The relative influence of soil moisture and SST in climate predictability explored within ensembles of AMIP type experiments

Three ensembles of AMIP-type simulations using the Arpege-climat coupled land–atmosphere model have been designed to assess the relative influence of SST and soil moisture (SM) on climate variability and predictability. The study takes advantage of the GSWP2 land surface reanalysis covering the 1986–1995 period. The GSWP2 forcings have been used to derive a global SM climatology that is fully consistent with the model used in this study. One ensemble of ten simulations has been forced by climatological SST and the simulated SM is relaxed toward the GSWP2 reanalysis. Another ensemble has been forced by observed SST and SM is evolving freely. The last ensemble combines the observed SST forcing and the relaxation toward GSWP2. Two complementary aspects of the predictability have been explored, the potential predictability (analysis of variance) and the effective predictability (skill score). An analysis of variance has revealed the effects of the SST and SM boundary forcings on the variability and potential predictability of near-surface temperature, precipitation and surface evaporation. While in the tropics SST anomalies clearly maintain a potentially predictable variability throughout the annual cycle, in the mid-latitudes the SST forced variability is only dominant in winter and SM plays a leading role in summer. In a similar fashion, the annual cycle of the hindcast skill (evaluated as the anomalous correlation coefficient of the three ensemble means with respect to the “observations”) indicates that the SST forcing is the dominant contributor over the tropical continents and in the winter mid-latitudes but that SM is supporting a significant part of the skill in the summer mid-latitudes. Focusing on boreal summer, we have then investigated different aspects of the SM and SST contribution to climate variations in terms of spatial distribution and time-evolution. Our experiments suggest that SM is potentially an additional source of climate predictability. A realistic initialization of SM and a proper representation of the land–atmosphere feedbacks seem necessary to improve state-of-the-art dynamical seasonal predictions, but will be actually efficient only in the areas where SM anomalies are themselves predictable at the monthly to seasonal timescale (since remote effects of SM are probably much more limited than SST teleconnections).     

Seasonal differences of model predictability and the impact of SST in the Pacific

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-AGCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer,the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Nino-3 region are significant. Moreover,particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.     

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model (Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model, hereinafter referred to as IAP9L_CoLM), the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated. The predictive skill of sea level pressures (SLP) and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model. Furthermore, the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature (SAT) anomalies over China was improved by applying realistic (prescribed) Eurasian snow conditions. The predictive skill of the summer precipitation in China was low; however, when realistic snow conditions were employed, the predictability increased, illustrating the effectiveness of the application of realistic Eurasian snow conditions. Overall, the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China. When Eurasian snow conditions in the global climate model (GCM) can be more realistically represented, the predictability of summer climate over China increases.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

有关南半球大气环流与东亚气候的关系研究的若干新进展

南半球大气环流是全球大气环流的重要组成部分,也是影响气候变化和亚洲季风系统的一个重要因素.中国气象学家很早就注意到南半球大气环流对东亚夏季风降水的影响.近年来,有关南半球气候变率的研究目前正受到世界气象学家越来越多的关注.南半球中高纬大气资料的丰富及南极涛动的确定,使得认识南半球高中纬环流的年际变动规律及其与东亚气候关系成为可能.本文主要介绍近年来有关南极涛动的年际变化与沙尘天气发生频次及东亚冬春季气候的关系,古气候资料揭示的南极涛动与华北降水的关系,以及南半球大气环流与长江中下游夏季降水的关系和南极涛动变率的可预测性等方面的研究进展.并对未来研究方向作了初步的展望.     

Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study

Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986–1995 period. We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate) predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America), at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the observed variability in the upper troposphere.     

季风与ENSO的选择性相互作用:年循环和春季预报障碍的影响

本文主要基于对Webster and Yang（1992）一文的回顾,讨论了年循环在季风和ENSO相互作用中的作用、春季预报障碍（SPB）、Webster-Yang指数（WYI）、以及亚洲夏季风的前期讯号等内容。亚洲季风和ENSO作为全球天气和气候变率的主要来源,它们之间的相互作用存在明显的年变化和季节“锁相”特征:在北半球秋冬季,亚洲季风对流活动最弱,此时ENSO的信号最强;但是到了北半球春季,亚洲季风对流快速爆发,而此时的ENSO信号却迅速衰减。亚洲季风和ENSO位相的错位变化使得热带海—气系统的不稳定性在北半球春季达到最大,此时任意一个微小的扰动都容易快速增长,最终导致基于ENSO的预报技巧减小。亚洲夏季风环流本质上可以看成是大气对副热带地区潜热加热的低频罗斯贝波响应,它具有很强的垂直风切变,这是WYI定义的物理基础。WYI数值越大,代表垂直东风切变越大,即亚洲季风环流增强,反之亦然。利用WYI与前期大气环流场、欧亚雪盖、土壤湿度等物理量进行回归分析,结果表明:当亚洲夏季风增强时,前期冬季和春季,在北印度洋和亚洲副热带地区上空出现东风异常,同时在更高纬度地区伴随出现西风的异常;此外,副热带地区如印度次大陆、中南半岛和东亚的土壤湿度增大;中纬度地区尤其是青藏高原中西部的积雪密度明显减小。这些前期讯号的发现有助于我们构建动力统计模型,进而提高对亚洲夏季风的季节预报水平。     

土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟

利用全球大气环流模式NCARCAM3进行了在给定的观测海温条件下的22a（1979—2000年）5—8月的2组集合试验。运用方差分析方法,分析了在气候态和年际变化的表层土壤湿度情况下,CAM3模式模拟的东亚夏季气候潜在可预报性及其差异。结果表明：在给定的观测海温条件下,采用气候态的土壤湿度时,CAM3模式模拟的东亚夏季气候的潜在可预报性偏低;而采用年际变化的土壤湿度时,模拟的夏季气候潜在可预报性有所提高,尤其是在中国西北地区;后者模拟的中国西北地区夏季降水和气温的潜在可预报性比前者的模拟结果提高0．1以上。其原因可能是：采用年际变化的土壤湿度时,模式可以更好地模拟出中国西北地区的地表蒸发量和湍流热通量的年际变化,进而使得模式对该地区夏季气候的预报技巧得到提高。     

一个海气耦合模式对东亚夏季气候预测潜力的评估

利用一个具有较高分辨率的海气耦合模式SINTEX-F(Scale Interaction Experiment-Frontier Research Center for Global Change coupled GCM)的多年回报结果,评估了该海气耦合模式对东亚区域,尤其是中国地区气候异常的预测潜力.与观测实况的比较结果表明:SINTEX-F模式对夏季降水、500 hPa高度场和地表气温都有一定的预测技巧,但是相比而言降水与高度场的回报技巧要高于地表气温;而且耦合模式对东亚地区气候异常的主要空间分布和年际变化特征也有较好的预测潜力,对500 hPa高度场效果较好;对降水异常的年际变化也有一定的预测潜力,尤其是我国中部地区效果较好,但是模式预测的降水异常的幅值较观测相对偏弱;此外对我国西部的极端气候也有一定的预测潜力.     

初值对中国东部初夏土壤湿度可预报性影响

土壤湿度是影响天气和气候非常重要的因子之一,但目前针对土壤湿度可预报性的研究报道相对较少。该文在对BCC_CSM模式进行了适合的陆面初始化的条件下,设计了两组在中国东部地区采用不同土壤湿度初值的回报试验研究该地区土壤湿度的可预报性及初值对其可预报性影响问题。试验结果表明:BCC_CSM模式在真实的外场强迫下可以模拟出相对合理的土壤湿度;土壤湿度的可预报性在表层约为3候,随着深度的增加,土壤湿度的可预报性持续时间增加,在中层预报性甚至能达到月尺度以上;初值对于土壤湿度的预报存在影响,在表层影响时间约为2~3候,影响时间随着深度增加;浅层土壤湿度受降水的影响较大,浅层土壤湿度变化滞后降水变化约1~2 d,中层土壤湿度变化与降水变化存在5 d左右的滞后关系。     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

Relevance of soil moisture for seasonal atmospheric predictions: is it an initial value problem?

Ensembles of boreal summer atmospheric simulations, spanning a 15-year period (1979–1993), are performed with the ARPEGE climate model to investigate the influence of soil moisture on climate variability and potential predictability. All experiments are forced with observed monthly mean sea surface temperatures. In addition to a control experiment with interactive soil moisture boundary conditions, two sensitivity experiments are performed. In the first, the interannual variability of the deep soil moisture is removed during the whole season, through a relaxation toward the monthly mean model climatology. In the second, only the variability of the initial soil moisture conditions is suppressed. While it is shown that soil moisture strongly contributes to the climate variability simulated in the control experiment, an analysis of variance indicates that soil moisture does not represent a significant source of predictability in most continental areas. The main exception is the North American continent, where climate predictability is clearly reduced through the use of climatological initial conditions. Using climatological soil moisture boundary conditions does not lead to strong and homogeneous impacts on potential predictability, thereby suggesting that the climate signals driven by the sea surface temperature variability are not generally amplified by interactive soil moisture and that the relevance of soil moisture for seasonal forecasting is mainly an initial value problem.     

Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.     

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。     

The effects of boreal forest expansion on the summer Arctic frontal zone

Over the last 100?years, Arctic warming has resulted in a longer growing season in boreal and tundra ecosystems. This has contributed to a slow northward expansion of the boreal forest and a decrease in the surface albedo. Corresponding changes to the surface and atmospheric energy budgets have contributed to a broad region of warming over areas of boreal forest expansion. In addition, mesoscale and synoptic scale patterns have changed as a result of the excess energy at and near the surface. Previous studies have identified a relationship between the positioning of the boreal forest-tundra ecotone and the Arctic frontal zone in summer. This study examines the climate response to hypothetical boreal forest expansion and its influence on the summer Arctic frontal zone. Using the Weather Research and Forecasting model over the Northern Hemisphere, an experiment was performed to evaluate the atmospheric response to expansion of evergreen and deciduous boreal needleleaf forests into open shrubland along the northern boundary of the existing forest. Results show that the lower surface albedo with forest expansion leads to a local increase in net radiation and an average hemispheric warming of 0.6°C at and near the surface during June with some locations warming by 1–2°C. This warming contributes to changes in the meridional temperature gradient that enhances the Arctic frontal zone and strengthens the summertime jet. This experiment suggests that continued Northern Hemisphere high-latitude warming and boreal forest expansion might contribute to additional climate changes during the summer.     

Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Nin o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.     

Estimating the Predictability of the Quasi-Biweekly Oscillation Using the Nonlinear Local Lyapunov Exponent Approach

The quasi-biweekly oscillation (QBWO) is a major intraseasonal variability (ISV) in the tropics. Based on bandpass-filtered outgoing longwave radiation (OLR) and wind field data, the predictability limits of the QBWO in boreal summer and boreal winter are investigated using the nonlinear local Lyapunov exponent (NLLE) approach The analysis shows that the evolution of the mean error growth of the QBWO in boreal summer and the evolution of the mean error growth in boreal winter are comparable Both curves exhibit rapid growth in the initial stage followed by a slowly fluctuating, ascending trend before saturation is reached. As a result, the potential predictability limits for the boreal summer QBWO are very close to those for the boreal winter QBWO, with a lead time of approximately three weeks. Given the current limitations in the simulation and prediction of ISV, including the QBWO, the results of this study provide a useful reference for assessing the predictability of the QBWO using model simulations.     

北半球海冰强迫作用下大气可预报性研究

使用LASG/IAP GOALS耦合模式中的全球大气环流模式分量AGCMR15L9的计算结果，在其他外强迫维持气候值不变的情况下，用方差分析的方法，以外部方差与总方差之比R_e作为衡量标准，考察该模式关于海冰的季节和跨季节潜在可预报性的大小。结果发现，从总体上看，北半球海冰变化所造成的潜在可预报性较小，只有在大气低层的一些气候要素，如温度、湿度的结果中，才存在R_e＞0.5的现象。潜在可预报性结果的局地特征比较明显，高值往往发生在海冰年际变率大的区域里。与中低纬海温在中高纬地区的影响相比，不排除海冰的作用更大的可能性。另外，如果分区域看，北半球某些区域的海冰，在若干挑选出的其区域海冰面积发生大异常年份中的潜在可预报性可能会比不做挑选的总体结果要大。这说明北半球某些区域海冰在面积发生较大异常的时候，可能对同期或（及）后期环流有着比较重要的影响。     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.     

Soil moisture memory and West African monsoon predictability: artefact or reality?

Besides sea surface temperature (SST), soil moisture (SM) exhibits a significant memory and is likely to contribute to atmospheric predictability at the seasonal timescale. In this respect, West Africa was recently highlighted as a “hot spot” where the land–atmosphere coupling could play an important role, through the recycling of precipitation and the modulation of the meridional gradient of moist static energy. Particularly intriguing is the observed relationship between summer monsoon rainfall over Sahel and the previous second rainy season over the Guinean Coast, suggesting the possibility of a soil moisture memory beyond the seasonal timescale. The present study is aimed at revisiting this question through a detailed analysis of the instrumental record and a set of numerical sensitivity experiments. Three ensembles of global atmospheric simulations have been designed to assess the relative influence of SST and SM boundary conditions on the West African monsoon predictability over the 1986–1995 period. On the one hand, the results indicate that SM contributes to rainfall predictability at the end and just after the rainy season over the Sahel, through a positive soil-precipitation feedback that is consistent with the “hot spot” hypothesis. On the other hand, SM memory decreases very rapidly during the dry season and does not contribute to the predictability of the all-summer monsoon rainfall. Though possibly model dependent, this conclusion is reinforced by the statistical analysis of the summer monsoon rainfall variability over the Sahel and its link with tropical SSTs. Our results indeed suggest that the apparent relationship with the previous second rainy season over the Guinean Coast is mainly an artefact of rainfall teleconnections with tropical modes of SST variability both at interannual and multi-decadal timescales.