Intraseasonal atmospheric variability and its interannual modulation in Central Africa

The spatial and temporal structures of the intraseasonal atmospheric variability over central Africa is investigated using 2.5°?×?2.5° daily outgoing longwave radiation (OLR) and National Centers for Environmental Prediction (NCEP) Reanalysis zonal winds for the period 1980–2010. The study begins with an overview of the Central African rainfall regime, noting in particular the contrast amongst Western and Eastern parts, with different topography and surface conditions features. The annual mean rainfall and OLR over the region revealed a zone of intense convective activity centered on the equator near 30°E, which extends southward and covers almost all the Congo forest. The annual cycle of rainfall reflects the classical bi-annual shift of Inter-Tropical Convergence Zone across the equatorial belt, between 10°S and 10°N. The result of the empirical orthogonal functions (EOFs) analysis has shown that the three leading EOF modes explain about 45?% of total intraseasonal variability. The power spectra of all the three corresponding principal components (PCs) peak around 45–50?days, indicating a Madden–Julian Oscillation (MJO) signal. The first mode exhibits high positive loadings over Northern Congo, the second over Southern Ethiopia and the third over Southwestern Tanzania. The PCs time series revealed less interannual modulation of intraseasonal oscillations for the Congo mode, while Ethiopian and Tanzanian modes exhibit strong interannual variations. H?vmoller plots of OLR, 200 and 850?hPa NCEP zonal winds found the eastward propagating features to be the dominant pattern in all the three times series, but this propagation is less pronounced in the OLR than in the 850 and 200?hpa zonal wind anomalies. An index of MJO strength was built by averaging the 30–50?day power for each day. A plot of MJO indices and El Ni?o Southern Oscillation (ENSO) cycle confirm a strong interannual modulation of MJO over Eastern central Africa partially linked with the ENSO events (El Ni?o and La Ni?a). Strong MJO activity is observed during La Ni?a years or during ENSO-neutral years, while weak or absent MJO activity is typically associated with strong El Ni?o episodes.     

Observational and modelling studies of Amazonia interannual climate variability

The interannual variability of climate in the Amazon basin is studied using precipitation and river level anomalies observed near the March/April rainy season peak for the period 1980–86, supported by satellite imagery of tropical convection. Evaluation of this data in conjunction with the corresponding circulation and sea-surface temperature (SST) anomaly patterns indicates that abundant rainy seasons in Northern Amazonia are characterized by anomalously cold surface waters in the tropical eastern Pacific, and negative/positive SST anomalies in the tropical North/South Atlantic, accelerated Northeast trades and a southward displaced Intertropical Convergence Zone (ITCZ) over the Atlantic sector. Years with deficient rainfall show broadly opposite patterns.General circulation model (GCM) experiments using observed SST in three case studies were aimed at testing the teleconnections between SST and Amazon climate implied by the empirical analysis. The GCM-generated surface fields resemble the corresponding observers fields most closely over the tropical Pacific and, with one exception, over the tropical Atlantic as well. The modeled precipitation features, along the Northwest coast of South America, anomalies of opposite sign to the North and South of the equator, in agreement with observations and results from a different GCM. Similarities in simulations run from different initial conditions, but using the same global SST, indicate broad consistency in response to common boundary forcing.     

The Interannual Variability and Predictability in a Global Climate Model

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Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.     

The Interannual Variability of Climate in a Coupled Ocean-Atmosphere Model

In this paper, the interannual variability simulated by the coupled ocean-atmosphere general circulation model of the Institute of Atmospheric Physics (IAP CGCM) in 40 year integrations is analyzed, and compared with that by the corresponding IAP AGCM which uses the climatic sea surface temperature as the boundary condition in 25 year integrations.The mean climatic states of January and July simulated by IAP CGCM are in good agreement with that by IAP AGCM, i.e., no serious ‘climate drift’ occurs in the CGCM simulation. A comparison of the results from AGCM and CGCM indicates that the standard deviation of the monthly averaged sea level pressure simulated by IAP CGCM is much greater than that by IAP AGCM in tropical region. In addition, both Southern Oscillation (SO) and North Atlantic Oscillation (NAO) can be found in the CGCM simulation for January, but these two oscillations do not exist in the AGCM simulation.The interannual variability of climate may be classified into two types: one is the variation of the annual mean, another is the variation of the annual amplitude. The ocean-atmosphere interaction mainly increases the first type of variability. By means of the rotated EOF, the most important patterns corresponding to the two types of interannual variability are found to have different spatial and temporal characteristics.     

Response of the mean global vegetation distribution to interannual climate variability

The impact of interannual variability in temperature and precipitation on global terrestrial ecosystems is investigated using a dynamic global vegetation model driven by gridded climate observations for the twentieth century. Contrasting simulations are driven either by repeated mean climatology or raw climate data with interannual variability included. Interannual climate variability reduces net global vegetation cover, particularly over semi-arid regions, and favors the expansion of grass cover at the expense of tree cover, due to differences in growth rates, fire impacts, and interception. The area burnt by global fires is substantially enhanced by interannual precipitation variability. The current position of the central United States’ ecotone, with forests to the east and grasslands to the west, is largely attributed to climate variability. Among woody vegetation, climate variability supports expanded deciduous forest growth and diminished evergreen forest growth, due to difference in bioclimatic limits, leaf longevity, interception rates, and rooting depth. These results offer insight into future ecosystem distributions since climate models generally predict an increase in climate variability and extremes. CCR Contribution # 941     

Low-frequency variability and CO2 transient climate change. Part 3. Intermonthly and interannual variability

Components of interannual, intermonthly, and total monthly variability of lower troposphere temperature are calculated from a global coupled ocean-atmosphere general circulation model (GCM) (referred to as the coupled model), from the same atmospheric model coupled to a nondynamic mixedlayer ocean (referred to as the mixed-layer model), and from microwave sounding unit (MSU) satellite data. The coupled model produces most features of intermonthly and interannual variability compared to the MSU data, but with somewhat reduced amplitude in the extratropics and increased variability in the tropical western Pacific and tropical Atlantic. The relatively short 14-year period of record of the MSU data precludes definitive conclusions about variability in the observed system at longer time scales (e.g., decadal or longer). Different 14-year periods from the coupled model show variability on those longer time scales that were noted in Part 1 of this series. The relative contributions of intermonthly and interannual variability that make up the total monthly variability are similar between the coupled model and the MSU data, suggesting that similar mechanisms are at work in both the model and observed system. These include El Niño-Southern Oscillation (ENSO)-type interannual variability in the tropics, Madden-Julian Oscillation (MJO) type intermonthly variability in the tropics, and blocking-type intermonthly variability in the extratropics. Manifestations of all of these features have been noted in various versions of the model. Significant changes of variability noted in the coupled model with doubled carbon dioxide differ from those in our mixed-layer model and earlier studies with mixed-layer models. In particular, in our mixed-layer model intermonthly and interannual variability changes are similar with a mixture of regional increases and decreases, but with mainly decreases in the zonal mean from about 20°S to 60°N and near 60°S. In the coupled model, intermonthly and interannual changes of variability with doubled CO₂ show mostly increases of tropical interannual variability and decreases of intermonthly variability near 60°N. These changes in the tropics are related to changes in ENSO, the south Asian monsoon, and other regional hydrological regimes, while the alterations near 60°N are likely associated with changes in blocking activity. These results point to the important contribution from ENSO seen in the coupled model and the MSU data that are not present in the mixed-layer model.     

Intraseasonal and interannual zonal circulations over the Equatorial Indian Ocean

El Ni?o Southern Oscillation (ENSO) and given phases of the Madden?CJulian Oscillation (MJO) show similar regional signatures over the Equatorial Indian Ocean, consisting in an enhancement or reversing of the convective and dynamic zonal gradients between East Africa and the Maritime Continent of Indonesia. This study analyses how these two modes of variability add or cancel their effects at their respective timescales, through an investigation of the equatorial cellular circulations over the central Indian Ocean. Results show that (1) the wind shear between the lower and upper troposphere is related to marked regional rainfall anomalies and is embedded in larger-scale atmospheric configurations, involving the Southern Oscillation; (2) the intraseasonal (30?C60?days) and interannual (4?C5?years) timescales are the most energetic frequencies that modulate these circulations, confirming the implication of the MJO and ENSO; (3) extreme values of the Indian Ocean wind shear result from the combination of El Ni?o and the MJO phase enhancing atmospheric convection over Africa, or La Ni?a and the MJO phase associated with convective activity over the Maritime Continent. Consequences for regional rainfall anomalies over East Africa and Indonesia are then discussed.     

On the phase propagation and relationship of interannual variability in the tropical Pacific climate system

—Upper ocean thermal data and surface marine observations are used to describe the three-dimensional, basinwide co-evolution of interannual variability in the tropical Pacific climate system. The phase propagation behavior differs greatly from atmosphere to ocean, and from equatorial to off-equatorial and from sea surface to subsurface depths in the ocean. Variations in surface zonal winds and sea surface temperatures (SSTs) exhibit a standing pattern without obvious zonal phase propagation. A nonequilibrium ocean response at subsurface depths is evident, characterized by coherent zonal and meridional propagating anomalies around the tropical North Pacific: eastward on the equator but westward off the equator. Depending on geographic location, there are clear phase relations among various anomaly fields. Surface zonal winds and SSTs in the equatorial region fluctuate approximately in-phase in time, but have phase differences in space. Along the equator, zonal mean thermocline depth (or heat content) anomalies are in nonequilibrium with the zonal wind stress forcing. Variations in SSTs are not in equilibrium either with subsurface thermocline changes in the central and western equatorial Pacific, with the former lagging the latter and displaced to the east. Due to its phase relations to SST and winds, the basinwide temperature anomaly evolution at thermocline depths on an interannual time scale may determine the slow physics of ENSO, and play a central role in initiating and terminating coupled air-sea interaction. This observed basinwide phase propagation of subsurface anomaly patterns can be understood partially as water discharge processes from the western Pacific to the east and further to high latitudes, and partially by the modified delayed oscillator physics. Received: 17 January 1997 / Accepted: 10 March 1998     

Intraseasonal variability of the Tropical Easterly Jet

Summary By analyzing 12-year (1979–1990) 200 hPa wind data from National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis, we demonstrate that the intraseasonal time scale (30–60 days) variability of the Tropical Easterly Jet (TEJ) reported in individual case studies occurs during most years. In the entrance region (east of ∼70° E), axis of the TEJ at 200 hPa is found along the near equatorial latitudes during monsoon onset/monsoon revivals and propagates northward as the monsoon advances over India. This axis is found along ∼5° N and ∼15° N during active monsoon and break monsoon conditions respectively. Examination of the European Centre for Medium Range Weather Forecasts reanalysis wind data also confirms the northward propagation of the TEJ on intraseasonal time scales. During the intraseasonal northward propagations, axis of the TEJ is found about 10°–15° latitudes south of the well-known intraseasonally northward propagating monsoon convective belts. Because of this 10°–15° displacement, axis of the TEJ arrives over a location about two weeks after the arrival of the monsoon convection. Systematic shifting of the locations by convection, low level monsoon flow and TEJ in a collective way during different phases of the monsoon suggests that they all may be related.     

Caribbean hurricanes: interannual variability and prediction

Climatic conditions that affect the interannual variability of Caribbean hurricanes are studied. Composite meteorological and oceanographic reanalysis fields are constructed for active and inactive seasons since 1979, and differences are calculated for spring and summer periods to provide guidance in statistical analysis. Predictors are extracted for areas exhibiting high contrast between active and inactive seasons, and intercomparisons are made. Zonal winds north of Venezuela exhibit westerly anomalies prior to active years, so coastal upwelling and the north Brazil current are diminished. Rainfall increases in the Orinoco River basin, creating a fresh warm plume north of Trinidad. The predictor time series are regressed onto an index of Caribbean hurricanes, and multivariate algorithms are formulated. It is found that atmospheric kinematic and convective predictors explain only ??20% of hurricane variance at 3?C5-month lead time. Subsurface ocean predictors offer higher levels of explained hurricane variance (42%) at 3?C5-month lead time, using 1?C200-m-depth-averaged temperatures in the east Pacific and southern Caribbean. We place the statistical results in a conceptual framework to better understand climatic processes anticipating Caribbean hurricanes.     

A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

A scenario of European climate change for the late twenty-first century is described, using a high-resolution state-of-the-art model. A time-slice approach is used, whereby the atmospheric general circulation model, HadAM3P, was integrated for two periods, 1960–1990 and 2070–2100, using the SRES A2 scenario. For the first time an ensemble of such experiments was produced, along with appropriate statistical tests for assessing significance. The focus is on changes to the statistics of seasonal means, and includes analysis of both multi-year means and interannual variance. All four seasons are assessed, and anomalies are mapped for surface air temperature, precipitation and snow mass. Mechanisms are proposed where these are dominated by straightforward local processes. In winter, the largest warming occurs over eastern Europe, up to 7°C, mean snow mass is reduced by at least 80% except over Scandinavia, and precipitation increases over all but the southernmost parts of Europe. In summer, temperatures rise by 6–9°C south of about 50°N, and mean rainfall is substantially reduced over the same area. In spring and autumn, anomalies tend to be weaker, but often display patterns similar to the preceding season, reflecting the inertia of the land surface component of the climate system. Changes in interannual variance are substantial in the solsticial seasons for many regions (note that for precipitation, variance estimates are scaled by the square of the mean). In winter, interannual variability of near-surface air temperature is considerably reduced over much of Europe, and the relative variability of precipitation is reduced north of about 50°N. In summer, the (relative) interannual variance of both variables increases over much of the continent.     

The interannual precipitation variability in the southern part of Iran as linked to large-scale climate modes

The interannual variation of precipitation in the southern part of Iran and its link with the large-scale climate modes are examined using monthly data from 183 meteorological stations during 1974–2005. The majority of precipitation occurs during the rainy season from October to May. The interannual variation in fall and early winter during the first part of the rainy season shows apparently a significant positive correlation with the Indian Ocean Dipole (IOD) and El Ni?o-Southern Oscillation (ENSO). However, a partial correlation analysis used to extract the respective influence of IOD and ENSO shows a significant positive correlation only with the IOD and not with ENSO. The southeasterly moisture flux anomaly over the Arabian Sea turns anti-cyclonically and transport more moisture to the southern part of Iran from the Arabian Sea, the Red Sea, and the Persian Gulf during the positive IOD. On the other hand, the moisture flux has northerly anomaly over Iran during the negative IOD, which results in reduced moisture supply from the south. During the latter part of the rainy season in late winter and spring, the interannual variation of precipitation is more strongly influenced by modes of variability over the Mediterranean Sea. The induced large-scale atmospheric circulation anomaly controls moisture supply from the Red Sea and the Persian Gulf.     

江南雨季降水季节内演变及其年际、年代际变化特征

利用1961—2008年逐日降水资料,在对比我国东南部各地区气候态降水特征的基础上,着重探讨了江南地区(110~120°E、24~30°N)雨季降水的季节内变化特征及其年际、年代际变化规律。结果表明:1)江南雨季气候态降水的季节内变化具有明显的双峰型特征,两个峰值集中期分别是4、6月中旬前后。4月中下旬第一个降水峰值率先出现在江南地区,之后峰值降水南移,于6月上中旬华南地区达峰值集中期,之后强降水才逐渐北移,6月中下旬又回至江南地区,使江南地区降水达第二个峰值集中期。2)我国江南地区区域平均的双峰降水与4—6月的实际降水之间的相关系数达0.69,这表明双峰型降水确实反映了江南雨季降水的季节内演变特征。3)江南雨季降水双峰型的季节内变化特征具有明显的年际、年代际变化周期。年际变化周期为2~3 a,强信号主要集中在20世纪60年代后期到70年代中期以及80年代中期到21世纪初;年代际变化周期约为8~10 a,在整个时间域上都存在,最强信号集中在80年代初到90年代末期。4)年代际尺度上,江南雨季降水的季节内变化特征(双峰型态)具有隔代显著的特征,即20世纪60、80年代及21世纪初双峰型特征显著,而20世纪70、90年代双峰型特征不显著。     

The interannual variability of East Asian Monsoon and its relationship with SST in a coupled Atmosphere-Ocean-Land climate model

1.IntroductionThelargestinterannualvariabilityassociatedwiththeENSOcycleexistsinmonsoonregionsliketheAfricanmonsoon,Australianmonsoon,Pan--AmericanmonsoonandAsianmonsoon(RopelewskiandHalpert,1987;WebsterandYang,1992;JuandSlingo,1995).OnebasicquestionishowtorepresenttheAsianmonsoonanditsvariability.WebsterandYang(1992)foundareasonableindexbyaveragingthezonalwindshearbetween850hpaand200hpaovertheSouthAsianregion(40--110E,0--20N)todescribetheSouthAsianmonsooncirculationanditsvariability.…     

青藏高原雪盖的季节内变化及其影响

回顾了青藏高原雪盖的季节内变化及其影响研究的新进展。高原大部分地区雪盖不稳定且持续时间短,导致高原雪盖具有显著的季节内快速变化特征。局地气温和降水的季节内变化是控制高原雪盖季节内变化的直接原因,这种直接关系是区域大气环流季节内活动的结果。高原雪盖季节内变化还与大尺度大气环流的季节内活动有关,热带季节内振荡、北极涛动和北大西洋涛动引起的大气季节内过程可解释部分高原雪盖季节内变率。高原雪盖季节内变化通过雪-反照率效应迅速对大气施加影响,雪盖造成的冷异常通过大气平流过程影响高原及其下游地区,造成东亚高空急流和东亚大槽增强。由于高原雪盖季节内变化的重要影响,数值预报中高原雪盖的初始场和预报场会影响次季节预报技巧。     

The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021–2050 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM?×?GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021–2050 response which shows a similar pattern to the one obtained for 2071–2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021–2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe.     

Intraseasonal variability in South America during the cold season

Intraseasonal (IS) variability in South America is analyzed during the cold season using 10–90 day bandpass filtered OLR anomalies (FOLR). IS variability explains a large percentage of variance with maximum values over Paraguay, northeastern Argentina, and southern Brazil. The leading pattern of FOLR, as isolated from an EOF analysis, (Cold Season IS pattern, CSIS), is characterized by a monopole centered over southeastern South America (SESA) with a northwest-southeast orientation. CSIS induces a large modulation on daily precipitation anomalies, especially on both wet spells and daily precipitation extremes, which are favored during positive (wet) CSIS phases. Large-Scale OLR anomalies over the tropical Indian and west Pacific Oceans associated with CSIS exhibit eastward propagation along tropical latitudes. In addition, circulation anomalies in the Southern Hemisphere reveal the presence of an anticyclonic anomaly over Antarctica with opposite-sign anomalies in middle latitudes 10 days before CSIS is maximum as well as evidence of Rossby wave-like patterns. Positive precipitation anomalies in SESA are favored during wet CSIS phases by the intensification of a cyclonic anomaly located further south, which is discernible over the southeastern Pacific for at least 14 days before CSIS peaks. The cyclonic anomaly evolution is accompanied by the intensification of an upstream anticyclonic anomaly, which remains quasi-stationary near the Antarctica Peninsula before the CSIS peak. We speculate that the stationary behavior of the anticyclonic center is favored by a hemispheric circulation anomaly pattern resembling that associated with a negative southern annular mode phase and a wavenumber 3–4 pattern at middle latitudes.