|May 7, 2013
1:00 PM – 2:30 PM ET
|Intraseasonal to Interannual Prediction|
|Speakers and Topics:||Ben Kirtman (University of Miami)
The Diversity of ENSO in the NMME Prediction Experiment
Vasu Misra (Florida State University)
Kathy Pegion (NOAA Earth System Research Laboratory)
Duane Waliser (NASA Jet Propulsion Laboratory)
Matt Newman (NOAA Earth System Research Laboratory)
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Ben Kirtman — The Diversity of ENSO in the NMME Prediction Experiment — The longitudinal position of the center of maximum sea surface temperature anomaly (SSTA) associated with El Niño has become the subject of considerable scientific interest and debate. Much of the debate centers on whether there are two kinds of El Niño – the canonical El Niño that has its maximum close to the coast of South America and the second kind that has its maximum in the central Pacific – or whether there is simply a continuum of events. This second kind of El Niño has several different names in the literature, e.g. “dateline El Niño”, “El Niño Modoki”, “Central Pacific El Niño” and “Warm Pool El Niño”. The above debate is important because there appears to be largely different teleconnections associated with the two types of El Niño and there is some evidence that the relative frequency of these two types changes with a changing climate. In this work, we are agnostic with respect to whether there are two kinds of El Niño or a continuum. We simply acknowledge that there is considerable variability in the central longitude of the maximum SSTA, and we ask whether the current generation of climate prediction systems can capture this variability.
Our approach is to assess whether current models capture the variability in the location of maximum warming within the context of the North American Multi-Model Ensemble (NMME) prediction experiment. The NMME experiment includes nine state-of-the-art coupled ocean-land-atmosphere models in which retrospective forecast have been initialized each month of 1982-2009, and six of the nine models continue to make forecasts in real-time. Our analysis focuses on the retrospective forecasts in the tropical Pacific, and we assess the forecast quality in terms of SSTA and rainfall anomalies. This large ensemble also allows us to assess whether the NMME models detect predictability differences associated with the variability in the longitude of maximum SSTA. Here we propose two simple measures of predictability that can be applied on a forecast-by-forecast case basis. The first predictability metric is a signal-to-noise ratio where we define the signal as the square of the ensemble mean anomaly and we define the noise as the deviation about the signal. The second predictability metric measures how well correlated the individual ensemble members are to the ensemble mean.
Vasu Misra — Global Seasonal Climate Predictability in a Two Tiered Forecast System vis-a-vis NMME — In this talk we examine the seasonal climate predictability for summer and winter starts from the Florida Climate Institute Seasonal Hindcasts at 50km (FISH50) and compare them with the National Multi-Model Ensemble Project (NMME) models.
FISH50 is a two-tier forecast which takes the multi-model SST forecast from two of the NMME models (CCSM3 and CFSv2) and applies a bias correction to it, with no implied cheating. In other words, the observed climatology used for the bias correction of the SST does not include the hindcast period of the NMME (viz., 1982-present). We will focus on the seasonal prediction of global precipitation and surface temperature and also show some results specifically focused on the Asian summer monsoon.
Kathy Pegion — Forecasting Forecast Skill: Can An ENSO-Conditional Skill Mask Improve Seasonal Predictions? — The contribution of the CFSv2 forecast to the Climate Prediction Center’s official seasonal forecast products is dependent on the month and lead-time dependent skill of the retrospective forecasts. If the skill for a given lead-time and month is less than 0.3 in the retrospective forecasts, the model is considered not skillful at that location and the model forecast is not used in making the official forecast. It would be beneficial to have a skill mask that is conditional on a priori identification of times when the skill is expected to be higher. For seasonal prediction, it is likely that such a skill mask is dependent on the phase of the El Nino Southern Oscillation (ENSO), although other large-scale modes of climate variability may also play a role.
We investigate the potential for improving the skill of seasonal predictions by exploring the relationship between the Nino3.4 index and perfect model skill of the CFSv2 re-forecasts for temperature over the continental United States with the goal of developing a conditional skill mask for U.S. seasonal temperature and precipitation forecasts.
Duane Waliser — Intraseasonal Variability Hindcast Experiment (ISVHE) — Intraseasonal Variability (ISV) serves as a crucial source of predictability, providing a bridge between the predictability of weather and seasonal climate prediction. Notable components of ISV include the well-known eastward propagating Madden Julian Oscillation (MJO), which impacts a wide variety of weather and climate phenomena, and the northward propagating intraseasonal oscillations that strongly dictate the onset and breaks of the Asian summer monsoon. To better quantify the predictability of ISV and exploit it for operational forecasts, the ISV Hindcast Experiment (ISHVE) was launched jointly in 2009 by the CLIVAR Asian-Australian Monsoon Panel (AAMP), the WCRP-WWRP/THORPEX YOTC MJO Task Force, and the Scientific Steering Committee of Asian Monsoon Year (AMY). The ISVHE project is the first attempt to produce a long-term hindcast dataset that specifically targets the needs and themes associated with intraseaonal prediction research. The objectives of the ISVHE are to: 1) better understand the physical basis for intraseasonal prediction and estimate the potential and practical predictability of ISV, including the MJO, in a multi-model framework, and 2) develop optimal strategies for a multi-model ensemble ISV prediction system, including optimal initialization schemes and quantification of prediction skill with forecast metrics under operational conditions. This presentation will briefly summarize the results of the ISVHE to date and use the venue to describe the experiment and encourage additional utilization of this unique and valuable multi-model experiment. ISVHE has been supported by Asia-Pacific Economic Cooperation Climate Center and the NOAA Climate Test Bed.