尘暴日数1.4～2d,南疆沙尘暴日数7d,它们虽比历年平均值偏低,但高于近15a的平均值。     

黑龙江省1981-2010年气候平均值的变化及对气候评价的影响

利用黑龙江省气候评价业务使用的71个气象台站的气温、降水和日照时数资料,对1981-2010年气候平均值和1971-2000年气候平均值进行比较。结果表明:黑龙江省大部地区年平均气温升高,冬季偏暖突出;年降水量大部地区增多,春季、夏季、冬季降水量增多,秋季减少,7月降水量增幅最大;年日照时数大部地区减少。     

气候动力学与气候预测理论的研究

主要概述了中国科学院大气物理研究所近些年来在气候动力学与气候预测理论研究领域的若干重要研究进展.通过对气候系统变化多尺度特征及其动力学的分析和研究,提出了一系列气候系统动力学理论,并在此基础上提出了适合于我国季风气候特点的气候预测理论和方法,在国际上率先开展了跨季度数值气候预测,进一步建立了先进、完善的短期数值气候预测系统,并应用于我国夏季旱涝预测业务.这些工作既带有极大的基础性意义,同时也具有巨大的应用价值,为我国大气科学及气候科学乃至环境科学的研究提供了重要工具.     

Seasonal climatic variability

Abstract

An analysis of variance of the 1000–500 mb thickness field is performed to investigate the possibility of seasonal change in climatic variability during the period 1949 to 1975.

The mean thickness and measures of transient eddy, standing eddy and north‐south variance, averaged over the region from 25°N to the Pole, are analysed for the annual average and for each of the four seasons. For the annually averaged data, the only statistically significant trend is a linear decrease in mean thickness. None of the variability measures display significant trends in annually averaged values.

On a seasonal basis, a significant trend in mean thickness is found in three of the four seasons. Several seasonal measures of variability show statistically significant trends. The most notable result of the analysis is an apparent increasing trend in summer season values of both transient and standing eddy measures of variability.

The results of this study reiterate those of a previous study that found no overall change in climatic variability during the period. In addition, however, the data suggest an increase in variability during the summer season although this increase is not sufficient to affect the overall annually averaged value.     

Wind erosion climatic erosivity

A physically based wind-erosion climatic factor has been derived: $$CE = \rho \int {[u^2 } - (u_T^2 + \gamma ^l /\rho a^2 )]^{3/2} f(u)du$$ where ? is the air density, a is a constant made up of other constants (von Karman, height of wind speed observation, roughness parameter), u is the horizontal wind speed, u _T is threshold wind speed, f(u) u) is a wind speed probability density function, and γ is the cohesive resistance caused by water on the soil particles. Cohesive resistance is proportional to the square of water content relative to water content at ?1500 J kg^?1. Relative water content is approximated from the Budyko dryness ratio and the Thornthwaite PE index with similar results. CE is calculable from wind speed and other generally available meteorological data, and is usable in the wind erosion equation without some of the limitations of a previously used wind erosion climatic factor.     

A description of persistent climatic anomalies in a 1000-year climatic model simulation

 The Mark 2 version of the CSIRO coupled global climatic model has been used to generate a 1000-year simulation of natural (i.e. unforced) climatic variability representative of “present conditions”. The annual mean output from the simulation has been used to investigate the occurrence of decadal and longer trends over the globe for a number of climatic variables. Here trends are defined to be periods of years with a climatic anomaly of a given sign. The analysis reveals substantial differences between the trend characteristics of the various climatic variables. Trends longer than 12 years duration were unusual for rainfall. Such trends were fairly uniformly distributed over the globe and had an asymmetry in the rate of occurrence for wet or dry conditions. On the other hand, trends in surface wind stress, and especially the atmospheric screen temperature, were of longer duration but primarily confined to oceanic regions. The trends in the atmospheric screen temperature could be traced deep into the oceanic mixed layer, implying large changes in oceanic thermal inertia. This thermal inertia then constituted an important component of the `memory' of the climatic system. While the geographic region associated with a given trend could be identified over several adjacent grid boxes of the model, regional plots for individual years of the trend revealed a range of variations, suggesting that a consistent forcing mechanism may not be responsible for a trend at a given location. Typical return periods for 12-year rainfall trends were once in 1000 years, highlighting the rarity of such events. Using a looser definition of a trend revealed that drying trends up to 50 years duration were also possible, attributable solely to natural climatic variability. Significant (∼20% to 40%) rainfall reductions per year can be associated with a long-term drying trend, hence such events are of considerable climatic significance. It can take more than 100 years for the hydrologic losses associated with such a trend to be overcome. Overall, the simulation provides new and useful insights into climatic trends, and quantifies a number of poorly observed characteristics. The results highlight the extensive and pervasive influence of unforced natural climatic variability as an omnipresent generator of climatic trends. Received: 20 January 2000 / Accepted: 21 September 2000     

Agricultural adaptation to climatic variation

Assumptions underlying impact assessments of climatic change for agriculture are explored conceptually and empirically. Variability in climatic conditions, the relevance of human decision-making, and the role of non-climatic forces are reviewed and captured in a model of agricultural adaptation to climate. An empirical analysis of farmers' decisions in light of variations in climate and other forces is based on a survey of 120 farm operators in southwestern Ontario. Many farmers were affected by variable climatic conditions over a six-year-period, and some undertook strategic adaptations in their farm operations. Frequency of dry years was the key climatic stimulus to farming adaptations. However, only 20 percent of farmers were sufficiently influenced by climatic conditions to respond with conscious changes in their farm operations.     

Prediction of nonstationary climatic series

Summary Most of the stochastic prediction methods are developed for stationary time series. However, many climatic series show clear evidence of non-stationarity. In such cases, methods based on the stationarity assumptions would be inappropriate. Alternative methods such as those based on stochastic approximation are preferable in these cases because they are based on adaptive learning principles. These methods have not been applied and their suitability not tested with nonstationary climatic time series.In the stochastic approximation method, the deterministic component of a nonstationary time series is estimated by first predicting the two steps ahead value of a time series. The two steps-ahead forecast may involve a term characterizing the trend in the time series. The two steps-ahead predictor is corrected to obtain the one step ahead prediction by using a gain sequence.The dynamic stochastic approximation method is used herein to predict non-stationary climatic time series. Daily minimum temperature series at West Lafayette, Indiana, U.S.A. and seasonal temperature and precipitation series at Evansville, Indiana, U.S.A. are used in the study. For data trends, an improved dynamic stochastic approximation method, called the modified dynamic stochastic approximation method gives more accurate predictions. If the method is used for seasonal data, then it can be used to track the time varying mean value.With 6 Figures     

Modelling geomorphic response to climatic change

This paper develops a three-step thaw model to assess the impact of predicted warming on an ice-rich polar desert landscape in the Canadian high Arctic. Air temperatures are established for two climate scenarios, showing mean annual increases of 4.9 and 6.5°C. This leads to a lengthening of the summer thaw season by up to 26 days and increased thaw depths of 12–70 cm, depending on the thermal properties of the soil. Subsidence of the ground surface is the primary landscape response to warming and is shown to be a function of the amount and type of ground ice in various cryostratigraphic units. In areas of pore ice and thin ice lenses with a low density of ice wedges, subsidence may be as much as 32 cm. In areas with a high density of ice wedges, subsidence will be slightly higher at 34 cm. Where massive ice is present, subsidence will be greater than 1 m. Landscape response to new climate conditions can take up to 15 years, and may be as long as 50 years in certain cases.     

Possible climatic impacts of tropical deforestation

Large-scale conversion of tropical forests into pastures or annual crops will likely lead to changes in the local microclimate of those regions. Larger diurnal fluctuations of surface temperature and humidity deficit, increased surface runoff during rainy periods and decreased runoff during the dry season, and decreased soil moistrue are to be expected.It is likely that evapotranspiration will be reduced because of less available radiative energy at the canopy level since grass presents a higher albedo than forests, also because of the reduced availability of soil moisture at the rooting zone primarily during the dry season. Recent results from general circulation model (GCM) simulations of Amazonian deforestation seem to suggest that the equilibrium climate for a grassy vegetation in Amazonia would be one in which regional precipitation would be significantly reduced.Global climate changes probably will occur if there is a marked change in rainfall patterns in tropical forest regions as a result of deforestation. Besides that, biomass burning of tropical forests is likely adding CO₂ into the atmosphere, thus contributing to the enhanced greenhouse warming.     

Solar signals in global climatic change

Based on the physical background that varying solar activity should lead to variations of the ‘solar constant’ and that the climate system may respond sensitively even to small solar variations, a correlation analysis is performed where hemispheric and global averages of the annual mean surface air temperature are compared with the variations of a variety of solar forcing parameters: sunspots, related hypotheses including variations of the quasi-eleven-year solar cycle length, solar diameter variations and gravitational effects. This analysis is based on the 1881–1988 period, for the northern hemisphere including proxy data 1671–1988. Cross correlations and correlations moving in time reveal some instability effects which are hard to interpret. The temperature variance components which may be hypothetically explained by solar forcing are small. Similarly, a seasonal and regional signal and signal-to-noise analysis based on a gridded temperature time series 1890–1985 reveals small signals which do not exceed roughly 1.5 K in the arctic winter (maximum) or 0.2-0.3 K on a global average.