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

Observational Evidence that Soil Moisture Variations Affect Precipitation

Land-atmosphere feedback, by which precipitation-induced soil moisture anomalies affect subsequent precipitation, may be an important element of Earth's climate system, but its very existence has never been demonstrated conclusively at regional to continental scales. Evidence for the feedback is sought in a 50-year observational precipitation dataset covering the United States. The precipitation variance and autocorrelation fields are characterized by features that agree (in structure, though not in magnitude) with those produced by an atmospheric general circulation model (AGCM). Because the model-generated features are known to result from land-atmosphere feedback alone, the observed features are highly suggestive of the existence of feedback in nature

The Experimental MJO Prediction Project

Weather prediction is typically concerned with lead times of hours to days, while seasonal-to-interannual climate prediction is concerned with lead times of months to seasons. Recently, there has been growing interest in 'subseasonal' forecasts---those that have lead times on the order of weeks (e.g., Schubert et al. 2002; Waliser et al. 2003; Waliser et al. 2005). The basis for developing and exploiting subseasonal predictions largely resides with phenomena such as the Pacific North American (PNA) pattern, the North Atlantic oscillation (NAO), the Madden-Julian Oscillation (MJO), mid-latitude blocking, and the memory associated with soil moisture, as well as modeling techniques that rely on both initial conditions and slowly varying boundary conditions (e.g., tropical Pacific SST). An outgrowth of this interest has been the development of an Experimental MJO Prediction Project (EMPP). Th project provides real-time weather and climate information and predictions for a variety of applications, broadly encompassing the subseasonal weather-climate connection. Th focus is on the MJO because it represents a repeatable, low-frequency phenomenon. MJO's importance among the subseasonal phenomena is very similar to that of El Nino-Southern Oscillation(ENSO) among the interannual phenomena. This note describes the history and objectives of EMPP, its status,capabilities, and plans