The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Prediction and Predictability of North American Seasonal Climate Variability

Monthly and seasonal climate prediction of variables such as precipitation, temperature, and sea surface temperature (SST) is of current interest in the scientific research community, and also has implications for users in the agricultural and water management domains, among others. This dissertation studies a variety of approaches to seasonal climate prediction of variables over North America, including both climate prediction systems and methods of analysis. We utilize the North American Multi-Model Ensemble (NMME) System for Intra-Seasonal to Inter-Annual Prediction (ISI) to study seasonal climate prediction skill over North America. We also use the Community Climate System Model version 4.0 (CCSM4) to preformed targeted climate prediction experiments to study contributions to skill or predictability from SSTs, land and atmosphere initialization, and ocean-atmosphere coupling. While all can be considered important for predictions, we show that for winter predictions, SST errors are a leading cause in forecast degradation, and improvement of SSTs causes a significant improvement in skill. Climate models, including those involved in NMME, typically overestimate eastern Pacific warming during central Pacific El Niño events, which can affect precipitation predictions regions that are influenced by teleconnections, such as the southeast US. Land and atmosphere initialization, and the minimization of errors in these initial states, shows moderate improvement in skill, expected for the first seasonal lead. Finally, ocean-atmosphere coupling, in the context of this experiment design and in relation to prescribed SST versus fully coupled hindcasts, is a comparatively weak contribution to prediction skill and predictability

CGCM and AGCM seasonal climate predictions: A study in CCSM4

Seasonal climate predictions are formulated from known present conditions and simulate the near‐term climate for approximately a year in the future. Recent efforts in seasonal climate prediction include coupled general circulation model (CGCM) ensemble predictions, but other efforts have included atmospheric general circulation model (AGCM) ensemble predictions that are forced by time‐varying sea surface temperatures (SSTs). CGCMs and AGCMs have differences in the way surface energy fluxes are simulated, which may lead to differences in skill and predictability. Concerning model biases, forecasted SSTs have errors compared to observed SSTs, which may also affect skill and predictability. This manuscript focuses on the role of the ocean in climate predictions and includes the influences of ocean‐atmosphere coupling and SST errors on skill and predictability. We perform a series of prediction experiments comparing coupled and uncoupled Community Climate System Model version 4.0 (CCSM4) predictions and forecasted versus observed SSTs to determine which is the leading cause for differences in skill and predictability. Overall, prediction skill and predictability are only weakly influenced by ocean‐atmosphere coupling, with the exception of the western Pacific, while errors in forecasted SSTs significantly impact skill and predictability. Comparatively, SST errors lead to more significant and robust differences in prediction skill and predictability versus inconsistencies in ocean‐atmosphere coupling. Key Points Prediction skill (predictability) is not significantly influenced (weakly influenced) by ocean‐atmosphere coupling Errors in SSTs lead to more significant differences in prediction skill and predictability versus ocean‐atmosphere coupling difference

Prediction and predictability of land and atmosphere initialized CCSM4 climate forecasts over North America

Subseasonal‐to‐seasonal prediction is influenced by slowly varying surface fields such as sea surface temperature (SST) and soil moisture. Fully coupled hindcasts were recently completed in the Community Climate System Model version 4.0 (CCSM4) as part of the North American Multi‐Model Ensemble project. Using similar land and atmosphere initialization strategies, but with prescribed climatological SSTs, we attempt to determine the isolated impact of combined observed atmosphere and land initialization and of observed atmosphere initialization on monthly precipitation and 2 m temperature prediction‐estimated skill (i.e., skill assessed without SST variability) and predictability on monthly time scales. CCSM4 has been cited as having low land‐atmosphere coupling, and while combined land and atmosphere initialization significantly increases the estimated skill of precipitation and temperature in the first month after initialization (lead 0), land initialization influence is weak, consistent with low land‐atmosphere coupling in CCSM4. In contrast, atmosphere initialization is a stronger contributor to prediction skill and predictability. We find stronger influence of land and atmosphere initialization on precipitation in CCSM4 versus results from CCSM3. Predictability results show that there is potential skill to be gained for both precipitation and temperature should model errors, atmosphere or land initial state errors, and/or land‐atmosphere coupling improve. Key Points Observed land and atmosphere initial states increase skill of both precipitation and 2 m temperature in CCSM4 hindcasts Perfect model predictability indicates skill to be gained should model errors, initial errors, or land‐atmosphere coupling strength improv

Southeastern U.S. Rainfall Prediction in the North American Multi-Model Ensemble

Abstract The present study investigates the predictive skill of the North American Multi-Model Ensemble (NMME) system for intraseasonal-to-interannual (ISI) prediction with focus on southeastern U.S. precipitation. The southeastern United States is of particular interest because of the typically short-lived nature of above- and below-normal extended rainfall events allowing for focus on seasonal prediction, as well as the tendency for more predictability in the winter months. Included in this study is analysis of the forecast quality of the NMME system when predicting above- and below-normal rainfall and individual rainfall events, with particular emphasis on results from the 2007 dry period. Both deterministic and probabilistic measures of skill are utilized in order to gain a more complete understanding of how accurately the system predicts precipitation at both short and long lead times and to investigate the multimodel aspect of the system as compared to using an individual predictive model. The NMME system consistently shows low systematic error and relatively high skill in predicting precipitation, particularly in winter months as compared to individual model results

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3-4 weeks, while this time scale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.ISSN:0003-0007ISSN:1520-047

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models