Michael K. Tippett

Ensemble Square Root Filters

Abstract Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing?

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 Nino- 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 sta...

Pacific meridional mode and El Niño—Southern Oscillation

(1) We present intriguing evidence that the majority of El Nino events over the past four decades are preceded by a distinctive sea-surface warming and southwesterly wind anomaly in the vicinity of the Intertropical Convergence Zone (ITCZ) during the boreal spring. This phenomenon, known as the Meridional Mode (MM), is shown to be intrinsic to the thermodynamic coupling between the atmosphere and ocean. The MM effectively acts as a conduit through which the extratropical atmosphere influences ENSO. Modeling results further suggest that the MM plays a vital role in the seasonal phase-locking behavior of ENSO. The findings provide a new perspective for understanding the important role of thermodynamic ocean-atmosphere feedback in ENSO and may have profound implications for ENSO prediction, particularly the unresolved issue of the spring predictability barrier. Citation: Chang, P., L. Zhang, R. Saravanan, D. J. Vimont, J. C. H. Chiang, L. Ji, H. Seidel, and M. K. Tippett (2007), Pacific meridional mode and El Nino—Southern Oscillation, Geophys. Res. Lett., 34, L16608, doi:10.1029/2007GL030302.

A Significant Component of Unforced Multidecadal Variability in the Recent Acceleration of Global Warming

Abstract The problem of separating variations due to natural and anthropogenic forcing from those due to unforced internal dynamics during the twentieth century is addressed using state-of-the-art climate simulations and observations. An unforced internal component that varies on multidecadal time scales is identified by a new statistical method that maximizes integral time scale. This component, called the internal multidecadal pattern (IMP), is stochastic and hence does not contribute to trends on long time scales; however, it can contribute significantly to short-term trends. Observational estimates indicate that the trend in the spatially averaged “well observed” sea surface temperature (SST) due to the forced component has an approximately constant value of 0.1 K decade−1, while the IMP can contribute about ±0.08 K decade−1 for a 30-yr trend. The warming and cooling of the IMP matches that of the Atlantic multidecadal oscillation and is of sufficient amplitude to explain the acceleration in warming d...

A Poisson Regression Index for Tropical Cyclone Genesis and the Role of Large-Scale Vorticity in Genesis

A Poisson regression between the observed climatology of tropical cyclogenesis (TCG) and large-scale climate variables is used to construct a TCG index. The regression methodology is objective and provides a framework for the selection of the climate variables in the index. Broadly following earlier work, four climate variables appear in the index: low-level absolute vorticity, relative humidity, relative sea surface temperature (SST), and vertical shear. Several variants in the choice of predictors are explored, including relative SST versus potential intensity and satellite-based column-integrated relative humidity versus reanalysis relative humidity at a single level; these choices lead to modest differences in the performance of the index.The featureof the newindexthat leadsto thegreatest improvement is afunctionaldependenceon lowlevel absolute vorticity that causes the index response to absolute vorticity to saturate when absolute vorticity exceeds a threshold. This feature reduces some biases of the index and improves the fidelity of its spatial distribution. Physically, this result suggests that once low-level environmental vorticity reaches a sufficiently largevalue,otherfactorsbecomeratelimitingsothatfurtherincreasesinvorticity(atleastonamonthlymean basis) do not increase the probability of genesis. Although the index is fit to climatological data, it reproduces some aspects of interannual variability when applied to interannually varying data. Overall, the new index compares positively to the genesis potential index (GPI), whose derivation, computation, and analysis is more complex in part because of its dependence on potential intensity.

Testing the Performance of Tropical Cyclone Genesis Indices in Future Climates Using the HiRAM Model

AbstractTropical 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 simulate...

Climate and Hazardous Convective Weather

Substantial progress has been made recently relating the large-scale climate system and hazardous convective weather (HCW; tornadoes, hail, and damaging wind), particularly over the USA where there are large societal impacts and a long observational record. Despite observational data limitations, HCW has shown to be influenced by the climate system and the tropical atmosphere via the Madden-Julian Oscillation and El Niño-Southern Oscillation. Analysis of the atmospheric environments favorable to HCW (e.g., convective available potential energy and vertical wind shear) avoids observational and model limitations. While few robust trends are seen over recent decades, future climate projections indicate increased frequency of such environments over the USA, Europe, and Australia, suggesting increased future HCW activity. A recent increase in the year-to-year variability of US tornado occurrence is striking, but not yet understood. Dynamical downscaling to convection-permitting resolutions promises improved understanding of the relationships between large-scale climate and HCW occurrence.

Prospects for Dynamical Prediction of Meteorological Drought

AbstractThe 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 ...

Skill of Multimodel ENSO Probability Forecasts

Abstract The cross-validated hindcast skills of various multimodel ensemble combination strategies are compared for probabilistic predictions of monthly SST anomalies in the ENSO-related Nino-3.4 region of the tropical Pacific Ocean. Forecast data from seven individual models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used, spanning the 22-yr period of 1980–2001. Skill of the probabilistic forecasts is measured using the ranked probability skill score and rate of return, the latter being an information theory–based measure. Although skill is generally low during boreal summer relative to other times of the year, the advantage of the model forecasts over simple historical frequencies is greatest at this time. Multimodel ensemble predictions, even those using simple combination methods, generally have higher skill than single model predictions, and this advantage is greater than that expected as a result of an increase in ensemble...