Asian summer monsoon prediction in ECMWF System 4 and NCEP CFSv2 retrospective seasonal forecasts
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The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982–2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.
The global monsoon (Webster et al. 1998; Wang et al. 2011a) is a major component of global climate system, affecting the global climate and weather such as floods, droughts and other climate extremes. The Asian monsoon influences almost half of the world’s population with their agriculture, life and society depending on monsoon climate. Therefore, understanding the physical processes that determine the character of monsoon systems and also providing accurate extended range predictions on a seasonal timescale is crucial for the economy and policy planning in the monsoon regions. Individual dynamical models and multi-model simulations have played an important role in monsoon prediction. It has been shown that the sensitivity in monsoon prediction/simulation depends on model features, primarily on the presence of ocean–atmosphere coupling, model resolution and improvement of the model physics (Kang et al. 2002; Wang et al. 2005a, and many others). These model improvements are providing substantial advances in seasonal prediction. Several coupled model hindcast intercomparison projects have shown that recent ocean–atmosphere coupled models are able to capture the gross feature of Asian monsoon variability from the intraseasonal to seasonal timescales (Kumar et al. 2005; Wang et al. 2005b; Kim et al. 2008; Kug et al. 2008; Wang et al. 2008; Lee et al. 2010).
Several modeling centers routinely provide operational seasonal forecast with ocean–atmosphere coupled model systems. In this study we focus on the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and Climate Forecast System version 2 from the National Center for Environmental Prediction (NCEP CFSv2). ECMWF and NCEP have been operating coupled ocean–atmosphere seasonal prediction systems since 1997 and 2004, respectively. ECMWF System 3 was introduced in March 2007 (Anderson et al. 2007), showing improved skill for seasonal prediction relative to previous versions (Tompkins and Feudale 2010; Stockdale et al. 2011). Recently, ECMWF upgraded its operational seasonal forecasts to System 4, which has been operational since late 2011. System 4 utilizes ECMWF’s the most recent atmospheric model version, with higher resolution and a higher top of the atmosphere, more ensemble members and a larger reforecast data set (Molteni et al. 2011). The NCEP CFSv1 has been examined in simulating and predicting El Nino-Southern Oscillation (ENSO) variability (Wang et al. 2005b); Asian-Australian/Indian monsoon (Yang et al. 2008; Liang et al. 2009; Pattanaik and Kumar 2010) and climatic variation in the U.S. (Yang et al. 2009). The NCEP CFSv2 (http://cfs.ncep.noaa.gov/) is an upgraded version of CFSv1 (Saha et al. 2006) and became operational since 2011. The NCEP CFSv2 represents a substantial change from CFSv1 in all aspects of the forecast system including model components, data assimilation system and ensemble configuration, and shows important advances in operational prediction (Weaver et al. 2011).
The capabilities of individual coupled systems for predicting the Asian monsoon have been analyzed previously for various target seasons, different regions and a wide range of variables. However, the ECMWF system 4 and NCEP CFSv2, which are the most recently upgraded systems, have not been compared using the same validation methods. An overall assessment of simulation ability and prediction skill of these two modeling systems will be of interests to the scientific, operational and user communities, providing information to support the choice of which model(s) to use. In Kim et al. (2012), the seasonal prediction skill and the simulation ability for ENSO teleconnection for the boreal winter in ECMWF system 4 and NCEP CFSv2 have been compared. Here, we compare the simulated climate variability and seasonal prediction skill for the Northern Hemisphere (NH) summer monsoon, with special focus on the Asian monsoon region. Section 2 introduces details of the reforecasts and observation data sets. Section 3 examines the simulation and prediction of monsoon in the two modeling systems. Section 4 focuses on the ENSO and the monsoon prediction capabilities. Discussion for the failure in monsoon prediction is given in Sect. 5 and the summary in Sect. 6.
ECMWF System 4 (hereafter SYS4) and NCEP CFSv2 (hereafter CFSv2) are fully coupled atmosphere–ocean forecast systems that provide operational seasonal predictions together with reforecast data to evaluate and calibrate the models. The SYS4 seasonal reforecasts include 15 member ensembles and consist of 7 month simulations initialized on the 1st day of every month from 1981 until 2010. Details for the ECMWF System 4 can be found in at www.ecmwf.int/products/forecasts/seasonal/documentation/system4. The CFSv2 reforecasts are a set of 9-month reforecasts initiated every 5th day with four ensemble members for the period from 1982 to 2010. Initial conditions for the atmosphere and ocean come from the NCEP Climate Forecast System Reanalysis (CFSR, Saha et al. 2010). Details of the system can be found in http://cfs.ncep.noaa.gov.
We investigated the forecasts initialized in spring with the aim of assessing the prediction skill of the boreal summer season. We match the ensemble size, as well as lead-time for the comparison of the SYS4 and CFSv2 forecasts, as prediction skill depends strongly on the ensemble size (Kumar and Hoerling 2000). The SYS4 reforecast set consists of 15 ensemble members initialized on May 1st. The CFSv2 set consists of 16 ensemble members initialized from April 21st to May 6th. Particular variables are followed from their initial date through June to August (JJA), which we define as the period of the NH summer. A total of 28 NH summers are examined in the 1982–2009 period.
For forecast evaluation, Global Precipitation Climatology Project (GPCP) version 2.1 combined precipitation dataset (Adler et al. 2003) is used. Sea surface temperature (SST) data is obtained from monthly NOAA Optimum Interpolation SST V2 (Reynolds et al. 2002). The wind data at 850 hPa and 200 hPa are obtained from the ERA-Interim (Berrisford et al. 2009) and CFSR products. ERA-Interim (hereafter ERA) is the latest global atmospheric reanalysis dataset produced by the ECMWF. The CFSR is a major improvement over the first generation NCEP reanalyses (NCEP R1 and R2) and is the product of a coupled ocean–atmosphere–land system at higher spatial resolution (Saha et al. 2010).
3 Monsoon simulation and prediction
3.1 Seasonal mean bias and prediction skill
The monsoon climate is defined as a shift in the wind direction caused by differential heating of major landmasses and adjacent oceans that brings about seasonal wet and dry rainfall anomalies over the monsoon area. Figure 2 shows the climatology of 850 hPa lower tropospheric wind in ERA interim (Fig. 2a) and biases in both modeling systems (Fig. 2b–e). As the climatology patterns are slightly different between ERA interim and CFSR, we show the bias from both ERA interim (Fig. 2b, c) and CFSR (Fig. 2d, e). An issue in evaluating the reforecast is the choice of the reanalysis. Kim et al. (2012) showed that the predictive skill of the surface temperature of the two models depends on the reanalysis dataset used due to discrepancies and uncertainties associated with the reanalysis. Therefore, the analyses in this study were conducted using both reanalysis datasets.
The observed NH summer mean circulation pattern (Fig. 2a) is characterized by the easterly trade wind over the equatorial Pacific, anti-cyclonic flow in the North Pacific, southwest monsoon flow over the tropical Asia and the cross-equatorial Somali Jet in the Indian monsoon region. These major circulation features are captured in each reanalysis set. However, the southwest monsoon flow and the Somali Jet are stronger in SYS4 but weaker in CFSv2 relative to both reanalyses (Fig. 2b–e). The strong monsoon flow in SYS4 is dynamically consistent with the high precipitation in the western Indian Ocean, while the bias of monsoon flow in CFSv2 is consistent with the wet anomaly in the equatorial Indian Ocean. In CFSv2, the southeasterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker than observed in a manner similar to CFSv1 (Yang et al. 2008). In both modeling systems, the stronger-than-observed easterly trade wind and convergence over the western North Pacific is consistent with the wet bias over the western Pacific warm pool and central Pacific (Fig. 1c, d). The weaker-than-observed anticyclonic flow over the western North Pacific and weaker southerly from South China Sea is consistent with the deficiency of East Asian monsoon precipitation as less water vapor is transported to the monsoon region. These biases over the Asian monsoon region were also found in previous studies (Yang et al. 2008; Lee et al. 2010). The mean bias in the two simulations relative to the CFSR shows similar characteristics to the mean bias with ERA interim but with regional differences (Fig. 2d–e).
3.2 The Asian summer monsoon
Comparing with the ERA interim, the interannual variation of WYI is quite well reproduced by both reforecasts, with correlation coefficient of 0.78 and 0.74 for SYS4 and CFSv2, respectively, which exceeds 99 % statistical significant level. The correlations between CFSR and the predicted index are 0.63 for both modeling systems (Fig. 5a). Although the low level zonal wind is not well reproduced in either reforecast data set (Fig. 3c), the high skill of WYI could result from the high skill in the upper level zonal wind (not shown). However, the IMI shows insignificant skill, ranging from 0.2 to 0.3 in both reforecast products (Fig. 5b). Both models perform poorly in capturing the variability of the monsoon component measured by IMI, given the difficulties in simulating the circulation field for Indian monsoon region. The low prediction skill in IMI could result from the small area used to define the index. The WNPMI (Fig. 5c) is well captured in both modeling systems, having correlation coefficient ranging from 0.57 to 0.77.
4 ENSO and the monsoon prediction
Previous results show that the Asian monsoon is strongly modulated by the ENSO forcing in both the observation and model predictions. ENSO is generally well predicted (up to 6 month) in seasonal forecast models (Jin et al. 2008) at least after the April/May “predictability barrier” (Webster and Yang 1992; Webster 1995). For SYS4 and CFSv2, the correlation coefficients between the observed and predicted JJA Nino 3.4 index is 0.87 and 0.83, respectively. The slightly lower skill in CFSv2 compared to SYS4 could result from the shift in SST bias associated with the changes in satellite observations that were assimilated in the CFSR (Xue et al. 2011; Wang et al. 2011b; Kim et al. 2012).
5 Discussion: Failure in Asian monsoon prediction
Although the two modeling systems have less skill in predicting the Asian monsoon than ENSO alone, the model predictions show clearly greater prediction skill than persistence prediction. The low skill, especially in CFSv2, results from contributions for a number of years: 1982, 1996, and 2008 (Fig. 10b). The 2003 monsoon seems to be difficult to predict in both modeling systems (Fig. 10b). The persistence prediction in 1996 and 2003 shows negative correlations, indicating that the zonal wind pattern in the initial month (May) changed during the following summer. One interesting fact is that the CFSv2 prediction skill has a strong correlation with the skill in persistence prediction skill (correlations above 0.5).
Notable failures in monsoon prediction in CFSv2, compared to SYS4, occur in the summers of 1996 and 2008. In May 1996 (Fig. 13b), observations show cooling over the central-east Pacific and western Indian Ocean. The circulation pattern over the Asian monsoon region shows similar features in SYS4 to the observed fields, while both the SST anomaly and the circulation pattern is different from the observations in CFSv2. The observations for June show a broad area of easterly wind anomalies over the Asian monsoon region. The one-month lead prediction in SYS4 represents the June SST anomaly and the wind pattern similar to observations. However, the CFSv2 shows a warm SST anomaly in the central Pacific associated with the different circulation pattern over the western Pacific from the observations. In 2008, the CFSv2 also has an opposite sign for the SST anomaly at June with positive anomaly over the central Pacific, while SYS4 represents the observed strong negative anomaly very well. The skill in 1982 is also different in two modeling systems, even though it is a strong ENSO year (Fig. 13). The SST pattern in CFSv2 for the one-month lead (June) shows a different pattern from the observed SST, thus resulting in a poor prediction of the circulation pattern (Fig. 13).
Here we compared 4 years that show distinct failure in monsoon prediction. However, it is clear that it is not easy to generalize the underlying processes that determine monsoon prediction failure. More analysis is required to understand this problem.
This study has examined the seasonal predictive skill of the NH summer using retrospective predictions by the ECMWF System 4 and NCEP CFS version 2. Seasonal predictions of one to three-month lead (initialized in May) for the boreal summer (JJA) have been investigated with 15–16 ensembles for the period 1982–2009. The ensemble means for both reforecasts reproduce realistically the gross pattern of the observed climatology for SST, precipitation and low-level wind, although systematic biases are found. In both SYS4 and CFSv2, a cold bias of SST is found over the equatorial Pacific, North Atlantic, Indian Ocean and a broad region over the Southern Hemisphere and a warm bias is found over the northern part of North Pacific and North Atlantic. Both systems show a dry bias over the East Asia monsoon region and northern part of South America while excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and maritime continent.
The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to reanalysis products. Both modeling systems simulate the solstice global monsoon mode well, with good representation of the Asian-Australian and Africa monsoons. In both systems, the SST prediction has its greatest skill in the tropical belt, especially over the central and eastern Pacific and equatorial Atlantic where the influence of ENSO is dominant. In the Asian monsoon region, especially over the Indian Ocean, the prediction of precipitation has low skill, where the correlation coefficients do not exceed the significant confidence level. The SYS4 shows greater skill for the low-level wind prediction over the tropical Pacific and Indian Ocean compared to CFSv2 translating into higher predictive skill for the monsoon indices in SYS4 compared to CFSv2. Both reforecasts capture the large-scale South Asian summer monsoon variability quite well, although both models perform poorly in simulating the Indian monsoon circulation.
The two leading principal EOF components in precipitation that are closely related to the ENSO variability are captured well by both models, although the model’s eigenvectors show biases in the spatial pattern. Both models capture the main ENSO teleconnection pattern of shifting of the Walker circulation, which induces ascending motion with a wet anomaly across the equatorial central to eastern Pacific and sinking motion with a dry anomaly over the maritime continent and equatorial eastern Indian Ocean in an El Nino summer. The Asian monsoon prediction skill increases with the ENSO amplitude, implying that a significant portion of the Asian monsoon prediction skill comes from the ENSO forcing. However, although both systems perform very well in simulating ENSO and its associated teleconnection patterns, the relationship between ENSO and the monsoon is much stronger than observed, resulting in an overly strong impact of ENSO on the Asian monsoon. The inability of the two models to capture the observed recent weakening of the ENSO-monsoon relationship could be the cause for poor skill in monsoon prediction. Although the two modeling systems have less skill in monsoon prediction than for ENSO prediction, both models clearly show greater skill than persistence prediction.
We have assessed Asian monsoon predictive skill in two reforecast systems. The seasonal monsoon rainfall not only depends on the magnitudes of the slowly varying boundary conditions but also on the seasonal average of the intraseasonal component (Charney and Shukla 1981; Webster et al. 1998). Even though ENSO provides a linkage to the strength of the South Asian monsoon, the correlations are still rather weak. This may be due to the role of intraseasonal variability. Hoyos and Webster (2007) found that the greatest difference between years is found in the intraseasonal (30–60 day) band. That is, the degree of variance of a monsoon year comes from the number of intraseasonal events. Earlier, Lawrence and Webster (2002) found that the correlation of the phase of ENSO and the degree of intraseasonal variability was very weak. Thus, intraseasonal variability, possibly a phenomenon independent of ENSO, may dilute the overall impact of ENSO on the predictability of the monsoon. Intraseasonal variability strongly influences regional rainfall and is a source of extended range predictability of monsoon weather (Lawrence and Webster 2001; Webster and Hoyos 2004; Wang et al. 2005a, Hoyos and Webster 2007). Therefore, it is important to assess the prediction skill of the monsoon intraseasonal variability in state-of-the-art ocean–atmosphere coupled forecasting system. Comparison in prediction skill of intraseasonal variability between ECMWF SYS4 and NCEP CFSv2 prediction will be explored in a following study.
We would like to thank the reviewers for thoughtful and helpful comments. The ECMWF System 4 reforecasts were obtained by the authors through a commercial agreement with ECMWF. The Climate Dynamics Division of the National Science Foundation under grant NSF-AGS 0965610 provided funding support for this research.
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