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  • Author or Editor: Michael K. Tippett x
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Timothy Hall
and
Michael K. Tippett

Abstract

A statistical model of northeastern Pacific Ocean tropical cyclones (TCs) is developed and used to estimate hurricane landfall rates along the coast of Mexico. Mean annual landfall rates for 1971–2014 are compared with mean rates for the extremely high northeastern Pacific sea surface temperature (SST) of 2015. Over the full coast, the mean rate and 5%–95% uncertainty range (in parentheses) for TCs that are category 1 and higher on the Saffir–Simpson scale (C1+ TCs) are 1.24 (1.05, 1.33) yr−1 for 1971–2014 and 1.69 (0.89, 2.08) yr−1 for 2015—a difference that is not significant. The increase for the most intense landfalls (category-5 TCs) is significant: 0.009 (0.006, 0.011) yr−1 for 1971–2014 and 0.031 (0.016, 0.036) yr−1 for 2015. The SST impact on the rate of category-5 TC landfalls is largest on the northern Mexican coast. The increased landfall rates for category-5 TCs are consistent with independent analysis showing that SST has its greatest impact on the formation rates of the most intense northeastern Pacific TCs. Landfall rates on Hawaii [0.033 (0.019, 0.045) yr−1 for C1+ TCs and 0.010 (0.005, 0.016) yr−1 for C3+ TCs for 1971–2014] show increases in the best estimates for 2015 conditions, but the changes are statistically insignificant.

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Michael K. Tippett
,
Anthony G. Barnston
, and
Shuhua Li

Abstract

The performance of the International Research Institute for Climate and Society “ENSO forecast plume” during the 2002–11 period is evaluated using deterministic and probabilistic verification measures. The plume includes multiple model forecasts of the Niño-3.4 index for nine overlapping 3-month periods beginning the month following the latest observations. Skills decrease with increasing lead time and are highest for forecasts made after the northern spring predictability barrier for target seasons occurring prior to the forthcoming such barrier. Forecasts are found to verify systematically better against observations occurring earlier than the intended forecast targets, an effect that becomes greater with increasing lead time. During the study period, the mean forecasts of dynamical models appear to slightly (and statistically insignificantly) outperform those of statistical models, representing a subtle shift from earlier studies. The mean forecasts of dynamical models have overall larger anomalies but similar errors to those of statistical models. Intermodel spread is related to forecast error in an average sense with changes in forecast error due to changes in lead and verification season being properly reflected in changes in spread. The intermodel spread underestimates the forecast error variance, to a greater extent for statistical forecasts than for dynamical ones. Year-to-year changes in plume spread provide little additional information relative to climatological ones.

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Anthony G. Barnston
,
Michael K. Tippett
,
Huug M. van den Dool
, and
David A. Unger

Abstract

Since 2002, the International Research Institute for Climate and Society, later in partnership with the Climate Prediction Center, has issued an ENSO prediction product informally called the ENSO prediction plume. Here, measures to improve the reliability and usability of this product are investigated, including bias and amplitude corrections, the multimodel ensembling method, formulation of a probability distribution, and the format of the issued product. Analyses using a subset of the current set of plume models demonstrate the necessity to correct individual models for mean bias and, less urgent, also for amplitude bias, before combining their predictions. The individual ensemble members of all models are weighted equally in combining them to form a multimodel ensemble mean forecast, because apparent model skill differences, when not extreme, are indistinguishable from sampling error when based on a sample of 30 cases or less. This option results in models with larger ensemble numbers being weighted relatively more heavily. Last, a decision is made to use the historical hindcast skill to determine the forecast uncertainty distribution rather than the models’ ensemble spreads, as the spreads may not always reproduce the skill-based uncertainty closely enough to create a probabilistically reliable uncertainty distribution. Thus, the individual model ensemble members are used only for forming the models’ ensemble means and the multimodel forecast mean. In other situations, the multimodel member spread may be used directly. The study also leads to some new formats in which to more effectively show both the mean ENSO prediction and its probability distribution.

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Michael K. Tippett
,
Laurie Trenary
,
Timothy DelSole
,
Kathleen Pegion
, and
Michelle L. L’Heureux

Abstract

Forecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

Open access
Chia-Ying Lee
,
Adam H. Sobel
,
Suzana J. Camargo
,
Michael K. Tippett
, and
Qidong Yang

Abstract

This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

Open access
Xiao-Wei Quan
,
Martin P. Hoerling
,
Bradfield Lyon
,
Arun Kumar
,
Michael A. Bell
,
Michael K. Tippett
, and
Hui Wang

Abstract

The prospects for U.S. seasonal drought prediction are assessed by diagnosing simulation and hindcast skill of drought indicators during 1982–2008. The 6-month standardized precipitation index is used as the primary drought indicator. The skill of unconditioned, persistence forecasts serves as the baseline against which the performance of dynamical methods is evaluated. Predictions conditioned on the state of global sea surface temperatures (SST) are assessed using atmospheric climate simulations conducted in which observed SSTs are specified. Predictions conditioned on the initial states of atmosphere, land surfaces, and oceans are next analyzed using coupled climate-model experiments. The persistence of the drought indicator yields considerable seasonal skill, with a region’s annual cycle of precipitation driving a strong seasonality in baseline skill. The unconditioned forecast skill for drought is greatest during a region’s climatological dry season and is least during a wet season. Dynamical models forced by observed global SSTs yield increased skill relative to this baseline, with improvements realized during the cold season over regions where precipitation is sensitive to El Niño–Southern Oscillation. Fully coupled initialized model hindcasts yield little additional skill relative to the uninitialized SST-forced simulations. In particular, neither of these dynamical seasonal forecasts materially increases summer skill for the drought indicator over the Great Plains, a consequence of small SST sensitivity of that region’s summer rainfall and the small impact of antecedent soil moisture conditions, on average, upon the summer rainfall. The fully initialized predictions for monthly forecasts appreciably improve on the seasonal skill, however, especially during winter and spring over the northern Great Plains.

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Bradfield Lyon
,
Michael A. Bell
,
Michael K. Tippett
,
Arun Kumar
,
Martin P. Hoerling
,
Xiao-Wei Quan
, and
Hui Wang

Abstract

The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

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