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Michael K. Tippett
,
Anthony G. Barnston
,
David G. DeWitt
, and
Rong-Hua Zhang

Abstract

This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction.

Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.

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Erwan Koch
,
Jonathan Koh
,
Anthony C. Davison
,
Chiara Lepore
, and
Michael K. Tippett

Abstract

Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD = (CAPE)1/2 × SRH have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States over the period 1979–2015 and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May, and August, for CAPE maxima in April, May, and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate but have not previously been reported. Moreover, we show that El Niño–Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the United States where it was already high and that the risk from storms in February is increased over the main part of the region during La Niña years.

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

Abstract

An autoregressive model is developed to simulate the climatological distribution of global tropical cyclone (TC) intensity. The model consists of two components: a regression-based deterministic component that advances the TC intensity in time and depends on the storm state and surrounding large-scale environment and a stochastic forcing. Potential intensity, deep-layer mean vertical shear, and midlevel relative humidity are the environmental variables included in the deterministic component. Given a storm track and its environment, the model is initialized and then iterated along the track. Model performance is evaluated by its ability to represent the observed global and basin distributions of TC intensity as well as lifetime maximum intensity (LMI). The deterministic model alone captures the spatial features of the climatological TC intensity distribution but with intensities that remain below 100 kt (1 kt ≈ 0.51 m s−1). Addition of white (uncorrelated in time) stochastic forcing reduces this bias by improving the simulated intensification rates and the frequency of major storms. The model simulates a realistic range of intensities, but the frequency of major storms remains too low in some basins.

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Suzana J. Camargo
,
Michael K. Tippett
,
Adam H. Sobel
,
Gabriel A. Vecchi
, and
Ming Zhao

Abstract

Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor overpredicts the TC frequency decrease, while using potential intensity in place of relative SST as the other thermodynamic predictor gives a good prediction of the decrease’s magnitude. These positive results appear to depend on the spatial and seasonal patterns in the imposed SST changes; none of the indices capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2 K, or a doubling of CO2 with no change in SST.

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Michael K. Tippett
,
Adam H. Sobel
,
Suzana J. Camargo
, and
John T. Allen

Abstract

In previous work the authors demonstrated an empirical relation, in the form of an index, between U.S. monthly tornado activity and monthly averaged environmental parameters. Here a detailed comparison is made between the index and reported tornado activity. The index is a function of two environmental parameters taken from the North American Regional Reanalysis: convective precipitation (cPrcp) and storm relative helicity (SRH). Additional environmental parameters are considered for inclusion in the index, among them convective available potential energy, but their inclusion does not significantly improve the overall climatological performance of the index. The aggregate climatological dependence of reported monthly U.S. tornado numbers on cPrcp and SRH is well described by the index, although it fails to capture nonsupercell and cool season tornadoes. The contributions of the two environmental parameters to the index annual cycle and spatial distribution are examined with the seasonality of cPrcp (maximum during summer) relative to SRH (maximum in winter) accounting for the index peak value in May. The spatial distribution of SRH establishes the central U.S. “tornado alley” of the index, while the spatial distribution of cPrcp enhances index values in the South and Southeast and suppresses them west of the Rockies and over elevation. At the scale of the NOAA climate regions, the largest deficiency of the index climatology occurs over the central region where the index peak in spring is too low and where the late summer drop-off in the reported number of tornadoes is poorly captured. This index deficiency is related to its sensitivity to SRH, and increasing the index sensitivity to SRH improves the representation of the annual cycle in this region. The ability of the index to represent the interannual variability of the monthly number of U.S. tornadoes can be ascribed during most times of the year to interannual variations of cPrcp rather than of SRH. However, both factors are important during the peak spring period. The index shows some skill in representing the interannual variability of monthly tornado numbers at the scale of NOAA climate regions.

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Sarah D. Ditchek
,
William R. Boos
,
Suzana J. Camargo
, and
Michael K. Tippett

Abstract

Synoptic-scale monsoon disturbances produce the majority of continental rainfall in the monsoon regions of South Asia and Australia, yet there is little understanding of the conditions that foster development of these low pressure systems. Here a genesis index is used to associate monsoon disturbance genesis in a global domain with monthly mean, climatological environmental variables. This monsoon disturbance genesis index (MDGI) is based on four objectively selected variables: total column water vapor, low-level absolute vorticity, an approximate measure of convective available potential energy, and midtropospheric relative humidity. A Poisson regression is used to estimate the index coefficients. Unlike existing tropical cyclone genesis indices, the MDGI is defined over both land and ocean, consistent with the fact that monsoon disturbance genesis can occur over land. The index coefficients change little from their global values when estimated separately for the Asian–Australian monsoon region or the Indian monsoon region, suggesting that the conditions favorable for monsoon disturbance genesis, and perhaps the dynamics of genesis itself, are common across multiple monsoon regions. Vertical wind shear is found to be a useful predictor in some regional subdomains; although previous studies suggested that baroclinicity may foster monsoon disturbance genesis, here genesis frequency is shown to be reduced in regions of strong climatological vertical shear. The coefficients of the MDGI suggest that monsoon disturbance genesis is fostered by humid, convectively unstable environments that are rich in vorticity. Similarities with indices used to describe the distribution of tropical cyclone genesis are discussed.

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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
Anthony G. Barnston
,
Michael K. Tippett
,
Michelle L. L'Heureux
,
Shuhua Li
, and
David G. DeWitt

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.

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