Kathy Pegion

Local Air–Sea Relationship in Observations and Model Simulations

Abstract The present study compares the local simultaneous correlation between rainfall–evaporation and sea surface temperature (SST)–SST tendency among observations, coupled general circulation model (CGCM) simulations, and stand-alone atmospheric general circulation model (AGCM) simulations. The purpose is to demonstrate to what extent the model simulations can reproduce the observed air–sea relationship. While the model-simulated correlation agrees with the observations in the tropical eastern Pacific, large discrepancies are found in the subtropics, midlatitudes, and tropical Indo-western Pacific Ocean regions. In tropical Indo-western Pacific Ocean regions and the midlatitudes where the atmosphere contributes to the observed SST changes, the specified SST simulations produce excessive SST forcing, whereas the CGCM captures the atmospheric feedback on the SST, but with somewhat of an overestimation. In the subtropics, both the AGCM and CGCM produce unrealistic positive rainfall–SST correlations. In th...

The Impact of Air–Sea Interactions on the Simulation of Tropical Intraseasonal Variability

Abstract The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between ...

Internal Atmospheric Dynamics and Tropical Indo-Pacific Climate Variability

Abstract One possible explanation for tropical sea surface temperature (SST) interannual variability is that it can be accurately described by a linear autoregressive model with damped coupled feedbacks and stochastic forcing. This autoregressive model can be viewed as a “null hypothesis” for tropical SST variability. This paper advances a new coupled general circulation model (CGCM) coupling strategy, called an interactive ensemble, as a method to test this null hypothesis. The design of the interactive ensemble procedure is to reduce the stochastic variability in the air–sea fluxes applied to the ocean component while retaining the deterministic component of the coupled feedbacks. The interactive ensemble procedure uses multiple realizations of the atmospheric GCM coupled to a single realization of the ocean GCM. The ensemble mean of the atmospheric GCM fluxes are applied to the ocean model thereby significantly reducing the variability due to internal atmospheric dynamics in the air–sea fluxes. If the ...

The Impact of Air–Sea Interactions on the Predictability of the Tropical Intraseasonal Oscillation

Abstract This study investigates whether air–sea interactions contribute to differences in the predictability of the boreal winter tropical intraseasonal oscillation (TISO) using the NCEP operational climate model. A series of coupled and uncoupled, “perfect” model predictability experiments are performed for 10 strong model intraseasonal events. The uncoupled experiments are forced by prescribed SST containing different types of variability. These experiments are specifically designed to be directly comparable to actual forecasts. Predictability estimates are calculated using three metrics, including one that does not require the use of time filtering. The estimates are compared between these experiments to determine the impact of coupled air–sea interactions on the predictability of the tropical intraseasonal oscillation and the sensitivity of the potential predictability estimates to the different SST forcings. Results from all three metrics are surprisingly similar. They indicate that predictability e...

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.

More reliable coastal SST forecasts from the North American multimodel ensemble

The skill of monthly sea surface temperature (SST) anomaly predictions for large marine ecosystems (LMEs) in coastal regions of the United States and Canada is assessed using simulations from the climate models in the North American Multimodel Ensemble (NMME). The forecasts based on the full ensemble are generally more skillful than predictions from even the best single model. The improvement in skill is particularly noteworthy for probability forecasts that categorize SST anomalies into upper (warm) and lower (cold) terciles. The ensemble provides a better estimate of the full range of forecast values than any individual model, thereby correcting for the systematic over-confidence (under-dispersion) of predictions from an individual model. Probability forecasts, including tercile predictions from the NMME, are used frequently in seasonal forecasts for atmospheric variables and may have many uses in marine resource management.

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Sub-seasonal to seasonal (S2S) retrospective forecasts from three global coupled models are used to evaluate the predictability of the onset and demise dates of the rainy season over monsoonal regions. The onset and demise dates of the rainy season are defined using only precipitation data. The forecasts of the onset and demise dates of the rainy season are based on a hybrid methodology that combines observations and simulations. Although skillful model precipitation predictions remain challenging in many regions, our results show that they are skillful enough to identify onset and demise dates of the rainy season in many monsoon regions at sub-seasonal (approximately 30 days) lead-times in retrospective forecasts. We verify sub-seasonal prediction skill for the onset and demise dates of the rainy season over South America, East Asia, and Northern Australia. However, we find low prediction skill for the onset and demise of the rainy season on sub-seasonal scales over the Indian monsoon region. This information would be valuable to sectors related to water management.

The South Pacific Meridional Mode as a Thermally Driven Source of ENSO Amplitude Modulation and Uncertainty

AbstractThis study seeks to identify thermally-driven sources of ENSO amplitude and uncertainty, as they are relatively unexplored compared to wind-driven sources. Pacific meridional modes are argued to be wind triggers for ENSO events. This study offers an alternative role for the South Pacific Meridional Mode (SPMM) in ENSO dynamics, not as an ENSO trigger, but as a coincident source of latent heat flux (LHF) forcing of ENSO SSTA that if correctly (incorrectly) predicted, could reduce (increase) ENSO prediction errors. We utilize a coupled model simulation in which ENSO variability is perfectly periodic and each El Nino experiences identical wind stress forcing. Differences in El Nino amplitude are primarily thermally-driven via the SPMM. When El Nino occurs coincidentally with positive phase SPMM, the positive SPMM LHF anomaly counteracts a fraction of the LHF damping of El Nino, allowing for a more intense El Nino. If the SPMM phase is instead negative, the SPMM LHF amplifies the LHF damping of El Nin...

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.