This study, based on an analysis with observational and reanalysis data, highlights seasonal tropical-extratropical atmospheric teleconnections originating from tropical rainfall modes unrelated to the Niño 3.4 index for northern winters.... more
This study, based on an analysis with observational and reanalysis data, highlights seasonal tropical-extratropical atmospheric teleconnections originating from tropical rainfall modes unrelated to the Niño 3.4 index for northern winters. The mode decomposition for tropical rainfall is done by first removing the Niño 3.4 index related variability from the tropical rainfall and then applying rotated empirical orthogonal function (REOF) analysis to the residual. The corresponding teleconnection patterns are obtained by regressing global atmospheric fields against the time series of the rainfall modes. Analyses of the tropical heating-atmospheric circulation relationship indicate that the circulation anomalies corresponding to the rainfall modes are forced response to the corresponding rainfall mode. The teleconnection patterns reveal some new features and show that some intrinsic mid-latitude patterns can be triggered by tropical forcing with different rainfall patterns. Results from ...
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Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr... more
Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr common period of 1995–2009. The analyses are applied to predictions of seasonal mean surface temperature and total precipitation over the conterminous United States for the shortest and most commonly used lead time of 0.5 months. The assessments include both categorical and probabilistic verification diagnostics—their seasonalities, spatial distributions, and probabilistic reliability. Attribution of skill to specific physical sources is attempted when possible. Motivations for the analyses are to document improvements in skill between two generations of NOAA’s dynamical seasonal prediction system and to inform the forecast producers, but more importantly the user community, of the skill of the CFS model now in use (CFSv2) to help guide the users’ decisi...
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Based on hindcasts of seasonal forecast systems participating in the North American Multi-Model Ensemble, the seasonal dependence of predictability of the El Niño–Southern Oscillation (ENSO) was estimated. The results were consistent with... more
Based on hindcasts of seasonal forecast systems participating in the North American Multi-Model Ensemble, the seasonal dependence of predictability of the El Niño–Southern Oscillation (ENSO) was estimated. The results were consistent with earlier analyses in that the predictability of ENSO was highest in winter and lowest in spring and summer. Further, predictability as measured by the relative amplitude of predictable and unpredictable components was dominated by the ensemble mean instead of the spread (or dispersion) among ensemble members. This result was consistent with previous analysis that most of ENSO predictability resides in the shift of the probability density function (PDF) of ENSO sea surface temperature (SST) anomalies (i.e., changes in the first moment of the PDF that is associated with the ensemble mean of ENSO SST anomalies) rather than due to changes in the spread of the PDF. The analysis establishes our current best estimate of ENSO predictability that can serve as a benchmark for quantifying further improvements resulting from advances in observing, assimilation, and seasonal prediction systems.
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From October 2014 to March 2015, the Niño3.4 index, referred to as sea surface temperature (SST) anomaly averaged over 170W-120W, 5S-5N, was in a 0.5C to 0.9C range. At the same time, except for the February 2015, the southern oscillation... more
From October 2014 to March 2015, the Niño3.4 index, referred to as sea surface temperature (SST) anomaly averaged over 170W-120W, 5S-5N, was in a 0.5C to 0.9C range. At the same time, except for the February 2015, the southern oscillation index (SOI), defined as the standardized surface pressure difference between Tahiti and Darwin (former minus later), was in a range of -0.6 to -0.9. The values of the both indices exceeded the thresholds for a weak El Niño conditions (Trenberth 1998). However, the atmospheric anomalies over the same time did not reflect typical ENSO like conditions, leading to the question why atmospheric circulation did not show a response typical to what is generally observed during El Niño conditions?
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In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup... more
In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can ...
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This work demonstrates the influence of the initial amplitude of the sea surface temperature anomaly (SSTA) associated with El Niño–Southern Oscillation (ENSO) following its evolutionary phase on the forecast skill of ENSO in... more
This work demonstrates the influence of the initial amplitude of the sea surface temperature anomaly (SSTA) associated with El Niño–Southern Oscillation (ENSO) following its evolutionary phase on the forecast skill of ENSO in retrospective predictions of the Climate Forecast System, version 2. It is noted that the prediction skill varies with the phase of the ENSO cycle. The averaged skill (linear correlation) of Niño-3.4 index is in a range of 0.15–0.55 for the amplitude of Niño-3.4 index smaller than 0.5°C (e.g., initial phase or neutral condition of ENSO), and 0.74–0.93 for the amplitude larger than 0.5°C (e.g., mature condition of ENSO) for 0–6-month lead predictions. The dependence of the prediction skills of ENSO on its phase is linked to the variation of signal-to-noise ratio (SNR). This variation is found to be mainly due to the changes in the amplitude of the signal (prediction of the ensemble mean) during different phases of the ENSO cycle, as the noise (forecast spread am...
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An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible... more
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP T...
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ABSTRACT In this paper, possible connections between actual and potential skill are discussed. Actual skill refers to when the prediction time series is validated against the observations as the verification while perfect skill refers to... more
ABSTRACT In this paper, possible connections between actual and potential skill are discussed. Actual skill refers to when the prediction time series is validated against the observations as the verification while perfect skill refers to when the observed verification time series is replaced by one of the members from the ensemble of predictions. It is argued that (i) there need not be a relationship between potential and actual skill; (ii) potential skill is not constrained to be always greater than actual skill, and examples to the contrary can be found; and (iii) there are methods whereby statistical characteristics of predicted anomalies can be compared with the corresponding in the observations, and inferences about the validity of the (positive) gap between potential and actual skill as "room for improvement" can be better substantiated.
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... J. Climate,9, 14031420. Trenberth, KE, GW Branstrator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface... more
... J. Climate,9, 14031420. Trenberth, KE, GW Branstrator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. ...
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... Arun Kumar, Wanqiu Wang, Lindsey Long, Muthuvel Chelliah, Gerald D. Bell, Peitao Peng, 2009: A Statistical Forecast Model for Atlantic Seasonal Hurricane Activity Based on the NCEP Dynamical Seasonal Forecast. ... Muthuvel Chelliah,... more
... Arun Kumar, Wanqiu Wang, Lindsey Long, Muthuvel Chelliah, Gerald D. Bell, Peitao Peng, 2009: A Statistical Forecast Model for Atlantic Seasonal Hurricane Activity Based on the NCEP Dynamical Seasonal Forecast. ... Muthuvel Chelliah, Gerald D. Bell, and Peitao Peng ...
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The global responses of two atmospheric general circulation models (AGCM), the National Centers for Environmental PredictionMedium Range Forecast (NCEPMRF9) and the University of Hamburg climate model3 (ECHAM), to simultaneous global... more
The global responses of two atmospheric general circulation models (AGCM), the National Centers for Environmental PredictionMedium Range Forecast (NCEPMRF9) and the University of Hamburg climate model3 (ECHAM), to simultaneous global SST forcing are examined on a ...
Research Interests: Oceanography, Climate variability, Principal Component Analysis, Climate, North Atlantic Oscillation, and 11 moreSeasonality, Atmospheric sciences, First-Order Logic, Prediction Model, Climate Model, Model Generation, Statistical Model, Dynamic Model of WSN, Boundary Condition, Southern Oscillation, and Signal to Noise Ratio
Abstract Based on a 40-member ensemble for the January-March (JFM) seasonal mean for the 1980-2000 period using an atmospheric general circulation model (AGCM), interannual variability in the first and second moments of probability... more
Abstract Based on a 40-member ensemble for the January-March (JFM) seasonal mean for the 1980-2000 period using an atmospheric general circulation model (AGCM), interannual variability in the first and second moments of probability density function (PDF) of ...
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... Fig.1 CFS climate drift in SST (upper), precipitation rate (middle) and 200hPa height (lower). ... It is obvious that the high ratio occurs in low latitude and the PNA region, suggesting that ENSO related variability dominates the... more
... Fig.1 CFS climate drift in SST (upper), precipitation rate (middle) and 200hPa height (lower). ... It is obvious that the high ratio occurs in low latitude and the PNA region, suggesting that ENSO related variability dominates the predictability of the atmosphere. ...