Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the i...     

Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

Dynamical seasonal prediction of Southern African summer precipitation

Prediction skill for southern African (16°–33°E, 22°–35°S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Niño/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Niño, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.     

Evaluation of subseasonal impacts of the MJO/BSISO in the East Asian extended summer

The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.

     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

Spatial and temporal variability of summer rainfall over Ethiopia from observations and a regional climate model experiment

The spatial and temporal variability of rainfall over Ethiopia during the summer (JJAS) season is studied using observations (both station and satellite based) and model simulation data. The simulation dataset is generated using the fourth version of the International Center for Theoretical Physics Regional Climate Model (RegCM4) for the period 1989–2005. Ethiopia is first divided into 12 homogeneous regions using criteria including rotated empirical orthogonal function (REOF), spatial correlation, seasonal cycles, and topographical features. Spatially averaged observed and simulated rainfall time series are then generated and analyzed for each region. Standardized rainfall anomalies of the observations and the simulated data are highly correlated over the northern, western, northeastern, central, and southwestern regions, while a weak correlation is found over the border regions of the country. The dominant modes of rainfall variability are identified using REOF, while time–frequency variations of different dominant modes are described by wavelet analysis. The first leading patterns of rainfall and upper wind (averaged between 100 and 300 hPa) are highly correlated and exhibit similar features between simulation and observations over the northern, western, southwestern, and eastern regions of Ethiopia. The second loading pattern of rainfall and the first loading pattern of low-level wind (averaged between 850 and 1,000 hPa) exhibit a dipole structure across the southwestern and northeastern regions of the country. The dominant signals in the first rotated principal component (RPC) of rainfall and upper level wind fields show a period of 4–5 and 2–3 years, while the dominant signals in the second RPC show a period of 2–3 years at a 0.05 significance level. The correlations of significant RPCs across gauge, gridded, and model rainfall fields with that of low and upper level winds show the presence of a significant relationship (correlation exceeding ～0.6). Overall, the RegCM4 shows a good performance in simulating the spatial and temporal variability of precipitation over Ethiopia.     

Predictability of the western North Pacific summer climate demonstrated by the coupled models of ENSEMBLES

The Asian monsoon system, including the western North Pacific (WNP), East Asian, and Indian monsoons, dominates the climate of the Asia-Indian Ocean-Pacific region, and plays a significant role in the global hydrological and energy cycles. The prediction of monsoons and associated climate features is a major challenge in seasonal time scale climate forecast. In this study, a comprehensive assessment of the interannual predictability of the WNP summer climate has been performed using the 1-month lead retrospective forecasts (hindcasts) of five state-of-the-art coupled models from ENSEMBLES for the period of 1960–2005. Spatial distribution of the temporal correlation coefficients shows that the interannual variation of precipitation is well predicted around the Maritime Continent and east of the Philippines. The high skills for the lower-tropospheric circulation and sea surface temperature (SST) spread over almost the whole WNP. These results indicate that the models in general successfully predict the interannual variation of the WNP summer climate. Two typical indices, the WNP summer precipitation index and the WNP lower-tropospheric circulation index (WNPMI), have been used to quantify the forecast skill. The correlation coefficient between five models’ multi-model ensemble (MME) mean prediction and observations for the WNP summer precipitation index reaches 0.66 during 1979–2005 while it is 0.68 for the WNPMI during 1960–2005. The WNPMI-regressed anomalies of lower-tropospheric winds, SSTs and precipitation are similar between observations and MME. Further analysis suggests that prediction reliability of the WNP summer climate mainly arises from the atmosphere–ocean interaction over the tropical Indian and the tropical Pacific Ocean, implying that continuing improvement in the representation of the air–sea interaction over these regions in CGCMs is a key for long-lead seasonal forecast over the WNP and East Asia. On the other hand, the prediction of the WNP summer climate anomalies exhibits a remarkable spread resulted from uncertainty in initial conditions. The summer anomalies related to the prediction spread, including the lower-tropospheric circulation, SST and precipitation anomalies, show a Pacific-Japan or East Asia-Pacific pattern in the meridional direction over the WNP. Our further investigations suggest that the WNPMI prediction spread arises mainly from the internal dynamics in air–sea interaction over the WNP and Indian Ocean, since the local relationships among the anomalous SST, circulation, and precipitation associated with the spread are similar to those associated with the interannual variation of the WNPMI in both observations and MME. However, the magnitudes of these anomalies related to the spread are weaker, ranging from one third to a half of those anomalies associated with the interannual variation of the WNPMI in MME over the tropical Indian Ocean and subtropical WNP. These results further support that the improvement in the representation of the air–sea interaction over the tropical Indian Ocean and subtropical WNP in CGCMs is a key for reducing the prediction spread and for improving the long-lead seasonal forecast over the WNP and East Asia.     

南京信息工程大学气候预测系统1.0版简介

南京信息工程大学气候预测系统1.0版(NUIST CFS1.0)是基于日本海洋科学技术开发机构(JAMSTEC)的SINTEX-F模式发展而来,可以实现对全球气候异常的季节-年际预测。对过去近40 a的集合历史回报预测试验结果的评估发现,该预测系统对热带太平洋和印度洋海温异常具有良好的预测技巧,并且该系统能提前1.5~2 a对ENSO(Nino3.4指数)做出有技巧的预测(即相关系数达0.5),同时也可以提前1~2个季节对印度洋偶极子(IOD)做出有较高技巧的预测,展现了对主要热带气候信号的良好预测技巧。但是与国内外所有动力模式预测系统类似,该系统对东亚地区的气候异常预测还存在较大的不足。考虑到ENSO对东亚地区气候异常的强烈影响,本文尝试去除与ENSO预测相关的系统偏差来初步订正东亚地区夏季温度异常和降水距平百分率的预测结果。对比订正前后的结果表明,这一简单的订正方法有助于提高我国气候异常的预测准确率。同时选取2019年夏季气温异常和降水距平百分率的实时预测结果作为个例进行分析,发现订正能够提供一定的技巧改善,但与观测结果相比仍存在较大偏差,需要在今后的工作中不断改进完善。此外,本文也初步评估了NUIST CFS1.0对我国冬春季的气候预测技巧,并提供了经简单订正后的2019/2020年冬季和2020年春季的实时预测结果。     

Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts

This paper examines the connection between the probability of precipitation and forecast amounts from Weather Research and Forecasting (WRF) model runs over Central and West Africa. A one season period (June–September 2010) was used to investigate the quantitative precipitation forecast–probability relationship. The predictive capability of this relationship was then tested on an independent sample of data (June–September 2011); 2010 and 2011 were wet and dry years, respectively. The results show that rainfall is less likely to occur in those areas where the model indicates no precipitation than it is elsewhere in the domain. Rainfall is more likely to occur in those regions where precipitation is predicted, especially where the predicted precipitation amounts are largest. The probabilities of rainfall forecasts based on this relationship are found to possess skill as measured by relative operating characteristic curves, reliability diagrams, and Brier skill scores. Skillful forecasts from the technique exist throughout 24-h periods for which WRF output was available. The results suggest that this forecasting tool might assist forecasters throughout the season in a wide variety of weather events and not only in areas of difficult-to-forecast convective systems.     

Characteristics of the Asian–Pacific Oscillation in Boreal Summer Simulated by BCC_CSM with Different Horizontal Resolutions

The summer Asian–Pacific Oscillation(APO) is a major teleconnection pattern that reflects the zonal thermal contrast between East Asia and the North Pacific in the upper troposphere. The performance of Beijing Climate Center Climate System Models(BCC CSMs) with different horizontal resolutions, i.e., BCC CSM1.1 and BCC CSM1.1(m), in reproducing APO interannual variability, APO-related precipitation anomalies, and associated atmospheric circulation anomalies, is evaluated.The results show that BCC CSM1.1(m) can successfully capture the interannual variability of the summer APO index. It is also more capable in reproducing the APO's spatial pattern, compared to BCC CSM1.1, due to its higher horizontal resolution. Associated with a positive APO index, the northward-shifted and intensified South Asian high, strengthened extratropical westerly jet, and tropical easterly jet in the upper troposphere, as well as the southwesterly monsoonal flow over North Africa and the Indian Ocean in the lower troposphere, are realistically represented by BCC CSM1.1(m), leading to an improvement in reproducing the increased precipitation over tropical North Africa, South Asia, and East Asia, as well as the decreased precipitation over subtropical North Africa, Japan, and North America. In contrast, these features are less consistent with observations when simulated by BCC CSM1.1. Regression analysis further indicates that surface temperature anomalies over the North Pacific and the southern and western flanks of the Tibetan Plateau are reasonably reproduced by BCC CSM1.1(m), which contributes to the substantial improvement in the simulation of the characteristics of summer APO compared to that of BCC CSM1.1.     

Multimodel based superensemble forecasts for short and medium range NWP over various regions of Africa

Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis. The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase. This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and all the members. Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within the ITCZ over the region. These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range weather forecasts in real-time.     

La Niña diversity and Northwest Indian Ocean Rim teleconnections

The differences in tropical Pacific sea surface temperature (SST) expressions of El Niño-Southern Oscillation (ENSO) events of the same phase have been linked with different global atmospheric circulation patterns. This study examines the dynamical forcing of precipitation during October–December (OND) and March–May (MAM) over East Africa and during December–March (DJFM) over Central-Southwest Asia for 1950–2010 associated with four tropical Pacific SST patterns characteristic of La Niña events, the cold phase of ENSO. The self-organizing map method along with a statistical distinguishability test was used to isolate La Niña events, and seasonal precipitation forcing was investigated in terms of the tropical overturning circulation and thermodynamic and moisture budgets. Recent La Niña events with strong opposing SST anomalies between the central and western Pacific Ocean (phases 3 and 4), force the strongest global circulation modifications and drought over the Northwest Indian Ocean Rim. Over East Africa during MAM and OND, subsidence is forced by an enhanced tropical overturning circulation and precipitation reductions are exacerbated by increases in moisture flux divergence. Over Central-Southwest Asia during DJFM, the thermodynamic forcing of subsidence is primarily responsible for precipitation reductions, with moisture flux divergence acting as a secondary mechanism to reduce precipitation. Eastern Pacific La Niña events in the absence of west Pacific SST anomalies (phases 1 and 2), are associated with weaker global teleconnections, particularly over the Indian Ocean Rim. The weak regional teleconnections result in statistically insignificant precipitation modifications over East Africa and Central-Southwest Asia.     

Variability and trends of local/regional scale surface climate in northern Africa during the twentieth century

Four regions are detected in northern Africa (20° W–40° E, 0–30° N) by applying the cluster analysis method on the annual rainfall anomalies of the period 1901–2000. The first region (R1), an arid land, covers essentially the north of 17.75° N from west to east of the study zone. The second region (R2), a semiarid land with a Sahelian climate, less warm than the dry climate of R1, is centred on Chad, with almost regular extension to the west towards Mauritania, and to the east, including the north of the Central African Republic and the Sudan. The region 3 (R3), a wet land, is centred on the Ivory Coast and covers totally Liberia, the south part of Ghana, Togo, Benin and the southwest of Nigeria. The fourth region (R4), corresponding to the wet equatorial forest, covers a part of Senegal, the Central Africa, the south of Sudan and a part of Ethiopia. An analysis of observed temperature and precipitation variability and trends throughout the twentieth century over these regions is presented. Summer, winter and annual data are examined using a range of variability measures. Statistically, significant warming trends are found over the majority of regions. The trends have a magnitude of up to 1.5 K per century. Only a few precipitation trends are statistically significant. Regional temperature and precipitation show pronounced variability at scales from interannual to multi-decadal. The interannual variability shows significant variations and trends throughout the century, the latter being mostly negative for precipitation and both positive and negative for temperature. Temperature and precipitation anomalies show a chaotic-type behaviour in which the regional conditions oscillate around the long-term mean trend and occasionally fall into long-lasting (up to 10 years or more) anomaly regimes. A generally modest temporal correlation is found between anomalies of different regions and between temperature and precipitation anomalies for the same region. This correlation is mostly positive for temperature in cases of adjacent regions. Several cases of negative interregional precipitation anomaly correlation are found. The El Niño Southern Oscillation significantly affects the anomaly variability patterns over a number of regions, mainly regions 3 (R3) and 4 (R4), while the North Atlantic Oscillation significantly affects the variability over arid and semiarid regions, R1 and R2.     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

Secular spring rainfall variability at local scale over Ethiopia: trend and associated dynamics

Spring rainfall secular variability is studied using observations, reanalysis, and model simulations. The joint coherent spatio-temporal secular variability of gridded monthly gauge rainfall over Ethiopia, ERA-Interim atmospheric variables and sea surface temperature (SST) from Hadley Centre Sea Ice and SST (HadISST) data set is extracted using multi-taper method singular value decomposition (MTM-SVD). The contemporaneous associations are further examined using partial Granger causality to determine presence of causal linkage between any of the climate variables. This analysis reveals that only the northwestern Indian Ocean secular SST anomaly has direct causal links with spring rainfall over Ethiopia and mean sea level pressure (MSLP) over Africa inspite of the strong secular covariance of spring rainfall, SST in parts of subtropical Pacific, Atlantic, Indian Ocean and MSLP. High secular rainfall variance and statistically significant linear trend show consistently that there is a massive decline in spring rain over southern Ethiopia. This happened concurrently with significant buildup of MSLP over East Africa, northeastern Africa including parts of the Arabian Peninsula, some parts of central Africa and SST warming over all ocean basins with the exception of the ENSO regions. The east-west pressure gradient in response to the Indian Ocean warming led to secular southeasterly winds over the Arabian Sea, easterly over central Africa and equatorial Atlantic. These flows weakened climatological northeasterly flow over the Arabian Sea and southwesterly flow over equatorial Atlantic and Congo basins which supply moisture into the eastern Africa regions in spring. The secular divergent flow at low level is concurrent with upper level convergence due to the easterly secular anomalous flow. The mechanisms through which the northwestern Indian Ocean secular SST anomaly modulates rainfall are further explored in the context of East Africa using a simplified atmospheric general circulation model (AGCM) coupled to mixed-layer oceanic model. The rainfall anomaly (with respect to control simulation), forced by the northwestern Indian Ocean secular SST anomaly and averaged over the 30-year period, exhibits prevalence of dry conditions over East and equatorial Africa in agreement with observation. The atmospheric response to secular SST warming anomaly led to divergent flow at low levels and subsidence at the upper troposphere over regions north of 5^° S on the continent and vice versa over the Indian Ocean. This surface difluence over East Africa, in addition to its role in suppressing convective activity, deprives the region of moisture supply from the Indian Ocean as well as the Atlantic and Congo basins.     

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。     

CMIP5 simulated climate conditions of the Greater Horn of Africa (GHA). Part 1: contemporary climate

The present study is a preliminary interrogation of the ability of ten Earth System Models (ESMs) from the fifth phase of coupled model intercomparison project to characterize seasonal and annual mean precipitation cycle over the Greater Horn of Africa region. Each ESM had at least 2 ensemble members. In spite of distributional anomalies of observations, ESM ensemble means were examined on the basis of gridded precipitation data. Majority of the ten ESMs analyzed correctly reproduce the mean seasonal and annual cycle of precipitation for the period 1979–2008 as compared to gridded satellite-derived observations. At the same time our analysis shows significant biases in individual models depending on region and season. Specifically, a modest number of models were able to capture correctly the peaks of bimodal (MAM and OND) and JJAS rainfall while a few either dragged the onset to subsequent months or displaced the locations of seasonal rainfall further north. Nearly all models were in agreement with their representation of the zonal orientation of spatial pattern of the leading EOF rainfall modes; more so, enhanced precipitation over the Indian Ocean and a dipole mode of precipitation pattern are captured in the first and second mode respectively. Further, the corresponding EOF time series of the ESMs rainfall modes were all in phase with observations. However, all models output were positively biased against observations, with large medians and varied range of anomalies. Therefore, caution needs to be taken when choosing models for applications over the region, especially when ensemble means have to be considered.     

Modulation of monthly precipitation patterns over East China by the Pacific Decadal Oscillation

Previous studies suggest that the Pacific Decadal Oscillation (PDO) modulates annual and summer precipitation patterns over East China. In this study, the effect of the PDO on monthly precipitation anomalies over this region is investigated. The new results show that the effect is month-dependent. The well-known North–South dipole patterns of annual precipitation are dominated by the July–August precipitation. In other months, the corresponding patterns vary in strength, position, and even shape. For example, the May and June precipitation patterns show opposite signs to the July–August or annual mean patterns, whereas the September–December monthly precipitation anomalies show a triple pattern. Monthly precipitation patterns over East China are largely determined by large-scale moisture transport controlled by atmospheric circulation. The PDO affects East China precipitation patterns by modulating the large-scale circulation pattern.     

Recent drought and precipitation tendencies in Ethiopia

In 2011, drought in the Horn of Africa again made news headlines. This study aims to quantify the meteorological component of this and other drought episodes in Ethiopia since 1971. A monthly precipitation data set for 14 homogeneous rainfall zones was constructed based on 174 gauges, and the standardized precipitation index was calculated on seasonal, annual, and biannual time scales. The results point to 2009 as a year of exceptionally widespread drought. All zones experienced drought at the annual scale, although in most zones, previous droughts were more extreme. Nationally, 2009 was the second driest year, surpassed only by the historic year 1984. Linear regression analysis indicates a precipitation decline in southern Ethiopia, during both February–May and June–September. In central and northern Ethiopia, the analysis did not provide evidence of similar tendencies. However, spring droughts have occurred more frequently in all parts of Ethiopia during the last 10–15 years.     

Hydroclimatic trends during 1950–2018 over global land

Global hydroclimatic changes from 1950 to 2018 are analyzed using updated data of land precipitation, streamflow, and an improved form of the Palmer Drought Severity Index. The historical changes are then compared with climate model-simulated response to external forcing to determine how much of the recent change is forced response. It is found that precipitation has increased from 1950 to 2018 over mid-high latitude Eurasia, most North America, Southeast South America, and Northwest Australia, while it has decreased over most Africa, eastern Australia, the Mediterranean region, the Middle East, and parts of East Asia, central South America, and the Pacific coasts of Canada. Streamflow records largely confirm these precipitation changes. The wetting trend over Northwest Australia and Southeast South America is most pronounced in austral summer while the drying over Africa and wetting trend over mid-high latitude Eurasia are seen in all seasons. Coupled with the drying caused by rising surface temperatures, these precipitation changes have greatly increased the risk of drought over Africa, southern Europe, East Asia, eastern Australia, Northwest Canada, and southern Brazil. Global land precipitation and continental freshwater discharge show large interannual and inter-decadal variations, with negative anomalies during El Niño and following major volcanic eruptions in 1963, 1982, and 1991; whereas their decadal variations are correlated with the Interdecadal Pacific Oscillation (IPO) with IPO’s warm phase associated with low land precipitation and continental discharge. The IPO and Atlantic multidecadal variability also dominate multidecadal variations in land aridity, accounting for 90 % of the multidecadal variance. CMIP5 multi-model ensemble mean shows decreased precipitation and runoff and increased risk of drought during 1950–2018 over Southwest North America, Central America, northern and central South America (including the Amazon), southern and West Africa, the Mediterranean region, and Southeast Asia; while the northern mid-high latitudes, Southeast South America, and Northwest Australia see increased precipitation and runoff. The consistent spatial patterns between the observed changes and the model-simulated response suggest that many of the observed drying and wetting trends since 1950 may have resulted at least partly from historical external forcing. However, the drying over Southeast Asia and wetting over Northwest Australia are absent in the 21st century projections.