Atmospheric response to the North Atlantic Ocean variability on seasonal to decadal time scales

The NCEP twentieth century reanalyis and a 500-year control simulation with the IPSL-CM5 climate model are used to assess the influence of ocean-atmosphere coupling in the North Atlantic region at seasonal to decadal time scales. At the seasonal scale, the air-sea interaction patterns are similar in the model and observations. In both, a statistically significant summer sea surface temperature (SST) anomaly with a horseshoe shape leads an atmospheric signal that resembles the North Atlantic Oscillation (NAO) during the winter. The air-sea interactions in the model thus seem realistic, although the amplitude of the atmospheric signal is half that observed, and it is detected throughout the cold season, while it is significant only in late fall and early winter in the observations. In both model and observations, the North Atlantic horseshoe SST anomaly pattern is in part generated by the spring and summer internal atmospheric variability. In the model, the influence of the ocean dynamics can be assessed and is found to contribute to the SST anomaly, in particular at the decadal scale. Indeed, the North Atlantic SST anomalies that follow an intensification of the Atlantic meridional overturning circulation (AMOC) by about 9 years, or an intensification of a clockwise intergyre gyre in the Atlantic Ocean by 6 years, resemble the horseshoe pattern, and are also similar to the model Atlantic Multidecadal Oscillation (AMO). As the AMOC is shown to have a significant impact on the winter NAO, most strongly when it leads by 9 years, the decadal interactions in the model are consistent with the seasonal analysis. In the observations, there is also a strong correlation between the AMO and the SST horseshoe pattern that influences the NAO. The analogy with the coupled model suggests that the natural variability of the AMOC and the gyre circulation might influence the climate of the North Atlantic region at the decadal scale.     

Detecting abrupt climate changes on different time scales

Two concepts are introduced for detecting abrupt climate changes. In the first case, the sampling frequency of climate data is high as compared to the frequency of climate events examined. The method is based on a separation of trend and noise in the data and is applicable to any dataset that satisfies some mild smoothness and statistical dependence conditions for the trend and the noise, respectively. We say that an abrupt change occurs when the first derivative of the trend function has a discontinuity and the task is to identify such points. The technique is applied to Northern Hemisphere temperature data from 1850 to 2009, Northern Hemisphere temperature data from proxy data, a.d. 200?C1995 and Holocene ??¹⁸O values going back to 11,700 years BP. Several abrupt changes are detected that are, among other things, beneficial for determining the Medieval Warm Period, Little Ice Age and Holocene Climate Optimum. In the second case, the sampling frequency is low relative to the frequency of climate events studied. A typical example includes Dansgaard?COeschger events. The methodology used here is based on a refinement of autoregressive conditional heteroscedastic models. The key element of this approach is the volatility that characterises the time-varying variance, and abrupt changes are defined by high volatilities. The technique applied to ??¹⁸O values going back to 122,950 years BP is suitable for identifying DO events. These two approaches for the two cases are closely related despite the fact that at first glance, they seem quite different.     

A study of atmosphere-ocean predictability on long time scales

 Two different methods, one diagnostic and the other prognostic, are used to investigate the predictability of the coupled atmosphere-ocean system on time scales of months to decades. The diagnostic approach analyzes the output of a 200 year coupled model simulation for evidence that the long time scale variability of annual, pentadal, and decadal means of surface air temperature are “potentially predictable”. The prognostic “perfect model” approach analyzes predictability of the system by calculating the rate of separation of a small set of coupled model simulations in terms of monthly and annual means of surface air temperature over the globe. Both approaches give reasonably coherent results. At the shorter of the time scales considered, there is little predictability over land but some evidence of predictability over the tropical Pacific, the Southern Ocean and to some extent the extratropical northern Pacific. At longer time scales there is some slight evidence of predictability over certain land areas and more definite evidence of predictability in the tropical Pacific and Southern Ocean. A new area of predictability emerges in the tropical Atlantic. There are plausible physical mechanisms which may explain these long time scale areas of predictability. Both approaches have the potential of shedding light on the long time scale predictability of the coupled system in more extensive predictability studies of this kind. Received: 24 September 1998 / Accepted: 8 October 1999     

Potential predictability of the Asian summer monsoon on monthly and seasonal time scales

Summary ¶The potential predictability of the monthly and seasonal means during the Northern Hemisphere summer and winter is studied by estimating the signal-to-noise ratio. Based on 33 years of daily low-level wind observations and 24 years of satellite observations of outgoing long wave radiation, the predictability of the Asian summer monsoon region is contrasted with that over other tropical regions. A method of separating the contributions from slowly varying boundary forcing and internal dynamics (e.g., intraseasonal oscillations) that determine the predictability of the monthly mean tropical climate is proposed. We show that the Indian monsoon climate is only marginally predictable in monthly time scales as the contribution of the boundary forcing in this region is relatively low and that of the internal dynamics is relatively large. It is shown that excluding the Indian monsoon region, the predictable region is larger and predictability is higher in the tropics during northern summer. Even though the boundary forced variance is large during northern winter, the predictable region is smaller as the internal variance is larger and covers a larger region during northern winter (due to stronger intraseasonal activity). Consistent with the estimates of predictability of monthly means, estimates of potential predictability on seasonal time scales also indicate that predictability of seasonal mean Indian monsoon is limited.Received December 6, 2002; accepted March 16, 2003 Published online: June 12, 2003     

Observed spatiotemporal characteristics of drought on various time scales over the Czech Republic

This paper analyses the observed spatiotemporal characteristics of drought in the Czech Republic during the growing season (April to September) as quantified using the Standardised Precipitation Evapotranspiration Index (SPEI) on various time scales. The SPEI was calculated for various lags (1, 3, 6, 12, and 24 months) from monthly records of mean temperature and precipitation totals using a dense network of 184 climatological stations for the period 1961–2010. The characteristics of drought were analysed in terms of the temporal evolution of the SPEI, the frequency distribution and duration of drought at the country level, and for three regions delimited by station altitude. The driest and the wettest years during the growing season were identified. The frequency distribution of the SPEI values for seven drought category classes (in per cent) indicates that normal moisture conditions represent approximately 65 % of the total SPEI values for all time scales in all three regions, whereas moderate drought and moderate wet conditions are almost equally distributed around 10.5 %. Differences in extremely dry conditions (5 %) compared with extremely wet conditions (1.5 %) were observed with increasing SPEI time scales. The results of the non-parametric Mann–Kendall trend test applied to the SPEI series indicate prevailing negative trends (drought) at the majority of the stations. The percentage of stations displaying a significant negative trend for the 90, 95, 99, and 99.9 % confidence levels is approximately 40 %. An Empirical Orthogonal Functions (EOF) analysis was used to identify the principal patterns of variability of the SPEI during the growing season that accounted for the highest amount of statistical variance. The variance explained by the leading EOF range 66 to 56 %, whereas for EOF2 and EOF3, the value is between 7 and 11 % and between 4 and 7 %, respectively, for the SPEI is calculated for 1- to 24-month lags.     

Detecting trends that are nonlinear and asymmetric on diurnal and seasonal time scales

Trends in climate time series are often nonlinear and temporally-asymmetric, i.e. the trend is different for different seasons and/or hours of the day. Here a method is developed that allows the nonlinearity and temporal asymmetry of a trend to be investigated simultaneously. First, nonlinear trend components are extracted from a univariate time series, by adapting a nonparametric dimension-reduction method. Then, the nonlinear trend components are substituted into a regression model in which the periodic mean component and the periodic variation in the amplitude of the nonlinear trend are modeled using harmonic functions of the seasonal and diurnal periods. Third, trend patterns in the positive and negative anomalies are investigated, by extending the nonlinear trend model using indicator variables. Fourth, a non-local inferential test is developed to test the statistical significance of the trend patterns. The nonlinear trend model is applied to a simulated time series, as well as to long-term high-resolution temperature records from five Southern Hemisphere sites: Lucas Heights, Sydney Airport, Cape Grim, Macquarie Island and Law Dome. Our method should be generally useful for identifying the effect of both climate-related factors and observation/site-related factors on seasonal and diurnal trends in meteorological data series.     

Climate predictability on interannual to decadal time scales: the initial value problem

Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by "chaos" in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a "perfect model" approach whereby small perturbations are made to a coupled atmosphere-ocean general circulation model and the spread of nearby model trajectories, on time and space scales appropriate to seasonal-decadal climate variability, is measured to assess the lead time at which the error saturates. The study therefore represents an estimate of the upper limit of the predictability of climate (appropriate to the initial value problem) given a perfect model and near perfect knowledge of the initial conditions. It is found that, on average, surface air temperature anomalies are potentially predictable on seasonal to interannual time scales in the tropical regions and are potentially predictable on decadal time scales over the ocean in the North Atlantic. For mid-latitude surface air temperature anomalies over land, model trajectories rapidly diverge and there is little sign of any potential predictability on time scales greater than a season or so. For mean sea level pressure anomalies, there is potential predictability on seasonal time scales in the tropics, and for some global scale annual-decadal anomalies, although not those associated with the North Atlantic Oscillation. For precipitation, the only potential for predictability is for seasonal time anomalies associated with the El-Niño Southern Oscillation. For the majority of the highly populated regions of the world, climate predictability on interannual to decadal time scales based in the initial value approach is likely to be severely limited by chaotic error growth. It is found however that there can be cases in which the potential predictability can be higher than average indicating that there is perhaps some utility in making initial value forecasts of climate in those regions which show low predictability on average.     

Modelling the variability of midlatitude storm activity on decadal to century time scales

The output of several multi-century simulations with a coupled ocean–atmosphere general circulation model is examined with respect to the variability of global storm activity in winter on time scales of decades and longer. The frequency of maximum wind speed events within a grid box, using the lower limits on the Beaufort wind speed scale of 8 and 10 Bft as thresholds, is taken as the characteristic parameter. Two historical climate runs with time-dependent forcing of the last five centuries, one control simulation, and three climate change experiments are considered. The storm frequency shows no trend until recently. Global maps for the industrially influenced period hardly differ from pre-industrial maps, even though significant temperature anomalies temporarily emerge in the historical runs. Two indicators describing the frequency and the regional shift of storm activity are determined. In historical times they are decoupled from temperature. Variations in solar and volcanic forcing in the historical simulations as well as in greenhouse gas concentrations for the industrially influenced period are not related to variations in storm activity. Also, anomalous temperature regimes like the Late Maunder Minimum are not associated with systematic storm conditions. In the climate change experiments, a poleward shift of storm activity is found in all three storm track regions. Over the North Atlantic and Southern Ocean, storm activity increases, while it decreases over the Pacific Ocean. In contrast to the historical runs, and with the exception of the North Pacific storm frequency index, the storm indices parallel the development of temperature, exceeding the 2 σ-range of pre-industrial variations in the early twenty-first century.     

基于组网观测的那曲土壤湿度不同时间尺度的变化特征

利用第三次青藏高原大气科学试验的土壤湿度观测数据,分析了那曲多空间尺度组网观测的28个站2、5、10、20和30 cm 5个不同深度土壤湿度的季节变化和日变化特征,并对比讨论了土壤湿度站点间的差异。分析表明,各层土壤湿度均存在显著的季节变化。冬春季节,20 cm以上土壤湿度随深度变浅而减小。夏秋季节土壤湿度随深度增加而减小,并分别在7月上、中旬和9月出现两个峰值。10月以后进入土壤湿度衰减期。土壤温度和土壤湿度存在协同变化关系。在一定的温度范围内,土壤发生冻结-融化过程,引起土壤湿度变化。在太阳辐射加热下,土壤表层水分蒸发,进而影响土壤温度。不同观测站间土壤湿度差异较大,夏秋季离散性大于冬春季。不同季节土壤湿度的日变化存在差异。春季10 cm以上土壤湿度日变化明显,08-10时（北京时）达到最低,19-20时达到最高。夏季土壤湿度日变化较为平缓。秋季2 cm深度土壤湿度日变化明显。线性拟合结果表明,1、4、10月土壤湿度和土壤温度为正相关关系。但是在夏季,土壤湿度与土壤温度为负相关。站点间土壤湿度变化的离散性表明,多测站才能全面体现青藏高原某区域的陆面状态。文中结果为青藏高原地区土壤湿度卫星参数验证和数值模式参数化提供了多角度的观测依据。      

中国不同时间尺度地表太阳总辐射估算研究

利用全国95个气象站点逐日地表太阳总辐射和日照时数资料,通过最小二乘法拟合回归建立地表太阳总辐射气候学计算模型。通过对比分析以日值和月值为起点的地表太阳总辐射计算模型的精度,确定了全国不同省份和区域的不同时间尺度（月、季节、生长季和年）地表太阳总辐射计算模型,并探讨了经验系数a、b值的分布及变化特征。结果表明,以日值和月值为起点建立的月、四季、生长季和年地表太阳总辐射计算模型精度无显著性差异,相对误差均低于8.5%,但以日值为起点的计算模型a、b值变异性更小。在以日值为起点建立计算模型的前提下,全国各地a、b值自西北部向南部减小,且从四季到生长季再到年尺度,随着时间尺度增大,a、b值振幅减小。根据不同省份年地表太阳总辐射计算模型经验系数a、b值,全国可划分为新甘蒙地区、青藏高原地区和中东部地区3个区域,分别确定了每个区域四季、生长季和年尺度下地表太阳总辐射计算模型。各区域不同时间尺度地表太阳总辐射计算模型均通过了显著性检验（p<0.01）,其中青藏高原地区和新甘蒙地区模型相对误差低于8.0%,模拟精度较高。     

不同时间尺度上黄淮地区夏季降水异常成因及预测研究

利用1961—2006年中国降水资料、NCEP/NCAR再分析资料、NOAA海表温度资料,分析了黄淮地区夏季降水的年代际和年际变化特征,研究了不同时间尺度上降水异常成因。结果表明,年代际尺度上,当太平洋年代际振荡处在暖(冷)位相时,南方涛动偏弱(强),黄淮地区夏季降水偏多(少)。在年际及以下尺度上,当印度洋北部海温偏高、南部偏低时,500 hPa位势高度场上,中高纬乌拉尔山以东和鄂霍次克海附近出现明显的双阻塞高压,副热带高压偏强;200 hPa风场上,西风急流略偏南,黄淮流域上空西风偏强,为反气旋环流;850 hPa风场上,黄淮流域上空出现西南—东北风的切变,使得急流出口区右侧次级环流的异常上升支恰好位于黄淮流域上空,高低空环流的这种配置导致了黄淮流域上空降水偏多。进一步分析发现,利用印度洋海温作为预测因子,建立预测模型,对黄淮流域降水年际变率有较高的预测能力。     

Validating a rapid-update satellite precipitation analysis across telescoping space and time scales

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day^?1.     

Seasonality and time scales in the relationship between global SST and African rainfall

The main goal of this study is to determine the oceanic regions corresponding to variability in African rainfall and seasonal differences in the atmospheric teleconnections. Canonical correlation analysis (CCA) has been applied in order to extract the dominant patterns of linear covariability. An ensemble of six simulations with the global atmospheric general circulation model ECHAM4, forced with observed sea surface temperatures (SSTs) and sea ice boundary variability, is used in order to focus on the SST-related part of African rainfall variability. Our main finding is that the boreal summer rainfall (June–September mean) over Africa is more affected by SST changes than in boreal winter (December–March mean). In winter, there is a highly significant link between tropical African rainfall and Indian Ocean and eastern tropical Pacific SST anomalies, which is closely related to El Niño-Southern Oscillation (ENSO). However, long-term changes are found to be associated with SST changes in the Indian and tropical Atlantic Oceans, thus, showing that the tropical Atlantic plays a critical role in determining the position of the intertropical convergence zone (ITCZ). Since ENSO is less in summer, the tropical Pacific and the Indian Oceans are less important for African rainfall. The African summer monsoon is strongly influenced by SST variations in the Gulf of Guinea, with a response of opposite sign over the Sahelian zone and the Guinean coast region. SST changes in the subtropical and extratropical oceans mostly take place on decadal time scales and are responsible for low-frequency rainfall fluctuations over West Africa. The modelled teleconnections are highly consistent with the observations. The agreement for most of the teleconnection patterns is remarkable and suggests that the modelled rainfall anomalies serve as suitable predictors for the observed changes.     

Kinetic energy exchanges between the time scales of ENSO and the Pacific decadal oscillation

This paper examines the non-linear kinetic energy interactions of the atmospheric ENSO and decadal oscillations over the Pacific. The calculations are based on a 54-year dataset of tropospheric winds from NCEP reanalysis. We verify that the decadal oscillations have two dominant modes, corresponding well to the pentadecadal and bidecadal modes reported in the literature. Energy interactions involving the range of decadal oscillations and the range of ENSO oscillations are considered in the context of kinetic energy exchanges in the frequency domain. We quantify the relative amplitudes and spatial structures of the quadratic and triplet terms of the kinetic energy exchanges over the Pacific and conclude that quadratic interactions with the mean flow are the dominant term in the kinetic-to-kinetic energy exchanges. Additionally, we show that triplet interactions provide a non-trivial contribution to the total. The interactions between the range of decadal oscillations and the range of ENSO oscillations are found to be the strongest near the regions of maximum oscillation amplitude and of the maximum oscillation amplitude gradient. Due to their similar spatial structures, the two dominant ENSO modes and the bidecadal mode are found to interact in a resonant way. The interactions among the range of ENSO modes and the range of decadal modes are found to strengthen the ENSO modes in the equatorial, subtropical and midlatitude belts, and to weaken the decadal modes in all but the equatorial belt.     

Spatiotemporal patterns of drought at various time scales in Shandong Province of Eastern China

The temporal variations and spatial patterns of drought in Shandong Province of Eastern China were investigated by calculating the standardized precipitation evapotranspiration index (SPEI) at 1-, 3-, 6-, 12-, and 24-month time scales. Monthly precipitation and air temperature time series during the period 1960–2012 were collected at 23 meteorological stations uniformly distributed over the region. The non-parametric Mann-Kendall test was used to explore the temporal trends of precipitation, air temperature, and the SPEI drought index. S-mode principal component analysis (PCA) was applied to identify the spatial patterns of drought. The results showed that an insignificant decreasing trend in annual total precipitation was detected at most stations, a significant increase of annual average air temperature occurred at all the 23 stations, and a significant decreasing trend in the SPEI was mainly detected at the coastal stations for all the time scales. The frequency of occurrence of extreme and severe drought at different time scales generally increased with decades; higher frequency and larger affected area of extreme and severe droughts occurred as the time scale increased, especially for the northwest of Shandong Province and Jiaodong peninsular. The spatial pattern of drought for SPEI-1 contains three regions: eastern Jiaodong Peninsular and northwestern and southern Shandong. As the time scale increased to 3, 6, and 12 months, the order of the three regions was transformed into another as northwestern Shandong, eastern Jiaodong Peninsular, and southern Shandong. For SPEI-24, the location identified by REOF1 was slightly shifted from northwestern Shandong to western Shandong, and REOF2 and REOF3 identified another two weak patterns in the south edge and north edge of Jiaodong Peninsular, respectively. The potential causes of drought and the impact of drought on agriculture in the study area have also been discussed. The temporal variations and spatial patterns of drought obtained in this study provide valuable information for water resources planning and drought disaster prevention and mitigation in Eastern China.     

Northern hemisphere atmospheric response to changes of atlantic ocean SST on decadal time scales: a GCM experiment

Analyses indicate that the Atlantic Ocean seasurface temperature (SST) was considerably colder at the beginning than in the middle of the century. In parallel, a systematic change in the North Atlantic sea-level pressure (SLP) pattern was observed. To find out whether the SST and SLP changes analyzed are consistent, which would indicate that the SST change was real and not an instrumental artifact, a response experiment with a low-resolution (T21) atmospheric GCM was performed. Two perpetual January simulations were conducted, which differ solely in the Atlantic Ocean (40° S-60° N) SST: the cold simulation utilizes the SSTs for the period 1904–1913; the warm simulation uses the SSTs for the period 1951–1960. Also, a control run with the model's standard SST somewhat between the cold and warm SST was made. For the response analysis, a rigorous statistical approach was taken. First, the null hypothesis of identical horizontal distributions was subjected to a multivariate significance test. Second, the level of recurrence was estimated. The multivariate statistical approaches are based on hierarchies of test models. We examined three different hierarchies: a scale-dependent hierarchy based on spherical harmonics (S), and two physically motivated ones, one based on the barotropic normal modes of the mean 300 hPa flow (B) and one based on the eigenmodes of the advection diffusion operator at 1000 hPa (A). The intercomparison of the cold and warm experiments indicates a signal in the geostrophic stream function that in the S-hierarchy is significantly nonzero and highly recurrent. In the A-hierarchy, the low level temperature field is identified as being significantly and recurrently affected by the altered SST distribution. The SLP signal is reasonably similar to the SLP change observed. Unexpectedly, the upper level stream-function signal does not appear to be significantly nonzero in the B-hierarchy. If, however, the pairs of experiments warm versus control and cold versus control are examined in the B-hierarchy, a highly significant and recurrent signal emerges. We conclude that the cold versus warm response is not a small disturbance that would allow the signal to be described by eigenmodes of the linear system. An analysis of the three-dimensional structure of the signal leads to the hypothesis that two different mechanisms are acting to modify the model's mean state. At low levels, local heating and advection are dominant, but at upper levels the extratropical signal is a remote responce to modifications of the tropical convection.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dilmenil.AWI Publication no. 254     

用动力模式做热带气旋的季节内到百年预测与预估的评估

正热带气旋对社会经济和人类生命财产有明显的影响,因此做热带气旋的预测有重要的意义~([1-2])。热带气旋的预测从时间尺度为小时和日的预报到百年的预估,空间尺度从全球到热带气旋经常发生的局地区域,预测的方法主要是诊断分析+统计方法,动力模式,以及统计+动力模式。热带气旋预测的内容包括生成位置(源地),个数(年,季,月),强度(中心气压、风速、降水量、海浪),移动路径,影响范围,生成与消亡时间(持续时间);国外还经