Given observed initial conditions, how well do coupled atmosphere-ocean models predict precipitation climatology with 1-month lead forecast? And how do the models' biases in climatology in turn ...affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981-2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models' ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian-Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems ...(7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial-temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80-90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model's slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.
Three-day accumulations of precipitation for 2.5° long × 2.0° lat areas along the west coast of the United States are used to rank precipitation events. Extreme precipitation events (those above the ...90th percentile) occur at all phases of the El Niño–Southern Oscillation (ENSO) cycle, but the largest fraction of these events (for the West Coast as a whole) occur during neutral winters just prior to the onset of El Niño. In the tropical Pacific these winters are characterized by enhanced activity on intraseasonal (roughly 20–60 day) timescales and by relatively small sea surface temperature anomalies compared to ENSO winters. For these winters, lagged composites are used to document a coherent relationship between the location of extreme precipitation events along the West Coast and the location of enhanced tropical convection on intraseasonal timescales. The evolution of the atmospheric circulation patterns associated with the extreme precipitation events is described and a physical mechanism relating tropical intraseasonal oscillations, the “pineapple express,” and the extreme precipitation events is proposed and illustrated.
This study examines the forecast performance of tropical intraseasonal oscillation (ISO) in recent dynamical extended range forecast (DERF) experiments conducted with the National Centers for ...Environmental Prediction (NCEP) Global Forecasting System (GFS) model. The present study extends earlier work by comparing prediction skill of the northern winter ISO (Madden-Julian Oscillation) between the current and earlier experiments. Prediction skill for the northern summer ISO is also investigated. Since the boreal summer ISO exhibits northward propagation as well as eastward propagation along the equator, forecast skill for both components is computed. For the 5-year period from 1 January, 1998 through 31 December, 2002, 30-day forecasts were made once a day. Compared to the previous DERF experiment, the current model has shown some improvements in forecasting the ISO during winter season so that the skillful forecasts (anomaly correlation>0.6) for upper-level zonal wind anomaly extend from the previous shorter-than 5 days out to 7 days lead-time. A similar level of skill is seen for both northward and eastward propagation components during the summer season as in the winter case. Results also show that forecasts from extreme initial states are more skillful than those from null phases for both seasons, extending the skillful range by 3-6 days. For strong ISO convection phases, the GFS model performs better during the summer season than during the winter season. In summer forecasts, large-scale circulation and convection anomalies exhibit northward propagation during the peak phase. In contrast, the GFS model still has difficulties in sustaining ISO variability during the northern winter as in the previous DERF run. That is, the forecast does not maintain the observed eastward propagating signals associated with large-scale circulation; rather the forecast anomalies appear to be stationary at their initial location and decay with time. The NCEP Coupled Forecast System produces daily operational forecasts and its predication skill of the MJO will be reported in the future.
Tropical intraseasonal convective anomalies (TICA) have a central role in subseasonal changes in the coupled ocean–atmosphere system, but the climatology of TICA events has not been properly ...documented. This study exploits 24 years of outgoing longwave radiation (OLR) data and a tracking algorithm to develop a climatology of eastward propagating TICA events. Three distinct types of TICA occurrences are documented according to their propagation characteristics. The first type (IND) is characterized by events that propagate in the Indian Ocean without significant influence in the western Pacific Ocean. The second and third types are associated with occurrences of the Madden–Julian oscillation during boreal winters (MJO) and summers (ISO). The frequency of occurrence of TICA events is highest in April–June and October–December and lowest in July–September. An analysis of the spatial and temporal characteristics reveals that MJO events tend to have the longest life cycle, greatest intensity, and largest variability inside the contiguous region of OLR anomaly. Given the data record of 24 years, the analysis of interannual occurrences of TICA events does not show statistically significant differences among events that occur in different phases of the El Niño–Southern Oscillation (ENSO). A procedure is developed to identify major MJO events and estimate their frequency of occurrence in the data record.
Tropical intraseasonal convective anomalies (TICAs) play a significant role in the coupled ocean–atmosphere system and the Madden–Julian oscillation (MJO) is the primary mode of this variability. ...This study describes statistical forecast models of intraseasonal variations. Twenty-four years of outgoing longwave radiation (OLR) and zonal components of the wind at 200 (U200) and 850 hPa (U850) are used. The models use the principal components (PCs) of combined EOF analysis of 20–90-day anomalies of OLR, U200, and U850 data. Forecast models are developed for each lead time from 1 to 10 pentads and for winter and summer seasons separately. The forecast models use a combination of the five most recent pentad values of the first five PCs of the combined EOF of (OLR, U200, U850) to predict the future values of a given PC
K
(k= 1, 5). The spatial structures are obtained by reconstructing the fields of OLR, U200, and U850 using the forecasts of PC
K
(k= 1, 5) and the associated EOFs. Verification with independent winter and summer data indicates useful forecasts of the first five PCs extending up to five pentads of lead time. The verification against 20–90-day anomalies indicates useful forecasts of the reconstructed fields of OLR, U200, and U850 extending up to four pentads of lead time over most of the Tropics. Furthermore, the statistical models provide useful forecasts of U200 and U850 intraseasonal anomalies up to two to three pentads of lead times in portions of the North Pacific region.
For the uncoupled atmospheric general circulation model (AGCM) simulations, the quantification of errors due to the lack of coupled ocean–atmospheric evolution on the characteristics of the ...atmospheric interannual variability is important for various reasons including the following: 1) AGCM simulations forced with specified SSTs continue to be used for understanding atmospheric interannual variability and 2) there is a vast knowledge base quantifying the global atmospheric influence of tropical SSTs that traditionally has relied on the analysis of AGCM-alone simulations. To put such results and analysis in a proper context, it is essential to document errors that may result from the lack of a coupled ocean–atmosphere evolution in the AGCM-alone integrations.
Analysis is based on comparison of tier-two (or uncoupled) and coupled hindcasts for the 1982–2005 period, and interannual variability for the December–February (DJF) seasonal mean is analyzed. Results indicate that for the seasonal mean variability, and for the DJF seasonal mean, atmospheric interannual variability between coupled and uncoupled simulations is similar. This conclusion is drawn from the analysis of interannual variability of rainfall and 200-mb heights and includes analysis of SST-forced interannual variability, analysis of El Niño and La Niña composites, and a comparison of hindcast skill between tier-two and coupled hindcasts.
The Madden and Julian Oscillation (MJO) is the most prominent mode of intraseasonal variations in the tropical region. It plays an important role in climate variability and has a significant ...influence on medium-to-extended ranges weather forecasting in the tropics. This study examines the forecast skill of the oscillation in a set of recent dynamical extended range forecasts (DERF) experiments performed by the National Centers for Environmental Prediction (NCEP). The present DERF experiments were done with the reanalysis version of the medium range forecast (MRF) model and include 50-day forecasts, initialized once-a-day (0Z) with reanalyses fields, for the period between 1 January, 1985, and 31 December, 1989. The MRF model shows large mean errors in representing intraseasonal variations of the large-scale circulation, especially over the equatorial eastern Pacific Ocean. A diagnostic analysis has considered the different phases of the MJO and the associated forecast skill of the MRF model. Anomaly correlations on the order of 0.3 to 0.4 indicate that skillful forecasts extend out to 5 to 7 days lead-time. Furthermore, the results show a slight increase in the forecast skill for periods when convective anomalies associated with the MJO are intense. By removing the mean errors, the analysis shows systematic errors in the representation of the MJO with weaker than observed upper level zonal circulations. The examination of the climate run of the MRF model shows the existence of an intraseasonal oscillation, although less intense (50-70%) and with faster (nearly twice as fast) eastward propagation than the observed MJO. The results indicate that the MRF model likely has difficulty maintaining the MJO, which impacts its forecast. A discussion of future work to improve the representation of the MJO in dynamical models and assess its prediction is presented. PUBLICATION ABSTRACT
In this study, a statistical model is developed that exploits the slow evolution of the Madden–Julian oscillation (MJO) to predict tropical rainfall variability at long lead times (i.e., 5–20 days). ...The model is based on a field-to-field decomposition that uses previous and present pentads of outgoing longwave radiation (OLR; predictors) to predict future pentads of OLR (predictands). The model was developed using 30–70-day bandpassed OLR data from 1979 to 1989 and validated on data from 1990 to 1996. For the validation period, the model exhibits temporal correlations to observed bandpassed data of about 0.5–0.9 over a significant region of the Eastern Hemisphere at lead times from 5 to 20 days, after which the correlation drops rapidly with increasing lead time. Correlations against observed total anomalies are on the order of 0.3–0.5 over a smaller region of the Eastern Hemisphere.
Comparing the skill values from the above OLR-based model, along with those from an identical statistical model using reanalysis-derived 200-mb zonal wind anomalies, to the skill values of 200-mb zonal wind predictions from the National Centers for Environmental Prediction’s Dynamic Extended Range Forecasts shows that the statistical models appear to perform considerably better. These results indicate that considerable advantage might be afforded from the further exploration and eventual implementation of MJO-based statistical models to augment current operational long-range forecasts in the Tropics. The comparisons also indicate that there is considerably more work to be done in achieving the likely forecast potential that dynamic models might offer if they could suitably simulate MJO variability.
The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are ...28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies showa strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.
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