Laurie Trenary

Does the Atlantic Multidecadal Oscillation Get Its Predictability from the Atlantic Meridional Overturning Circulation

AbstractThis paper investigates the predictive relation between the Atlantic multidecadal oscillation (AMO) and Atlantic meridional overturning circulation across different climate models. Three overturning patterns that are significantly coupled to the AMO on interannual time scales across all climate models are identified using a statistical optimization technique. Including these structures in an autoregressive model extends AMO predictability by 2–9 years, relative to an autoregressive model without these structures.

Predictability of Week-3–4 Average Temperature and Precipitation over the Contiguous United States

AbstractThis paper demonstrates that an operational forecast model can skillfully predict week-3–4 averages of temperature and precipitation over the contiguous United States. This skill is demonstrated at the gridpoint level (about 1° × 1°) by decomposing temperature and precipitation anomalies in terms of an orthogonal set of patterns that can be ordered by a measure of length scale and then showing that many of the resulting components are predictable and can be predicted in observations with statistically significant skill. The statistical significance of predictability and skill are assessed using a permutation test that accounts for serial correlation. Skill is detected based on correlation measures but not based on mean square error measures, indicating that an amplitude correction is necessary for skill. The statistical characteristics of predictability are further clarified by finding linear combinations of components that maximize predictability. The forecast model analyzed here is version 2 of ...

A new method for determining the optimal lagged ensemble

Abstract We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross‐lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of forecasts to be evaluated for an arbitrary ensemble size and initialization frequency. Remarkably, the parameterization also can estimate the MSE of a burst ensemble simply by taking the limit of an infinitely small interval between initialization times. This methodology is applied to forecasts of the Madden Julian Oscillation (MJO) from version 2 of the Climate Forecast System version 2 (CFSv2). For leads greater than a week, little improvement is found in the MJO forecast skill when ensembles larger than 5 days are used or initializations greater than 4 times per day. We find that if the initialization frequency is too infrequent, important structures of the lagged error covariance matrix are lost. Lastly, we demonstrate that the forecast error at leads ≥10 days can be reduced by optimally weighting the lagged ensemble members. The weights are shown to depend only on the cross‐lead error covariance matrix. While the methodology developed here is applied to CFSv2, the technique can be easily adapted to other forecast systems.

The Weighted‐Average Lagged Ensemble

Abstract A lagged ensemble is an ensemble of forecasts from the same model initialized at different times but verifying at the same time. The skill of a lagged ensemble mean can be improved by assigning weights to different forecasts in such a way as to maximize skill. If the forecasts are bias corrected, then an unbiased weighted lagged ensemble requires the weights to sum to one. Such a scheme is called a weighted‐average lagged ensemble. In the limit of uncorrelated errors, the optimal weights are positive and decay monotonically with lead time, so that the least skillful forecasts have the least weight. In more realistic applications, the optimal weights do not always behave this way. This paper presents a series of analytic examples designed to illuminate conditions under which the weights of an optimal weighted‐average lagged ensemble become negative or depend nonmonotonically on lead time. It is shown that negative weights are most likely to occur when the errors grow rapidly and are highly correlated across lead time. The weights are most likely to behave nonmonotonically when the mean square error is approximately constant over the range forecasts included in the lagged ensemble. An extreme example of the latter behavior is presented in which the optimal weights vanish everywhere except at the shortest and longest lead times.

Was the Cold Eastern Us Winter of 2014 Due to Increased Variability

CanESM2 Canadian Centre for Climate Modeling and Analysis-Canada CNRM-CM5 National Centre for Meteorological Research-France CSIRO-BOM0 Commonwealth Scientific and Industrial Research Organisation-Australia HADGgem2-CC Met Office Hadley Centre-United Kingdom IPSL-CM5A-LR Institute Pierre Simon Laplace-France IPSL-CM5A-MR Institute Pierre Simon Laplace-France IPSL-CM5B-LR Institute Pierre Simon Laplace-France MIROC5-ESM-CHEM CCSR/NIES/FRCGC-Japan MRI-CGCM3 Meteorological Research Institute-Japan NCC-NorESM1-M Norwegian Climate Centre-Norway GFDL-ESM2G NOAA Geophysical Fluid Dynamics Laboratory-United States GFDL-ESM2M NOAA Geophysical Fluid Dynamics Laboratory-United States This document is a supplement to “Was the Cold Eastern US Winter of 2014 Due to Increased Variablility?,” by Laurie Trenary, Timothy Delsole, Machael K. Tippett, and Brian Doty, (Bull. Amer. Meteor. Soc., 96 (12), S15–S19) • DOI:10.1175/BAMS-D-15-00138.1

Sources of Bias in the Monthly CFSv2 Forecast Climatology

AbstractForecast 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 Nino-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this f...

Seasonal Predictability of Summer Rainfall over South America

AbstractThe seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use th...

Monthly ENSO Forecast Skill and Lagged Ensemble Size

Abstract The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real‐time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real‐time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8–10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

Confidence intervals in optimal fingerprinting

Optimal fingerprinting is a standard method for detecting climate changes. Among the uncertainties taken into account by this method, one is the fact that the response to climate forcing is not known exactly, but in practice is estimated from ensemble averages of model simulations. This uncertainty can be taken into account using an Error-in-Variables model (or equivalently, the Total Least Squares method), and can be expressed through confidence intervals. Unfortunately, the predominant paradigm (likelihood ratio theory) for deriving confidence intervals is not guaranteed to work because the number of parameters that are estimated in the Error-in-Variables model grows with the number of observations. This paper discusses various methods for deriving confidence intervals and shows that the widely-used intervals proposed in the seminal paper by Allen and Stott are effectively equivalent to bias-corrected intervals from likelihood ratio theory. A new, computationally simpler, method for computing these intervals is derived. Nevertheless, these confidence intervals are incorrect in the “weak-signal regime”. This conclusion does not necessarily invalidate previous detection and attribution studies because many such studies lie in the strong-signal regime, for which standard methods give correct confidence intervals. A new diagnostic is introduced to check whether or not a data set lies in the weak-signal regime. Finally, and most importantly, a bootstrap method is shown to give correct confidence intervals in both strong- and weak-signal regimes, and always produces finite confidence intervals, in contrast to the likelihood ratio method which can give unbounded intervals that do not match the actual uncertainty.

Extreme Eastern U.S. Winter of 2015 Not Symptomatic of Climate Change

Introduction. In late February 2015, a massive cold wave struck the entire U.S. eastern seaboard, bringing record cold temperatures from Maine to Florida (NOAA 2015). Due to the persistent cold, February 2015 ranked in the top ten coldest Februarys on record for a number of eastern seaboard states. Blizzard conditions accompanied the cold wave, placing the month among the top twenty snowiest for most of the northeastern United States (NOAA 2015). Collectively, the heavy snowfall and frigid temperatures were responsible for more than