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Home » MAPP Webinar

MAPP Webinar

Date/Time Title
December 11, 2012
1:00 PM – 2:30 PM ET
Model Development and Analysis
Speakers and Topics:   Joao Teixeira (NASA JPL)
Turbulence and Convection Parameterizations: the Eddy-Diffusivity/Mass-Flux (EDMF) Approach and its Implementation into the GFS Model
Ning Zeng (University of Maryland)
Predicting Ecosystems and Carbon Cycle using NCEP/CFS seasonal-interannual climate prediction products
Tom Hamill (NOAA ESRL)
NOAA’s Second-Generation Global Ensemble Reforecast Data Set
Melissa Bukovsky (NCAR)
Towards Establishing NARCCAP Regional Model Credibility Through Process-Based Analysis
Shaoqing Zhang (NOAA GFDL)
Parameter Estimation in Coupled Models: Opportunities and Challenges
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Joao Teixeira — Turbulence and Convection Parameterizations: the Eddy-Diffusivity/Mass-Flux (EDMF) Approach and its Implementation into the GFS Model — Weather and Climate prediction models are still quite inaccurate in representing clouds, turbulence, and convection. The problem is that these processes can occur in a variety of scales, from the planetary scale to small scales that cannot be represented explicitly in any atmospheric model. A major challenge in climate and weather prediction is how to improve the representation of these sub-grid scale physical processes: the parameterization problem. This talk will focus on a major issue in particular: how to represent vertical sub-grid scale turbulent and convective flow in the boundary layer. A new parameterization for turbulence and convection involving optimal combinations of Eddy-Diffusivity (ED) and Mass-Flux (MF) methods – the EDMF approach – is described in detail. Results from a recent implementation of a dry version of EDMF into the GFS model will be discussed.

Ning Zeng — Predicting Ecosystems and Carbon Cycle using NCEP/CFS seasonal-interannual climate prediction products — A prototype prediction system was developed for dynamical ecosystem and global carbon cycle prediction on seasonal-interannual timescales. Using a 25-year hindcast experiment, we explore the feasibility of seasonal-interannual prediction of terrestrial ecosystem and the global carbon cycle. This has been achieved using a forecasting system in which the dynamic vegetation and terrestrial carbon cycle model VEGAS was forced with the 15-member ensemble climate prediction and lead time up to 9 month from the NCEP/CFS climate forecast system. The results show that the predictability is dominated by the ENSO signal for its major infuence on the tropical and subtropical regions, including the Amazon, Indonesia, western US and central Asia. There is also important non-ENSO related predictability such as that associated with midlatitude drought. Comparison of the dynamical prediction results with benchmark statistical methods show that the dynamical method is significantly better than benchmark statistical methods such as anomaly persistence and damping. The hindcasted ecosystem variables and carbon flux show significantly slower decrease in skill compared to the climate forcing, partly due to the memories in land and vegetation processes that filter out the higher frequency noise and sustain the signal.

Tom Hamill — NOAA’s Second-Generation Global Ensemble Reforecast Data Set — A multi-decadal ensemble reforecast database is now available that is consistent with the operational 2012 NOAA Global Ensemble Forecast System (GEFS).  The reforecast data set consists of an 11-member ensemble run once each day from 0000 UTC initial conditions.   Reforecasts are run to +16 days.  As with the operational 2012 GEFS, the reforecast is run at T254L42 resolution (approximately ½-degree grid spacing, 42 levels) for week +1 forecasts and T190L42 (approximately ¾-degree grid spacing) for the week +2 forecasts.  Reforecasts were initialized with Climate Forecast System Reanalysis initial conditions, and perturbations were generated using the ensemble transform with rescaling technique.  Reforecast data is available from 1985 to current.

Reforecast data sets were previously demonstrated to be very valuable for detecting and correcting systematic errors in forecasts, especially forecasts of relatively rare events and longer-lead forecasts.  What is novel about this reforecast data set relative to the first-generation NOAA reforecast is that: (a) a modern, currently operational version of the forecast model is used (the previous reforecast used a model version from 1998); (b) a much larger set of output data has been saved, including variables relevant for precipitation, hydrologic, wind-energy, solar-energy, severe weather, and tropical cyclone forecasting; and (c) the archived data is at much higher resolution.

The talk will describe more about the reforecast configuration and provide a few examples of how this second-generation reforecast data may be used for research and a variety of weather-climate forecast applications.

Melissa Bukovsky — Towards Establishing NARCCAP Regional Model Credibility Through Process-Based Analysis — This talk will present preliminary results from our analysis of the simulations produced by the North American Regional Climate Change Assessment Program (NARCCAP) in terms of their ability to simulate warm-season precipitation over the Northeast and Southwest U.S.  We focus on precipitation and the drivers behind the precipitation biases seen in the simulations of current climate.  Thus, we take a process-based approach to the question of model fidelity in order to help establish our confidence in the models.  The end goal of this study is to determine the credibility of warm-season precipitation projections made by the NARCCAP regional climate models (RCMs), their global climate model (GCM) drivers, and select GCM simulations completed for CMIP5 over these two regions plus the central U.S.

Shaoqing Zhang — Parameter Estimation in Coupled Models: Opportunities and Challenges — Traditional coupled data assimilation that uses observations from the climate observing system to only estimate climate states can produce artifacts in the estimated variability and cause prediction drift due to the existence of model bias. With ensemble methodology, a new strategy for climate estimation and prediction initialization uses climate observational information to optimize both coupled model states and parameters. The new climate estimation method can produce a much better fitting between the coupled reanalysis and observations. The new initialization strategy in which the prediction model not only starts from observation-optimized coupled states but also uses observation-optimized model parameters can significantly reduce model drift in predictions. This presentation summarizes the research advances in the ensemble coupled model parameter estimation and discusses the challenges of applications in CGCMs.



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