波与波的相互作用对阻塞高压的形成、维持和衰退的影响

采用波数域的动能方程和定常波与瞬变波的波数域动能方程计算了阻塞高压形成、维持和衰退阶段的波与波相互作用项和波与平均运动相互作用项。计算结果表明波与波的非线性相互作用项在阻塞高压形成阶段是促使阻塞高压形成的能源,在维持阶段,是维持其稳定的能源,在衰退阶段又是促使其崩溃的能汇。波与平均运动的相互作用项在衰退阶段也起着促使阻塞高压崩溃的重要作用。定常波与瞬变波的非线性相互作用起着在具体时段使阻塞高压形成、维持和衰退的重要作用。     

Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model

The subject of change detection in climate time series has recently received greater interest as the perception of a human-induced change in the climate is now widely accepted. However, changes in regional precipitation and temperature remain uncertain. This study characterizes projected fine-scale changes in precipitation and temperature in continental Southeast Asia over the period 1960?C2049. Twenty four annual variables were derived from grid-based daily precipitation and temperature produced by the PRECIS regional climate model under A2 and B2 scenarios. These time series, capturing precipitation intensities (classified as low, medium and high), seasonality and extremes in precipitation and temperature, were subjected to the modified Mann-Kendall trend detection test accounting for long-term persistence. The results indicate that temperature increases over the whole region with steeper trends in higher latitudes. Increases in annual precipitation, mainly restricted to Myanmar and the Gulf of Thailand, result from increases in high precipitation during the wet season. Decreases are observed mainly over the sea and caused by a reduction of low precipitation. Changes in the occurrence of the monsoon affect the low-latitude sea areas only. By showing that significant precipitation change are minor over land areas, these results challenge most of the previous studies that suggested significant precipitation changes over Southeast Asia, often mixing up multi-decadal variability and long-term unidirectional trends. Significant changes in precipitation and temperature may induce higher agricultural yields as steepest temperature and precipitation increases will predominantly affect the coldest and driest land areas of the region.     

Impact of land-atmospheric coupling in CFSv2 on drought prediction

Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land–atmosphere–ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land–atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land–atmosphere interactions we analyze National Centers for Environmental Prediction’s Climate Forecast System Version 2 (CFSv2) in terms of its climatological land–atmosphere interactions. To do this we use a recently developed classification of land–atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982–2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.     

Persistence of Summer Sea Surface Temperature Anomalies in the Midlatitude North Pacific and Its Interdecadal Variability

The present study investigates the persistence of summer sea surface temperature anomalies(SSTAs) in the midlatitude North Pacific and its interdecadal variability. Summer SSTAs can persist for a long time(approximately 8–14 months)around the Kuroshio Extension(KE) region. This long persistence may be strongly related to atmospheric forcing because the mixed layer is too shallow in the summer to be influenced by the anomalies at depths in the ocean. Changes in atmospheric circulation, latent heat flux, and longwave radiation flux all contribute to the long persistence of summer SSTAs. Among these factors, the longwave radiation flux has a dominant influence. The effects of sensible heat flux and shortwave radiation flux anomalies are not significant. The persistence of summer SSTAs displays pronounced interdecadal variability around the KE region, and the variability is very weak during 1950–82 but becomes stronger during 1983–2016. The changes in atmospheric circulation, latent heat flux, and longwave radiation flux are also responsible for this interdecadal variability because their forcings on the summer SSTAs are sustained for much longer after 1982.     

Study on the “winter persistence barrier” of Indian Ocean dipole events using observation data and CMIP5 model outputs

This study investigates the persistence barrier phenomenon associated with positive Indian Ocean dipole (IOD) events during the various phases of its development. The results derived from three observational datasets (the Simple Ocean Data Assimilation, International Comprehensive Ocean–Atmosphere Data Set, and Extended Reconstructed Sea Surface Temperature) indicate that significant winter persistence barriers (WPBs) occur during IOD events, both in its growing and decaying phases. The simulation skill of the 14 models within the Coupled Model Intercomparison Project Phase 5 with respect to persistence barriers was also evaluated and compared with observational data. The results show that although most models were able to simulate the WPB reasonably well during the growing phase, only five models could capture the appropriate WPB during the decaying phase. Further analysis demonstrates that the zonal equatorial gradient of climatological sea surface temperature (SST) and zonal sea surface winds at the equator in the Indian Ocean are very weak in winter, which indicates that the coupling between ocean and atmosphere is weakest in winter and encourages a rapid variation of IOD events and a swift reduction of persistence, favoring the occurrence of WPBs; furthermore, a deep climatological thermocline in winter implies that the subsurface water temperature cannot influence SST readily, and the memory of the subsurface temperature cannot help SST to recover from the loss of persistence during this period, leading to the occurrence of WPBs. In addition, an analysis of the climatological conditions in the outputs from the 14 models shows that those models that can (cannot) capture the winter climatological conditions frequently simulate the WPBs appropriately (poorly). This confirms that the occurrence of the WPB for IOD events may be closely related to particular winter climatological conditions, indicating that the WPB is an inherent property of IOD events.     

Skew eddy fluxes as signatures of non‐linear tidal current interactions,with application to Georges Bank

Abstract

The eddy flux of a conservative scalar in a time‐dependent rotary velocity field may have a component that is normal to the scalar gradient. This component is the “skew flux”, which consists of the scalar transport by the Stokes velocity and a part that is always non‐divergent (and hence does not affect scalar evolution). Since tidal velocity fields usually have rotary features, tidal‐band eddy scalar fluxes may include a skew component that can be useful in indicating the occurrence of non‐linear current interactions.

The skew temperature flux associated with the semidiurnal tide in a continental shelf region is demonstrated using simple models, and moored current and temperature observations from Georges Bank. The observed fluxes on the Bank are largely directed along isobaths, with apparent contributions from the topographic rectification of the barotropic tidal current over the Bank's side and from the rotary tidal ellipses in a frontal region. Simple models indicate that the weaker cross‐isobath fluxes can arise through the influence of frictionally induced vertical structure on topographic tidal rectification, a baroclinic tidal current interaction, or the interaction of baroclinic and barotropic tidal currents. In some cases, the simple models show qualitative agreement with the observed fluxes and currents but, in general, more realistic models and better estimates of the background mean temperature field are required to obtain quantitative estimates of the relative importance of these interactions and other processes. Nevertheless, the observations and models suggest that non‐linear interactions involving both barotropic and baroclinic tidal currents are occurring on Georges Bank.     

THE UNDERSTANDING OF ENSO CYCLE MECHANISM AND ENSO POTENTIAL PREDICTION ABILITY*

In recent years,the dynamic coupled models of ocean-atmosphere and statistical models have been used in routine operation for issuing long-lead forecasts.The dynamic coupled models consist of models with varying degrees of complexity,ranging from simplified coupled models of the shallow water to coupled general circulation models.During the period of 1980-1992,some models performed considerably better than the persistence forecast on predicting typical indices of ENSO for lead time of 6 to 12 months.It seems that ENSO is predictable at least one year in advance.However.nearly all the models have lost their skill of forecasting sea surface temperature(SST)changes in the eastern equatorial Pacific since 1992.It is a challenge not only to the dynamic models but also to the understanding of the ENSO cycle mechanism.This paper examines multiple time-space scales of the ocean-atmosphere interactions and potential prediction ability of ENSO event by using data analysis and model study.     

Possible causes for the persistence barrier of SSTA in the South China Sea and the vicinity of Indonesia

The persistence barrier refers to the lag correlation of sea surface temperature anomalies (SSTA) showing a rapid and significant decline in a specific season, regardless of the starting month. This implies that there is a decrease in forecast skill for SSTA in this specific season. This paper investigates the possible causes for the persistence barrier of SSTA in the South China Sea (SCS) and its adjacent regions from the perspective of interannual-interdecadal time scales. The results show that the persistence barrier of SSTA exists not only in the SCS, but also in the vicinity of Indonesia south of the equator. The SCS barrier occurs around October--November, while the occurrence of the barrier in the Indonesia region is around November--December. For these two regions, the occurrence of the persistence barrier is closely associated with the interdecadal variability of SSTA, as well as the interannual variability. The persistence barriers in the SCS and the Indonesia region do not exist alone if the interdecadal variability is not considered, because SSTA have a short memory of less than 4 months, regardless of the starting month. Moreover, the influence of the interdecadal variability of SSTA on the persistence barrier of SSTA in the SCS and the Indonesia region may be associated with SSTA in the Indian Ocean and the western Pacific, but is not closely associated with the Pacific Decadal Oscillation. However, compared with the spring persistence barrier (SPB) of ENSO, the close relationship between the persistence barriers in the SCS and the Indonesia region and the interdecadal variability is unique, since the ENSO SPB is not significantly affected by such variability. In addition, although the persistence barriers in both the SCS and the Indonesia region are quite obvious in strong ENSO cases, the interdecadal variability of SSTA also plays a non-negligible role in this relationship.     

A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the Australian region

 The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch between GCMs and climate impact models which need information on spatial scales that the GCMs cannot provide. The method of statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this case between synoptic scale mean sea level pressure (MSLP) fields and local temperature extremes at several stations in southeast Australia. In this study it was found that most of the day-to-day spatial variability of the synoptic circulation over the Australian region can be captured by six principal components. Using the scores of these PCs as multivariate indicators of the circulation a substantial part of the local daily temperature variability could be explained. The inclusion of temperature persistence noticeably improved the performance of the statistical model. The model established and tested with observations is thought to be finally applied to GCM-simulated pressure fields in order to estimate pressure-related changes in local temperature extremes under altered CO₂ conditions. Received: 26 March 1996 / Accepted: 20 September 1996     

Experiments of Reconstructing Discrete Atmospheric Dynamic Models from Data (I)

In this paper, we give some experimental results of our study in reconstructing discrete atmospheric dynamic models from data. After a great deal of numerical experiments, we found that the logistic map, xn +1= 1-uxn2 could be used in monthly mean temperature prediction when it was approaching the chaotic region, and its predictive results were in reverse states to the practical data. This means that the nonlinear developing behavior of the monthly mean temperature system is bifurcating back into the critical chaotic states from the chaotic ones.     

Testing for nonlinearity in European climatic time series by the method of surrogate data

Summary Using temperature and pressure records from Czech meteorological stations and NCEP/NCAR reanalysis series, we tested for the presence of detectable nonlinearity in univariate and multivariate climatic time series. The method of surrogate data was utilized for nonlinearity detection – results of nonlinear prediction for the original series were compared to the results for series whose nonlinear structure was randomized. The prediction was done by means of local linear models in the reconstructed phase space. None or very weak nonlinearity was found in the single (univariate) series, and pressure series generally exhibited stronger nonlinearity than series of temperature (daily mean, minimum or maximum). Distinct nonlinearity was found in all tested multivariate systems, especially when both temperatures and pressures were used simultaneously to form the phase space. Nonlinearity tests were carried out for 30-year and 10-year-long datasets and nonlinear behavior was generally more apparent in the longer versions. In addition, the tested systems showed more substantial nonlinearity when the success of short-range prediction was used as the discriminating statistic; with an increase of the prediction time, detectable nonlinearity became weaker and it disappeared completely for long-term prediction.     

CLIMATE CHANGE: LONG-TERM TRENDS AND SHORT-TERM OSCILLATIONS

Identifying the Northern Hemisphere (NH) temperature reconstruction and instrumental data for the past 1000 years shows that climate change in the last millennium includes long-term trends and various oscillations. Two long-term trends and the quasi-70-year oscillation were detected in the global temperature series for the last 140 years and the NH millennium series. One important feature was emphasized that temperature decreases slowly but it increases rapidly based on the analysis of different series. Benefits can be obtained of climate change from understanding various long-term trends and oscillations. Millennial temperature proxies from the natural climate system and time series of nonlinear model system are used in understanding the natural climate change and recognizing potential benefits by using the method of wavelet transform analysis. The results from numerical modeling show that major oscillations contained in numerical solutions on the interdecadal timescale are consistent with that of natural proxies. It seems that these oscillations in the climate change are not directly linked with the solar radiation as an external forcing. This investigation may conclude that the climate variability at the interdecadal timescale strongly depends on the internal nonlinear effects in the climate system.     

月平均环流异常的持续性与韵律性和海气相互作用

首先,讨论了环流异常的形成与海气相互作用的关系。指出在明显洋流区,尤其是暖洋流区,海洋为热源,对环流异常的形成有重要影响。在大洋中部的非洋流区,更大程度上是海洋热状况受大气环流控制。 其次,研究了环流异常的持续性。发现中纬度3—4月及9—10月有两次持续性的最低点,并且指出,这与冷热源的月际变化在春秋期间最大有关。但在低纬度,尤其在大洋上的暖洋流区,一年中只有一次持续性最低点,发生在夏末秋初。分析表明,这与那里冷热源强度的年变程有关。 最后,根据相似分析,讨论了环流及海温异常变化的韵律性问题。发现当两个月的环流异常相似时,以后并不是随着时间的推移相似性愈来愈小,而是在相隔4—5个月及9—11个月时相似性较大。这种大气与海洋的韵律活动在长期预报中有重要作用。     

A novel deep neural network model approach to predict Indian Ocean dipole and Equatorial Indian Ocean oscillation indices

Indian Ocean Dipole (IOD) and Equatorial Indian Ocean oscillation (EQUINOO) are important climatic system oscillation events in the Indian Ocean region that affects the Indian summer monsoon rainfall (ISMR). The prime focus of this study is to deliberate the influence of these events on ISMR and an attempt has been made to predict these events for future time scales using a Long short term memory (LSTM) deep learning model. LSTM is a special kind of recurrent neural network (RNN) which specializes in learning long-term dependencies and extracting important features. The features learnt by the model is then ranked using correlational analysis (linear and nonlinear). This approach helps in selecting decisive and imperative set of relevant predictors, which can be employed to predict IOD and EQUINOO. Nonlinear correlational identified predictors are found to forecast with greater precision as to their linear counterparts. The model-calibrated correlation coefficient for IOD and for EQUNIOO was 0.90 and 0.88 respectively at a lead of 5 months. Our proposed model was observed to work at par with the other existing models in terms of various statistical evaluation measures.     

The absence of memory in the climatic forcing of glaciers

Glaciers respond to both long-term, persistent climate changes as well as the year-to-year variability that is inherent to a constant climate. Distinguishing between these two causes of length change is important for identifying the true climatic cause of past glacier fluctuations. A key step in addressing this is to determine the relative importance of year-to-year variability in climate relative to more persistent climate fluctuations. We address this question for European climate using several long-term observational records: a century-long, Europe-wide atmospheric gridded dataset; longer-term instrumental measurements of summertime temperature where available (up to 250 years); and seasonal and annual records of glacier mass balance (between 30 and 50 years). After linear detrending of the datasets, we find that throughout Europe persistence in both melt-season temperature and annual accumulation is generally indistinguishable from zero. The main exception is in Southern Europe where a degree of interannual persistence can be identified in summertime temperatures. On the basis of this analysis, we conclude that year-to-year variability dominates the natural climate forcing of glacier fluctuations on timescales up to a few centuries.     

数值天气预报和气候预测可预报性研究的若干动力学方法

简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。     

夏季地面温度场月预报的统计模式

用15年北半球夏季地面温度的资料分析了月平均场与该月前后一周左右的逐日平均场在时间和波数上的相关。发现越新、越靠后的日平均场与该月的平均场相关越强。用第0天外推的未来30天平均场与实况的相关比用前30天平均场作外推的相关要显著得多。由此得出结论认为各种预报方法的效果检验应与前者定义的惯性预报作比较。文中提出了几个用于作月预报的统计模式，分别用不同时间或不同波数作预报因子。其结果均使预报准确率有所提高。既用不同时间又用不同波数作预报因子的模式更为理想。文中最后讨论了一个源于动力学考虑的统计模式。近1400个月预报例子的结果与实况的平均相关系数高达0.75，显著优于惯性预报。     

延庆-张家口地区复杂地形冬季山谷风特征分析

基于2016年12月—2017年2月和2017年12月—2018年2月两年冬季的近地面自动气象站逐时观测数据以及张家口探空数据分析延庆-张家口一带（包括张家口崇礼、赤城、海坨、小五台山区,延怀、怀涿、洋河、蔚县盆地以及北京延庆、昌平、怀柔部分平原地区）复杂地形的风场精细化时、空分布特征,揭示不同复杂地形下局地风场的时、空变化规律,加深对复杂地形动力、热力作用对近地面风场影响的认识,为冬季山区风场预报以及复杂地形数值模式改进提供参考。结果表明:晴朗小风天风持续性作为矢量平均风速和标量平均风速的比值,可以作为研究风场变化规律的重要参数。根据风持续性的日变化特征,可以将研究区域内所有站点分为10种类型,分别代表不同局地地形特征的影响,风持续与风向变化的相关也很强。研究区域主要有3种类型的地形风:斜坡风、峡谷风以及较大尺度的山区平原风。不同地形特征下的风场、风持续性存在明显不同的日变化特征,山风和谷风相互转化的时间也不同,山区最早,盆地次之,平原区最晚;山风时段持续时间较谷风时段长,风速小;晴朗小风天实测风反映了实际风场的特征,而排除环境背景风场,弱化地形动力作用后整个冬季的局地风作为理论山谷风,更能反映热力作用下的山谷风特征。      

Modeling olive-crop forecasting in Tunisia

Tunisia is the world’s second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia’s five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993–2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.     

An analysis of the difference between the multiple linear regression approach and the multimodel ensemble mean

An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression (MLR) ensemble and the simple multimodel ensemble mean (EM) was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nino3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.