Estimating the potential predictability of Australian surface maximum and minimum temperature

A study is made of the potential predictability of seasonal means in Australian surface maximum and minimum temperature using monthly data from December 1950 to November 2000. Because the usual assumption of stationarity cannot be applied to the observations at all stations and for all seasons, a modification to an existing methodology is proposed. Here, we show that, to a first order, monthly mean variances within a season can be modeled by a linear relationship, and inter-monthly correlations can be assumed to be stationary. The intraseasonal component of variability can then be estimated using monthly data. Removing the intraseasonal variance from the total interannual variance allows an estimate of the potential predictability to be made. Surface maximum and minimum temperature has high potential predictability over most of northern Australia in the four main seasons. However, there is high potential predictability only in some of the four seasons for the centre and south of Australia. Surface minimum temperature is generally more potentially predictable than surface maximum temperature. The spatial and temporal patterns of potential predictability are generally consistent with published patterns of hindcast skill from a statistical forecast scheme. A comparison between the intraseasonal variance of Australian surface maximum and minimum temperature estimated using the stationary variance assumption and the linear assumptions showed qualitatively and quantitatively similar patterns of distribution.     

Baseline predictability of daily east Asian summer monsoon circulation indices

The nonlinear local Lyapunov exponent (NLLE) method is adopted to quantitatively determine the predictability limit of East Asian summer monsoon (EASM) intensity indices on a synoptic timescale. The predictability limit of EASM indices varies widely according to the definitions of indices. EASM indices defined by zonal shear have a limit of around 7 days, which is higher than the predictability limit of EASM indices defined by sea level pressure (SLP) difference and meridional wind shear (about 5 days). The initial error of EASM indices defined by SLP difference and meridional wind shear shows a faster growth than indices defined by zonal wind shear. Furthermore, the indices defined by zonal wind shear appear to fluctuate at lower frequencies, whereas the indices defined by SLP difference and meridional wind shear generally fluctuate at higher frequencies. This result may explain why the daily variability of the EASM indices defined by zonal wind shear tends be more predictable than those defined by SLP difference and meridional wind shear. Analysis of the temporal correlation coefficient (TCC) skill for EASM indices obtained from observations and from NCEP’s Global Ensemble Forecasting System (GEFS) historical weather forecast dataset shows that GEFS has a higher forecast skill for the EASM indices defined by zonal wind shear than for indices defined by SLP difference and meridional wind shear. The predictability limit estimated by the NLLE method is shorter than that in GEFS. In addition, the June-September average TCC skill for different daily EASM indices shows significant interannual variations from 1985 to 2015 in GEFS. However, the TCC for different types of EASM indices does not show coherent interannual fluctuations.     

Potential predictability of dry and wet periods: Sicily and Elbe-Basin (Germany)

Summary The purpose of this paper is to evaluate a viable tool for the potential predictability of dry and wet spells. We select two regions in Europe that have distinct precipitation regimes: Sicily and Elbe basin (Germany). The analysis of dryness and wetness in these regions from 1951 to 2000 is based on the Standardized Precipitation Index (SPI) computed on a long-time scale (two years) and the evaluation of their time-space variability is carried out using Principal Component Analysis. Results suggest that periodicities ranging from 3.4 to 12 years characterise the SPI signals in both regions and essentially drive the main dry and wet occurrences. In Sicily, at variance with the Elbe basin, superimposed to this variability there is also a clearly detectable linear trend that is perhaps related to long-term periodicity. Moreover, the shift in phase found between the common periods implies that often on the longer time scale if the Elbe region has dry conditions, Sicily is wet and viceversa. The reconstruction of the SPI time series by considering the periodicity that greatly contribute to the total power spectrum variance gives good results and provides good opportunities for predictability.     

MJO对我国东部春季降水影响的分析

利用1982-2011年夏季（5-8月）中国气象观测站点逐日降水资料、NCEP/NCAR逐日再分析资料、NOAA逐日向外长波辐射和海表温度资料集,通过选取低频降水事件的方法,分析了华南夏季12-30 d持续性强降水事件的基本特征,然后利用位相合成法对持续性强降水期间伴随的低频大气环流型以及低频信号的来源和传播情况进行研究,同时也分析了低频海-气耦合过程对持续性强降水的影响。结果表明：（1）华南夏季降水具有显著的12-30 d低频振荡特征,持续性强降水事件在6月发生次数最多,低频降水期间的雨带自东南向西北传播。（2）在持续性强降水发生期间,华南及邻近海域低层受强大的低频气旋式环流控制,低频上升运动显著,而中国南海-菲律宾海一带则是强的低频反气旋式环流,其西侧向北的低频水汽输送不断将中国南海的水汽送至华南及邻近海域进行辐合上升。低层的低频信号来源于热带西太平洋和中国南海-菲律宾海一带低频振荡的西北向传播,同时伴随着西太平洋副热带高压明显的西伸东退过程。（3）在高层,华南北侧（22°-45°N,95°-130°E）区域强大的低频气旋式环流和孟加拉湾-中国南海一带的低频反气旋式环流相互配合,使华南高层处于强大的辐散环境中,从而加强了华南低层的辐合与低频上升运动,造成持续性强降水的增强。高层的低频信号来源于低频罗斯贝波列的东南向传播。（4）低频大气环流异常通过云辐射和热通量过程改变低频海表温度异常,而由大气强迫的低频海表温度异常通过影响低层大气的稳定性来对大气施加明显的反馈作用,该海-气耦合过程有利于大气低层低频信号向华南地区传播,从而影响了华南持续性强降水的发生、发展与结束。     

罗甸县春旱特征及成因浅析

根据2006年新颁布实施的《贵州省干旱标准》,利用罗甸县相关气候资料、春旱旱情资料和该县石漠化现状调查、森林资源调查等成果进行综合统计分析。结果表明:春旱是罗甸县发生最为频繁的气象灾害,重级和特重级春旱频率高、前春干旱重于后春干旱、跨季节连旱频繁是其基本特征。该县干旱的成因是该县地理位置、地形地貌、大气环流与生态环境等综合作用的结果,其石漠化较重、森林覆盖率低的北部乡镇春旱要重于石漠化程度较低、森林覆盖率较高的中南部乡镇。     

春季陆面植被对长江流域夏季降水可预报性的影响分析

利用美国全球监测与模型研究中心(GIMMS)1982—2006年逐月归一化植被指数(NDVI)、美国国家海洋和大气局(NOAA)1854—2008年海温资料以及中国国家气候中心(NCC)1951—2006年160站月降水资料,通过旋转经验正交函数分解(REOF)和相关分析获得了长江流域夏季降水预报序列和植被、海温预报因子集。基于最优子集回归方法(OSR),并借助交叉验证(CV)以及空间重建等手段,构建了单独以前期春季海温为预报因子和同时引入前期春季海温与归一化植被指数为因子的两类预报模型,对比分析引入陆面植被因子前后长江流域夏季降水预报效果改善状况,评估春季陆面植被对长江流域夏季降水可预报性的影响及预报效果的稳健性。结果表明:(1)相对于海温因子,春季陆面植被因子对长江流域夏季降水预报具有同样重要性,引入春季归一化植被指数后,长江流域夏季降水预报得到明显改善,相关系数平均由0.49提升到0.66,提高0.17左右,模型解释方差提升平均60%左右,其中单纯海温因子预报效果较差的汉江—淮河地区和淮河流域地区,相关系数更是提高了0.20—0.30,模型解释方差提升1倍左右;(2)交叉验证预报表明,相对于仅考虑海温因子模拟情形,交叉预报相关系数下降较多,模型稳健性较低,引入归一化植被指数后,长江流域夏季降水预报稳健性得到明显提升,长江中下游及其以南的长江三角洲地区、洞庭湖—鄱阳湖地区改善尤为明显;(3)长江流域降水可预报性存在明显的区域差异,嘉陵江流域地区、汉江—洞庭湖地区预报效果最好,汉江—淮河地区、淮河流域地区、长江三角洲地区预报效果最差,但引入归一化植被指数后预报效果提高最明显,而洞庭湖—鄱阳湖地区虽然模拟效果较好,但预报稳健性较低,交叉验证相关系数降幅达到0.27,这也从侧面说明了长江流域夏季降水分区预报的重要性。     

Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.     

基于IAP AGCM4的欧亚大陆春季雪水当量可预报性评估

欧亚大陆积雪是重要的气候预测因子,评估其在气候模式中的预测潜力可为季节气候预测和模式发展提供重要参考。本文利用IAP AGCM4的多年集合后报结果,分析了欧亚大陆春季雪水当量的可预报性。结果表明该模式对提前1月后报的欧亚大陆春季雪水当量的空间分布,主要模态及变化趋势具有较好的可预报能力。此外模式对欧亚中高纬积雪的年际异常也具有较高的预报技巧,特别是高纬度区域。可预报性来源分析则表明,大气初始异常对欧亚中高纬积雪可预报性的影响与海温异常相比显得更为重要。     

2021年盛夏中国东部极端降水月际演变成因及可预测性

2021年7—8月中国东部雨带演变特征与气候平均季风北推进程存在显著差异。其中,7月降水正异常中心位于江淮-华北地区,8月则南移至华中地区。2021年中国东部降水异常偏多且存在月际差异主要与7(8)月西北太平洋副热带高压(西太副高)偏北偏东(偏南偏西)、东亚副热带西风急流偏北(偏南)以及南亚高压持续东伸相关联。进一步研究表明,热带对流的活跃位置和北大西洋的增暖加强是影响其降水中心南移的主要原因。2021年7月热带大气低频振荡(MJO)在海洋性大陆地区活跃对应其热带海洋性大陆对流异常偏强,激发北传的类太平洋-日本(PJ)型遥相关波列,使得西太副高偏北偏东,有利于西北太平洋水汽在江淮-华北地区辐合,导致其降水偏多。8月,新发展MJO在热带印度洋上空对流异常持续偏强,加强局地经向环流,使得中国35°N以南至西北太平洋地区出现异常下沉运动,有利于西太副高南移西伸。此外,2021年8月北大西洋海温(SST)异常偏暖激发对流层高层向东南传播的Rossby波,有利于南亚高压加强和东亚副热带西风急流加强南移。因此,8月降水中心南移至华中地区。CFSv2预测系统(6月起报)结果能预测7月江淮-华北大部分地区降水偏多,但预测的8月华中南部地区降水偏少与实况相反。这可能是由于模式能够较好再现7月海洋性大陆热带对流活动影响江淮-华北地区降水的过程,但不能预测2021年8月热带印度洋对流活动和北大西洋海温异常偏暖对华中地区降水的影响。     

Potential impacts of East Asian winter monsoon on climate variability and predictability in the Australian summer monsoon region

This study explores potential impacts of the East Asian winter monsoon (EAWM) on summer climate variability and predictability in the Australia–Asian region through Australia–Asia (A-A) monsoon interactions. Observational analysis is conducted for the period of 1959 to 2001 using ERA-40 wind reanalysis and Climate Research Unit rainfall and surface temperature monthly datasets. Statistically significant correlations are established between the Australian summer monsoon and its rainfall variations with cross-equatorial flows penetrating from South China Sea region and northerly flow in the EAWM. The underlying mechanism for such connections is the response of the position and intensity of Hardley circulation to strong/weak EAWM. A strong EAWM is associated with an enhanced cross-equatorial flow crossing the maritime continent and a strengthened Australia summer monsoon westerlies which affect rainfall and temperature variations in northern and eastern part of the Australian continent. Furthermore, partial correlation analysis, which largely excludes El Niño-Southern Oscillation (ENSO) effects, suggests that these connections are the inherent features in the monsoon system. This is further supported by analyzing a global model experiment using persistent sea surface temperatures (SSTs) which, without any SST interannual variations, shows similar patterns as in the observational analysis. Furthermore, such interaction could potentially affect climate predictability in the region, as shown by some statistically significant lag correlations at monthly time scale. Such results are attributed to the impacts of EAWM on regional SST variations and its linkage to surface conditions in the Eurasian continent. Finally, such impacts under global warmed climate are discussed by analyzing ten IPCC AR4 models and results suggest they still exist in the warmed climate even though the EAWM tends to be weaker.     

Global seasonal climate predictability in a two tiered forecast system. Part II: boreal winter and spring seasons

We examine the Florida Climate Institute–Florida State University Seasonal Hindcast (FISH50) skill at a relatively high (50 km grid) resolution two tiered Atmospheric General Circulation Model (AGCM) for boreal winter and spring seasons at zero and one season lead respectively. The AGCM in FISH50 is forced with bias corrected forecast sea surface temperature averaged from two dynamical coupled ocean–atmosphere models. The comparison of the hindcast skills of precipitation and surface temperature from FISH50 with the coupled ocean–atmosphere models reveals that the probabilistic skill is nearly comparable in the two types of forecast systems (with some improvements in FISH50 outside of the global tropics). Furthermore the drop in skill in going from zero lead (boreal winter) to one season lead (boreal spring) is also similar in FISH50 and the coupled ocean–atmosphere models. Both the forecast systems also show that surface temperature hindcasts have more skill than the precipitation hindcasts and that land based precipitation hindcasts have slightly lower skill than the corresponding hindcasts over the ocean.     

The predictability of weather and climate

The last thirty years have seen the development of comprehensive numerical models of the large-scale circulation of the atmosphere, based on physical principles. Such models are quite skillful at describing the evolving weather up to a few days ahead, despite imperfect theory and inadequate observational data. Yet even a hypothetical perfect model, which exactly represented the dynamics of the real atmosphere, and which used data from the best conceivable observing system, could not produce an accurate forecast of indefinitely long range. Any forecast must eventually lose skill because of the intrinsic instability of the atmosphere itself.This limitation on the predictability of the detailed evolution of the atmosphere (weather) does not preclude the possibility of seasonal and longer-range forecasts of means and other statistical properties (climate). However, we are only beginning to learn what aspects of climate may be predictable, and what theoretical tools and observational data will be required to predict them.     

On the chaotic behavior and predictability of the real atmosphere

In this paper the concept of Chaos and its applications to the study of predictability theory is introduced. The au-thor’s attempt is to give a general overview of ideas and methods involved in this problem to scientists, who are inter-ested in the problem of predictability but not familiar with the theory of chaos. The problem is discussed in 4 sections. In the first section, the concept of chaos and the study methods are outlined briefly; in the second section, the methods of quantitatively measuring the main characteristics of chaos which are the basis for the predictability theory are in-troduced; the third section discusses the time series analysis for directly studying chaotic phenomena in practical prob-lems; and the last section presents some research results on the chaotic characteristics and the predictability of the real atmosphere.     

On the predictability of low-level flow during ALPEX

Summary The growth of error energy from initially uncertain states is a characteristic of global forecast models that is absent or markedly diminished in limited area forecasts. The enhanced regional predictability is presently studied with a limited area boundary layer model applied to a European region centered on the Alps. The results are remarkably insensitive to initial data, and a qualitative explanation of this is sought in terms of Thompson's (1957) and Lorenz's (1969) predictability analysis. It appears that the high predictability of regional models is an artifact of the overwhelming role that the prespecification of external boundaries plays in this problem. In cases that Dirichlet boundary conditions are imposed at the perimeter of the limited forecast region, the larger scale flow components, including most of the advecting flow are determined completely independently of internal dynamics and vorticity fluctuations, a condition that does not promote uncertainty growth.The simplest relaxation of this constraint is accomplished by imposing Neumann boundary conditions with zero gradient of forecast variables at the outer boundary. In this case the boundary values depend completely upon the interior forecast, and there is no theoretical reason to expect that error growth should be limited. Nevertheless, present results show that the only significant forecast errors associated with initial uncertainties in these cases are trapped near external boundaries. An explanation of this phenomenon and its generality is discussed. Our forecast results and analysis of error spread from boundaries suggest that topography may enhance local predictability.Although the predictability of a regional boundary layer model is high with respect to initial errors of even rather large magnitude, the same is not true with respect to large uncertainties in the representation of topography and surface, radiative and dissipative effects. Substantial variations of the parameterization of these processes through changes of the model equations produce boundary layer solution divergence with doubling time scales as short as one day. The uncertainty growth associated with smaller (and more realistic) perturbations of these processes remains to be studied.

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Über die Voraussagbarkeit von bodennahen Strömungen während ALPEX

Zusammenfassung Das Anwachsen der Fehler auf Grund anfänglich unsicherer Zustände ist ein Charakteristikum globaler Vorhersagemodelle. Diese Beschränkung ist nicht vorhanden oder stark vermindert in Vorhersagen für begrenzte Gebiete. Die verbesserte regionale Vorhersagbarkeit wird gegenwärtig an einem Grenzschichtmodell untersucht, welches auf einen Teil Europas mit den Alpen im Zentrum angewandt wird. Die Ergebnisse sind auffallend im empfindlich gegenüber den Anfangsdaten. Eine qualitative Erklärung dafür kann anhand der Vorhersagbarkeitsanalyse von Thompson (1957) und Lorenz (1969) durchgeführt werden. Die hohe Vorhersagbarkeit regionaler Modelle erscheint als Ergebnis der überwältigenden Rolle, die die Vorgabe der äußeren Ränder in diesem Problem spielt. In Fällen, wo Dirichlet-Randbedingungen an der Peripherie des begrenzten Vorhersagegebietes aufgezwungen werden, erfolgt die Bestimmung der großräumigen Strömungskomponenten inklusive des größten Teils der advektierenden Strömung, unabhängig von der internen Dynamik und den Wirbelfluktuationen. Diese Bedingung fördert das Anwachsen von Unsicherheiten nicht.Die einfachste Lockerung dieser Beschränkung wird durch Einführung von Neumann-Randbedingungen mit der Vorhersagevariablen ohne Gradienten an den äußeren Rändern erreicht. In diesem Fall hängen die Randwerte vollständig von der Vorhersage im Inneren ab und es besteht kein theoretischer Grund, eine Beschränkung des Fehlerwachstums zu erwarten. Dennoch zeigen die gegenwärtigen Ergebnisse, daß die einzigen wesentlichen Vorhersagefehler in Zusammenhang mit Anfangsunsicherheiten in diesen Fällen auf den Randbereich beschränkt sind. Eine Erklärung dieses Phänomens und seine Allgemeingültigkeit wird diskutiert. Unsere Vorhersageergebnisse und die Analyse der Fehlerausbreitung von den Rändern aus legt nahe, daß die topographie die lokale Vorhersagbarkeit verbessern kann.Obwohl die Vorhersagbarkeit eines regionalen Grenzschichtmodells in bezug auf die verhältnismäßig großen Anfangsfehler hoch ist, ist dies nicht so in bezug auf die großen Unsicherheiten in der Wiedergabe der Topographie und der Oberfläche sowie Strahlungs- und dissipativer Effekte. Wesentliche Variationen der Parametrisierung dieser Prozesse durch Änderungen der Modellgleichungen erzeugen Divergenzen in den Grenzschichtlösungen, die sich schon in einem Tag verdoppeln. Das Wachstum der Unsicherheit verbunden mit kleineren (und realistischeren) Störungen dieser Prozesse bleibt noch zu untersuchen.

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With 14 Figures