MAPP Webinar Series: Research and Forecasting Using the NMME Seasonal Prediction System

  • 21 February 2017
  • Number of views: 616

The NOAA CPO Modeling, Analysis, Predictions, and Projections (MAPP) program hosted a webinar on the topic of Research and Forecasting Using the North American Multi-Model Ensemble (NMME) Seasonal Prediction System on Tuesday, February 28, 2017. The announcement is provided below.

Date/Time Title & Presenters


February 28, 2017
1:00 PM - 2:30 PM ET

Research and Forecasting Using the North American Multi-Model Ensemble (NMME) Seasonal Prediction System
Speakers and Topics Kathleen Pegion (George Mason University)
Assessing the Fidelity of Predictability Estimates

Jason C. Furtado (University of Oklahoma)
Predictability of the Tropospheric NAM and Sudden Stratospheric Warming Events in the NMME Phase-2 Models

Michael Alexander (NOAA/OAR/ESRL Physical Sciences Division)
Skill of Coastal SST Forecasts, including the California Current System, from the North American Multi-model Ensemble

David DeWitt (NOAA/NWS/NCEP Climate Prediction Center)
Use of NMME Forecast Guidance in Climate Prediction Center Operations
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ABSTRACT:/p>

Assessing the Fidelity of Predictability Estimates
Kathleen Pegion, George Mason University

Predictability is an intrinsic limit of the climate system due to uncertainty in initial conditions and the chaotic nature of the atmosphere. Estimates of predictability together with calculations of current prediction skill are used to define the gaps in our prediction capabilities, inform future model developments, and indicate to stakeholders the potential for making forecasts that can inform their decisions. The true predictability of the climate system is not known and must be estimated, typically using a perfect model estimate from an ensemble prediction system. However, different prediction systems can give different estimates of predictability. Can we determine which estimate of predictability is most representative of the true predictability of the climate system? We test three metrics as potential indicators of the fidelity of predictability estimates in an idealized framework -- the spread-error relationship, autocorrelation and skill. Using the North American Multi-model Ensemble re-forecast database, we quantify whether these metrics accurately indicate a model's ability to properly estimate predictability. It is found that none of these metrics is a robust measure for determining whether a predictability estimate is realistic for Nino3.4. For temperature and precipitation over land, errors in the spread-error ratio are related to errors in estimating predictability at the shortest lead-times, while skill is not related to predictability errors. The relationship between errors in the autocorrelation and errors in estimating predictability varies by lead-time and region.

Predictability of the Tropospheric NAM and Sudden Stratospheric Warming Events in the NMME Phase-2 Models
Jason C. Furtado (University of Oklahoma), Judah Cohen (Atmospheric and Environmental Research), Emily Becker and Dan Collins (NOAA Climate Prediction Center)

The Northern Annular Mode (NAM) is the leading mode of variability of the Northern Hemisphere (NH) wintertime extratropical circulation in both the troposphere and stratosphere. Changes in the tropospheric NAM directly alter NH mid-latitude temperature and precipitation patterns and potentially increase chances for extreme winter weather in major population centers. These features make NAM predictability a significant priority for subseasonal-to-seasonal (S2S) wintertime forecasts. This study examines the predictability of the wintertime tropospheric NAM in the hindcast simulations of the North American Multi-Model Ensemble Phase-2 (NMME-2) model suite, specifically through examining how the models capture the evolution of sudden stratospheric warming (SSW) / weak polar vortex events. SSW events are well-known to precede large changes in the tropospheric NAM by 2-6 weeks, thereby offering extended predictability for mid-latitude winter weather. Findings indicate that the available NMME-2 models (CCSM4, CanCM3, and CanCM4) have an overall mixed performance in capturing the spatiotemporal characteristics of the near-surface NAM and its teleconnections. Strong biases are apparent in the persistence of positive vs. negative NAM regimes, the strength of the Atlantic jet stream, and polar vortex variability. For the lifecycle of simulated SSW events (i.e., those identified within the models), significant biases exist with stratosphere-troposphere coupling diagnostics, similar to those seen in other coupled models from other studies. For example, downward propagation of the stratospheric signal into the troposphere appears only in one model, with the other two models failing to show any connection between the two layers. Issues with precursor patterns leading up to SSWs as well as post-SSW impacts (e.g., 500 mb heights, surface temperature patterns) are also found to be inconsistent with, and sometimes opposite of, those derived from observations. These factors collectively impact the use of the NMME-2 subseasonal forecasts for potential high-impact winter weather regimes. Potential sources of error and pathways forward will also be discussed.

Skill of Coastal SST Forecasts, including the California Current System, from the North American Multi-model Ensemble
Michael Alexander (NOAA/OAR/ESRL Physical Sciences Division)

Variability in the ocean state, especially the sea surface temperature (SST), is known to strongly influence marine ecosystems. As a first step in the process of ecological forecasting we explored SST forecasts in large marine ecosystems (LMEs), including the California Current System (CCS) from the coupled climate models in the North American Multi-Model Ensemble (NMME, Kirtman et al. 2014, BAMS). As in most regions, the ensemble mean monthly SST predictions for the CCS have skill and it is greater than those from most individual models, especially for probabilty forecasts, i.e. what chance would a predicted SST anomaly be above (upper tercile) or below (lower tercile) average.
We explored several mechanisms that could drive SST predictability in the CCS, using the Canadian forecast model (CanCM4), perhaps the most skillful NMME member in the CCS. Skill mainly arises due to ENSO teleconnections to the extratropics and persistence of SST anomalies. The forecasts of SSTs in the CCS were shown to improve predictions of sardine biomass.

Use of NMME Forecast Guidance in Climate Prediction Center Operations
David DeWitt (NOAA/NWS/NCEP Climate Prediction Center)

This presentation will demonstrate how CPC forecasters use the NMME guidance to inform the development of operational forecast products including the monthly and seasonal temperature and precipitation outlooks and associated downstream products, and the El-Nino Diagnostic Discussion.

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P: 301-734-1237
E: annarita.mariotti@noaa.gov

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