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43rd NOAA Climate Diagnostics and Prediction Workshop S2S Prediction and Monitoring Success Stories from CPC and S2S Prediction Challenges for the Community |
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October 23, 2018 The 43rd NOAA Climate Diagnostics and Prediction Workshop was successfully held in the University of California, Santa Barbara, CA. In the opening session, NOAA Climate Prediction Center (CPC) Director David DeWitt gave a speech on progresses in improving S2S prediction and monitoring products and services and challenges faced by forecasters to continue development. The comprehensive skill metric (CSM) based on all CPC extended to long range outlooks revealed continuous improvement of CPC products. In a recent assessment followed by the Government Performance and Results Act (GPRA), CPC seasonal prediction of U.S. surface temperature went up to a record high of the performance skill (Fig.1). The advancement was inseparable with ongoing R2O activities, such as improved objective seasonal consolidation for statistical models and North American Multi-Model Ensemble (NMME) with added research models, calibration enabled by GEFS reforecasts, and leveraging from shorter lead forecasts etc. |
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The progresses were also reflected in newly implemented systems, tools and products exemplified by Modular Ocean Model version 6 (MOM6) Hybrid Global Ocean Data Assimilation System (GODAS), weak 3-4 Arctic sea ice prediction system, conventional observation reanalysis (CORe) to replace NCEP/NCAR Reanalysis 1, second generation CPC MORPHing technique (CMORPH), ENSO strength outlooks, and high-winds tool for week two probabilistic extremes outlook, to name a few. In view of future challenges, David DeWitt made a list of difficult problems for research reference. 1. Improving forecast skill for tropical SST beyond the current ability to forecast large SST anomalies in the central and eastern Pacific. |
Fig. 1 48 month running mean of T. Heidke skill score for seasonal forecast of US surface temperature. Each score plotted is an average of scores over the past 48 three moth "seasons". This skill score has been established as GPRA performance measure. The requirement of demonstrate program impact was mandated by Congress in 1993 through the Government Performance and Results Act (GPRA). |
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2. Improving forecast skill for tropical precipitation beyond the current ability to forecast local precipitation anomalies associated with large SST anomalies in the central and eastern Pacific. 3. Improving modeling of coupled atmosphere-ocean interactions in order to simulate the coupling strength with fidelity in the tropics. 4. Understanding why there is a predictive skill minimum in upper level flow over the western US on S2S timescales. In closing, Director DeWitt placed hopes on people in field with considerable effort, and sufficient and sustained investment to work together on trying to make a break through. |
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North American Multi-Model Ensemble Teleconference Verification of SSH hindcasts using multiple ocean reanalyses November 1, 2018 Sea surface height (SSH) has been used to measure the ocean currents that move heat around the globe as a critical component of Earth's climate. Its relationships with SST, salinity, tides, waves, and the atmospheric pressure loading patterns are of great interest of climate forecasters. To promote R2O activities for service improvement, the NMME project is planning to make its SSH hindcast data available to the community. In the teleconference this month, Dr. Bill Merryfield of the Canadian Centre for Climate Modelling and Analysis (CCCma) was invited to give a talk on his study of the verification of CCCma Coupled Climate Model, versions 3 and 4 (CanCM3/4) SSH hindcasts using multiple ocean reanalyses, i.e. Ocean ReAnalysis System 4 (ORAS4, ECMWF), Global Ocean Data Assimilation System (GODAS, NCEP), Cimate Forecast System Reanalysis (CFSR, NCEP) and German Estimating the Circulation and Climate of the Ocean, version 2 (GECCO2, University of Hamburg). His results showed hindcast skill of CanCM3/4 SSH was generally high (comparable to that for SST), although large differences existed depending on region and verification reanalysis product used, particularly in the Atlantic & Southern Oceans. |
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Among the four reanalyses, the spatial mean skill was the highest for GODAS; this relationship may be at least partially attributed to the use of GODAS ocean temperatures to initialize CanCM3/4 hindcasts, as well as the lack of Arctic data in GODAS. Dr. Merryfield's study revealed skill may be the highest when using the multi-reanalysis mean for verification. Inconsistencies were found between reanalyses in global SSH trends; most of the reanalyses lack any realistic trend. Further work to improve skill by replacing global trend with observed trends are in progress. |
Get oral/poster presentation files by click the corresponding picture. |
