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Arun Kumar

Abstract

For the dynamic normal-mode initialization (DNMI) procedure used in the initialization of the limited-area models, it is shown that it does not succeed in initializing higher vertical modes. An improved initialization procedure where the DNMI is performed in the vertical mode space is next formulated. A comparison of the two initialization schemes demonstrates that with the new procedure the initialization of the higher vertical modes can be better accomplished. The need for a better initialization method for limited-area models is prompted by the fact that if the assimilation of the observed estimates of the convective heating toward enhancing the analysis of the divergence is to be achieved, higher vertical modes have to be properly initialized. It is shown that the improved DNMI procedure does succeed in the assimilation of the convective heating information.

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Arun Kumar

Abstract

The expected value for various measures of skill for seasonal climate predictions is determined by the signal-to-noise ratio. The expected value, however, is only realized for long verification time series. In practice, the verifications for specific seasons—for example, forecasts for the December–February seasonal mean—seldom exceed a sample size of 30. The estimates of skill measure based on small verification time series, because of sampling errors, can have large departures from their expected value. An analysis of spread in the estimates of skill measures with the length of verification time series and for different signal-to-noise ratios is made. The analysis is based on the Monte Carlo approach and skill measures for deterministic, categorical, and probabilistic forecasts are considered. It is shown that the behavior of spread for various skill measures can be very different and it is not always the largest for the small values of signal-to-noise ratios.

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Arun Kumar

Abstract

In recent years, there has been a steady increase in the emphasis on routine seasonal climate predictions and their potential for enhancing societal benefits and mitigating losses related to climate extremes. It is also suggested by the users, as well as by the producers of climate predictions, that for informed decision making, real-time seasonal climate predictions should be accompanied by a corresponding level of skill estimated from a sequence of the past history of forecasts. In this paper it is discussed whether conveying skill information to the user community can indeed deliver the promised benefits or whether issues inherent in the estimates of seasonal prediction skill may still lead to potential misinterpretation of the information content associated with seasonal predictions. Based on the analysis of atmospheric general circulation model simulations, certain well-known, but often underappreciated, issues inherent in the estimates of seasonal prediction skill from the past performance of seasonal forecasts are highlighted. These include the following: 1) the stability of estimated skill depends on the length of the time series over which seasonal forecasts are verified, leading to scenarios where error bars on the estimated skill could be of the same magnitude as the skill itself; 2) a single estimate of skill obtained from the verification over a given forecast time series, because of variation in the signal-to-noise ratio from one year to another, is generally not representative of seasonal prediction skill conditional to sea surface temperature anomalies on a case-by-case basis. These issues raise questions on the interpretation, presentation, and utilization of skill information for seasonal prediction efforts and present opportunities for increased dialogue and the exploration of ways for their optimal utilization by decision makers.

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Arun Kumar
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Bhaskar Jha
and
Arun Kumar

Abstract

In the Atmospheric Model Intercomparison Project (AMIP) simulations the sea surface temperatures (SSTs) are specified and the oceanic evolution consistent with air–sea interaction is not included. This omission could lead to errors in the atmospheric response to SSTs. At the same time, the AMIP experimental setup is well suited for investigating many aspects of climate variability (e.g., the attribution of the interannual atmospheric variability) and continues to be extensively used. As coupled El Niño–Southern Oscillation (ENSO) SST variability is a dominant factor in determining the predictable component of the observed interannual atmospheric variability, the difference in the atmospheric response to ENSO SSTs between AMIP and coupled simulations is investigated. The results indicate that the seasonal atmospheric response to ENSO between coupled and uncoupled integrations is similar, and the inclusion of oceanic evolution consistent with air–sea interaction does not play a dominant role. The analysis presented in this paper is one step toward assessing differences in atmospheric response to SSTs in coupled and uncoupled simulations, and is required to correctly interpret the results of AMIP simulations.

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Peitao Peng
and
Arun Kumar

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 atmospheric seasonal means with sea surface temperatures (SSTs) is analyzed. Based on the strength of the SST anomaly in the Niño-3.4 index region, the years between 1980 and 2000 were additionally categorized into five separate bins extending from strong cold to strong warm El Niño events. This procedure further enhances the size of the ensemble for each SST category. All the AGCM simulations were forced with the observed SSTs, and different ensemble members for specified SST boundary forcing were initiated from different atmospheric initial conditions.

The main focus of this analysis is on the changes in the seasonal mean and the internal variability of tropical rainfall and extratropical 200-mb heights with SSTs. For the tropical rainfall, results indicate that in the equatorial tropical Pacific, internal variability of the tropical rainfall anomaly decreases (increases) for the La Niña (El Niño) events. On the other hand, seasonal mean variability of extratropical 200-mb height decreases for the El Niño events. Although there is increase in the seasonal mean variability of 200-mb heights for the La Niña events, results are rather inclusive. Analysis also indicates that for the variables studied, the influence of the interannual variability in SSTs is much stronger on the first moment of seasonal means compared to their influence on the internal variability. As a consequence, seasonal predictability due to changes in SSTs can be attributed primarily to the shift in the PDFs of the seasonal atmospheric means and less to changes in their spread.

Modes of internal variability for 200-mb extratropical seasonal mean heights for different SST categories are also analyzed. The dominant mode of internal variability has little dependence on the tropical SST forcing, while larger influence on the second mode of internal variability is found. For SST forcing changing from a La Niña to El Niño state, the spatial pattern of the second mode shifts eastward. For the cold events, the spatial patterns bear more resemblance to the Pacific–North American (PNA) pattern, while for the warm events, it more resembles the tropical–North Hemispheric (TNH) pattern. Change in the spatial pattern of this mode from strong cold to a strong warm event resembles the change in the spatial pattern of response in the mean state.

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Jieshun Zhu
and
Arun Kumar

Abstract

While previous studies suggested that salinity could feed back onto MJO variability via modulating upper ocean stratification and further on SST, there is no direct evidence yet proving (or disproving) the importance of this feedback in MJO evolution and its predictability. This study is an initial attempt to quantify the role of SSS feedback on MJO predictability, based on a “perfect model” framework with the CFSv2. Specifically, the SSS feedback is isolated by nudging model SSS to climatological states during forecasts. For comparison, two more experiments were done, one as a benchmark experiment by estimating MJO predictability in CFSv2 and another one for estimating the role of SST feedback. Analyses of these experiments indicate that SSS feedback exerts negligible influences on MJO predictability within the constraints of the model, in contrast to significant impacts from SST feedback. Further analysis showed that a lack of SSS influence in MJO predictability can be attributed to marginal changes in SST associated with the SSS nudging. However, there is a caveat to the conclusion about SSS feedback. Because the barrier layer (BL) acts as a “bridge” for possible SSS influences on SST over the tropical Indian and western Pacific oceans, its simulation in CFSv2 is further explored. Analyses indicate that, in spite of realistic simulations of the MJO and intraseasonal SSS variability in CFSv2, significant BL simulation biases are present in the tropical oceans, including too thin a climatological thickness, too small intraseasonal variations, and an unrealistic intraseasonal BL–SST relationship. Thus, our predictability experiments cannot reject the hypothesis that SSS does play a role in MJO predictability; it is possible that biases in CFSv2 influence its ability to capture such signals.

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Arun Kumar
and
Mingyue Chen

Abstract

Using extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

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Arun Kumar
and
Jieshun Zhu

Abstract

Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean–atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air–sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air–sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean–atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean–atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases.

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Bhaskar Jha
and
Arun Kumar

Abstract

Based on simulations from nine different atmospheric general circulation models (AGCMs), a comparative assessment of the influence of ENSO SST variability on the first and second moment of the probability density function (PDF) of 200-mb seasonal mean height is made. This comparison is quantified by regressing the interannual variability in the mean and the spread of the seasonal means against the Niño-3.4 SSTs. Based on the analysis of simulations from multiple AGCMs, it is concluded that the relative impact of interannual variability of SSTs is larger, and more systematic, on the mean of the PDF of 200-mb heights than on its spread. This result implies that seasonal predictability due to SSTs is predominantly a function of its influence on the seasonal mean. Further, for the practice of seasonal predictions, it might be pragmatic to assume that spread of seasonal means stays constant and that the seasonal forecast information resides entirely in the shift of the seasonal mean PDF.

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