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.     

Daily modes of South Asian summer monsoon variability in the NCEP climate forecast system

The leading modes of daily variability of the Indian summer monsoon in the climate forecast system (CFS), a coupled general circulation model, of the National Centers for Environmental Predictions (NCEP) are examined. The space?Ctime structures of the daily modes are obtained by applying multi-channel singular spectrum analysis (MSSA) on the daily anomalies of rainfall. Relations of the daily modes to intraseasonal and interannual variability of the monsoon are investigated. The CFS has three intraseasonal oscillations with periods around 106, 57 and 30?days with a combined variance of 7%. The 106-day mode has spatial structure and propagation features similar to the northeastward propagating 45-day mode in the observations except for its longer period. The 57-day mode, despite being in the same time scale as of the observations has poor eastward propagation. The 30-day mode is northwestward propagating and is similar to its observational counterpart. The 106-day mode is specific to the model and should not be mistaken for a new scale of variability in observations. The dominant interannual signal is related to El Ni?o-Southern Oscillation (ENSO), and, unlike in the observations, has maximum variance in the eastern equatorial Indian Ocean. Although the Indian Ocean Dipole (IOD) mode was not obtained as a separate mode in the rainfall, the ENSO signal has good correlations with the dipole variability, which, therefore, indicates the dominance of ENSO in the model. The interannual variability is largely determined by the ENSO signal over the regions where it has maximum variance. The interannual variability of the intraseasonal oscillations is smaller in comparison.     

Diagnostics of subseasonal prediction biases of the Asian summer monsoon by the NCEP climate forecast system

Biases of subseasonal prediction of the Asian summer monsoon are diagnosed using daily data from the hindcasts of 45-day integrations by the National Centers for Environmental Prediction Climate Forecast System version 2. The retrospective forecasts often show apparent systematic biases, which are mostly represented by the underestimation of the whole Asian monsoon. Biases depend not only on lead time, but also on the stage of monsoon evolution. An abrupt turning point of bias development appears around late June and early July, when ensemble spread and bias growth of winds and precipitation show a significant change over the northwestern Pacific (NWP) and the South Asian summer monsoon (SASM) region. The abrupt turning of bias development of winds, precipitation, and surface temperature is also captured by the first two modes of multivariate empirical orthogonal function analysis. Several features appear associated with the abrupt change in bias development: the western Pacific subtropical high (WPSH) begins its first northward jump and the surface temperature over the Tibetan Plateau commences a transition from warm bias to cold bias, and a reversal of surface temperature biases occurs in the eastern tropical Indian Ocean and the SASM region. The shift of WPSH position and the transition of surface thermal bias show close relationships with the formation of bias centers in winds and precipitation. The rapid growth in bias due to the strong internal atmospheric variability during short leads seems to mainly account for the weak WPSH and SASM in the model. However, at certain stages, particularly for longer-lead predictions, the biases of slowly varying components may also play an important role in bias development of winds and precipitation.     

Evaluation of NCEP TIGGE short-range forecast for Indian summer monsoon intraseasonal oscillation

Potential evapotranspiration (PET) is one of the most critical parameters in the research on agro-ecological systems. The computational methods for the estimation of PET vary in data demands from very simple (empirically based), requiring only information based on air temperatures, to complex ones (more physically based) that require data on radiation, relative humidity, wind speed, etc. The current research is focused on three study areas in Greece that face different climatic conditions due to their location. Twelve PET formulae were used, analyzed and inter-compared in terms of their sensitivity regarding their input coefficients for the Ardas River basin in north-eastern Greece, Sperchios River basin in Central Greece and Geropotamos River basin in South Greece. The aim was to compare all the methods and conclude to which empirical PET method(s) better represent the PET results in each area and thus should be adopted and used each time and which factors influence the results in each case. The results indicated that for the areas that face Mediterranean climatic conditions, the most appropriate method for the estimation of PET was the temperature-based, Hamon’s second version (PET_Ham2). Furthermore, the PET_Ham2 was able to estimate PET almost similarly to the average results of the 12 equations. For the Ardas River basin, the results indicated that both PET_Ham2 and PET_Ham1 can be used to estimate PET satisfactorily. Moreover, the temperature-based equations have proven to produce better results, followed by the radiation-based equations. Finally, PET_ASCE, which is the most commonly used PET equation, can also be applied occasionally in order to provide satisfactory results.     

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

This study investigates the El Niño Southern Oscillation (ENSO) teleconnections to tropical Indian Ocean (TIO) and their relationship with the Indian summer monsoon in the coupled general circulation model climate forecast system (CFS). The model shows good skill in simulating the impact of El Niño over the Indian Oceanic rim during its decay phase (the summer following peak phase of El Niño). Summer surface circulation patterns during the developing phase of El Niño are more influenced by local Sea Surface Temperature (SST) anomalies in the model unlike in observations. Eastern TIO cooling similar to that of Indian Ocean Dipole (IOD) is a dominant model feature in summer. This anomalous SST pattern therefore is attributed to the tendency of the model to simulate more frequent IOD events. On the other hand, in the model baroclinic response to the diabatic heating anomalies induced by the El Niño related warm SSTs is weak, resulting in reduced zonal extension of the Rossby wave response. This is mostly due to weak eastern Pacific summer time SST anomalies in the model during the developing phase of El Niño as compared to observations. Both eastern TIO cooling and weak SST warming in El Niño region combined together undermine the ENSO teleconnections to the TIO and south Asia regions. The model is able to capture the spatial patterns of SST, circulation and precipitation well during the decay phase of El Niño over the Indo-western Pacific including the typical spring asymmetric mode and summer basin-wide warming in TIO. The model simulated El Niño decay one or two seasons later, resulting long persistent warm SST and circulation anomalies mainly over the southwest TIO. In response to the late decay of El Niño, Ekman pumping shows two maxima over the southern TIO. In conjunction with this unrealistic Ekman pumping, westward propagating Rossby waves display two peaks, which play key role in the long-persistence of the TIO warming in the model (for more than a season after summer). This study strongly supports the need of simulating the correct onset and decay phases of El Niño/La Niña for capturing the realistic ENSO teleconnections. These results have strong implications for the forecasting of Indian summer monsoon as this model is currently being adopted as an operational model in India.     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

The latest version of the state-of-the-art global land–atmosphere–ocean coupled climate forecast system of NCEP has shown considerable improvement in various aspects of the Indian summer monsoon. However, climatological mean dry bias over the Indian sub-continent is further increased as compared to the previous version. Here we have attempted to link this dry bias with climatological mean bias in the Eurasian winter/spring snow, which is one of the important predictors of the Indian summer monsoon rainfall (ISMR). Simulation of interannual variability of the Eurasian snow and its teleconnection with the ISMR are quite reasonable in the model. Using composite analysis it is shown that a positive snow anomaly, which is comparable to the systematic bias in the model, results into significant decrease in the summer monsoon rainfall over the central India and part of the Equatorial Indian Ocean. Decrease in the summer monsoon rainfall is also found to be linked with weaker northward propagation of intraseasonal oscillation (ISO). A barotropic stationary wave triggered by positive snow anomaly over west Eurasia weakens the upper level monsoon circulation, which in turn reduces the zonal wind shear and hence, weakens the northward propagation of summer monsoon ISOs. A sensitivity experiment by reducing snow fall over Eurasian region causes decrease in winter and spring snow depth, which in turn leads to decrease in Indian summer monsoon rainfall. Results from the sensitivity experiment corroborate with those of composite analysis based on long free run. This study suggests that further improvements in the snow parametrization schemes as well as Arctic sea ice are needed to reduce the Eurasian snow bias during winter/spring, which may reduce the dry bias over Indian sub-continent and hence predictability aspect of the model.     

ENSO, IOD and Indian Summer Monsoon in NCEP climate forecast system

El Ni?o-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Indian Summer Monsoon rainfall features are explored statistically and dynamically using National Centers for Environment Prediction (NCEP) Climate Forecast System (CFSv1) freerun in relation to observations. The 100?years of freerun provides a sufficiently long homogeneous data set to find out the mean state, periodicity, coherence among these climatic events and also the influence of ENSO and IOD on the Indian monsoon. Differences in the occurrence of seasonal precipitation between the observations and CFS freerun are examined as a coupled ocean–atmosphere system. CFS simulated ENSO and IOD patterns and their associated tropical Walker and regional Hadley circulation in pure ENSO (PEN), pure IOD (PIO) and coexisting ENSO-IOD (PEI) events have some similarity to the observations. PEN composites are much closer to the observation as compared to PIO and PEI composites, which suggest a better ENSO prediction and its associated teleconnections as compared to IOD and combined phenomenon. Similar to the observation, the model simulation also show that the decrease in the Indian summer monsoon rainfall during ENSO phases is associated with a descending motion of anomalous Walker circulation and the increase in the Indian summer monsoon rainfall during IOD phase is associated with the ascending branch of anomalous regional Hadley circulation. During co-existing ENSO and IOD years, however, the fate of Indian summer monsoon is dictated by the combined influence of both of them. The shift in the anomalous descending and ascending branches of the Walker and Hadley circulation may be somewhat attributed to the cold (warm) bias over eastern (western) equatorial Indian Ocean basin, respectively in the model. This study will be useful for identifying some of the limitations of the CFS model and consequently it will be helpful in improving the model to unravel the realistic coupled ocean–atmosphere interactions for the better prediction of Indian Summer Monsoon.     

Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model

This study has identified probable factors that govern ISMR predictability. Furthermore, extensive analysis has been performed to evaluate factors leading to the predictability aspect of Indian Summer Monsoon Rainfall (ISMR) using uncoupled and coupled version of National Centers for Environmental Prediction Coupled Forecast System (CFS). It has been found that the coupled version (CFS) has outperformed the uncoupled version [Global Forecast System (GFS)] of the model in terms of prediction of rainfall over Indian land points. Even the spatial distribution of rainfall is much better represented in the CFS as compared to that of GFS. Even though these model skills are inadequate for the reliable forecasting of monsoon, it imparts the capacious knowledge about the model fidelity. The mean monsoon features and its evolution in terms of rainfall and large-scale circulation along with the zonal and meridional shear of winds, which govern the strength of the monsoon, are relatively closer to the observation in the CFS as compared to the GFS. Furthermore, sea surface temperature–rainfall relation is fairly realistic and intense in the coupled version of the model (CFS). It is found that the CFS is able to capture El Niño Southern Oscillation ISMR (ENSO-ISMR) teleconnections much strongly as compared to GFS; however, in the case of Indian Ocean Dipole ISMR teleconnections, GFS has the larger say. Coupled models have to be fine-tuned for the prediction of the transition of El Niño as well as the strength of the mature phase has to be improved. Thus, to sum up, CFS tends to have better predictive skill on account of following three factors: (a) better ability to replicate mean features, (b) comparatively better representation of air–sea interactions, and (c) much better portrayal of ENSO-ISMR teleconnections. This study clearly brings out that coupled model is the only way forward for improving the ISMR prediction skill. However, coupled model’s spurious representation of SST variability and mean model bias are detrimental in seasonal prediction.     

夏季江淮流域持久性旱涝现象的环流特征

基于美国环境预测中心提供的CFSv2模式回报数据以及淮河流域172个气象台站的气温降水观测数据, 评估了该模式对淮河流域夏季气温降水的预测能力。结果表明:模式对气温气候平均态的模拟与实况较为接近, 降水虽然能够反映出南多北少的分布特征, 但气候平均值明显偏小。模式对于气温和降水均方差的模拟均偏小。从频率分布来看, 回报气温的频率分布与实况较为接近, 回报降水不仅存在很大的负偏差, 出现异常降水的频率也比实况明显偏低。对ROC曲线的分析进一步表明模式预测高温事件的能力明显好于低温事件, 预测降水异常事件的能力在不同起报月有差异。从主要模态的时空结构来看, 模式能够反映出其空间结构, 对于增暖的趋势模拟也相当不错, 但增暖的速率高于实况, 虽然模式没有能够反映出降水主要模态的年代际变化特征, 但对于年际变化有较高的预测技巧。这些评估为短期气候预测和模式解释应用提供了参考。

     

An assessment of the surface climate in the NCEP climate forecast system reanalysis

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere?Cocean?Csea ice?Cland forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998?C2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately.     

Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2

Subseasonal forecast skills and biases of global summer monsoons are diagnosed using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2. Predictions for subseasonal variability of zonal wind and precipitation are generally more skillful over the Asian and Australian monsoon regions than other monsoon regions. Climatologically, forecasts for the variations of dynamical monsoon indices have high skills at leads of about 2 weeks. However, apparent interannual differences exist, with high skills up to 5 weeks in exceptional cases. Comparisons for the relationships of monsoon indices with atmospheric circulation and precipitation patterns between skillful and unskillful forecasts indicate that skills for subseasonal variability of a monsoon index depend partially on the degree to which the observed variability of the index attributes to the variation of large-scale circulation. Thus, predictions are often more skillful when the index is closely linked to atmospheric circulation over a broad region than over a regional and narrow range. It is also revealed that, the subseasonal variations of biases of winds, precipitation, and surface temperature over various monsoon regions are captured by a first mode with seasonally independent biases and a second mode with apparent phase transition of biases during summer. The first mode indicates the dominance of overall weaker-than-observed summer monsoons over major monsoon regions. However, at certain stages of monsoon evolution, these underestimations are regionally offset or intensified by the time evolving biases portrayed by the second mode. This feature may be partially related to factors such as the shifts of subtropical highs and intertropical convergence zones, the reversal of biases of surface temperature over some monsoon regions, and the transition of regional circulation system. The significant geographical differences in bias growth with increasing lead time reflect the distinctions of initial memory capability of the climate system over different monsoon regions.     

Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast system

In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics.     

Status of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon

National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) is selected to play a lead role for monsoon research (seasonal prediction, extended range prediction, climate prediction, etc.) in the ambitious Monsoon Mission project of Government of India. Thus, as a prerequisite, a detail analysis for the performance of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon (ISM) is attempted. It is found that the mean monsoon simulations by CFS in its long run are at par with the IPCC models. The spatial distribution of rainfall in the realm of Indian subcontinent augurs the better results for CFS as compared with the IPCC models. The major drawback of CFS is the bifurcation of rain types; it shows almost 80–90 % rain as convective, contrary to the observation where it is only 50–65 %; however, the same lacuna creeps in other models of IPCC as well. The only respite is that it realistically simulates the proper ratio of convective and stratiform rain over central and southern part of India. In case of local air–sea interaction, it outperforms other models. However, for monsoon teleconnections, it competes with the better models of the IPCC. This study gives us the confidence that CFS can be very well utilized for monsoon studies and can be safely used for the future development for reliable prediction system of ISM.     

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

FSU-GSM forecast error sensitivity to initial conditions: Application to Indian summer monsoon

Summary The full-physics adjoint of the FSU Global Spectral Model of version T42L12 is applied to carry out sensitivity analysis of the localized model forecast error to the initial conditions for a case test occurring on June 8, 1988 during the Indian summer monsoon. The results show that adjoint sensitivity based on ECMWF analysis can be used to identify regions with large analysis uncertainties. The conclusion is that more observations are required over the northern Bay of Bengal to improve the quality of analyses so as to ameliorate the model forecast skill.With 17 Figures