Johnna M. Infanti

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 2...

North American rainfall and temperature prediction response to the diversity of ENSO

Research has shown that there is significant diversity in the location of the maximum sea surface temperature anomaly (SSTA) associated with the El Niño Southern Oscillation (ENSO). In one extreme, warm SSTA peak near the South American coast (often referred to as Eastern Pacific of EP El Niño), and at the other extreme, warm SSTA peak in the central Pacific (Central Pacific or CP El Niño). Due to the differing tropical Pacific SSTA and precipitation structure, there are differing extratropical responses, particularly over North America. Recent work involving the North American Multi-Model Ensemble (NMME) system for intra-seasonal to inter-annual prediction on prediction of the differences between El Niño events found excess warming in the eastern Pacific during CP El Niño events. This manuscript investigates the ensemble and observational agreement of the NMME system when forecasting the North American response to the diversity of ENSO, focusing on regional land-based 2-meter temperature and precipitation. NMME forecasts of North American precipitation and T2m agree with observations more often during EP events. Ensemble agreement of NMME forecasts is regional. For instance, ensemble agreement in Southeast North America demonstrates a strong connection to NINO3 precipitation and SSTA amplitude during warm ENSO events. Ensemble agreement in Northwest North America demonstrates a weak connection to NINO4 precipitation and SSTA amplitude during warm ENSO events. Still other regions do not show a strong connection between ensemble agreement and strength of warm ENSO events.

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

AbstractThe 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 s...

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.

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.

High temporal resolution modeling of the impact of rain, tides, and sea level rise on water table flooding in the Arch Creek basin, Miami-Dade County Florida USA

Modeling of groundwater levels in a portion of the low-lying coastal Arch Creek basin in northern Miami-Dade County in Southeast Florida USA, which is subject to repetitive flooding, reveals that rain-induced short-term water table rises can be viewed as a primary driver of flooding events under current conditions. Areas below 0.9m North American Vertical Datum (NAVD) elevation are particularly vulnerable and areas below 1.5m NAVD are vulnerable to exceptionally large rainfall events. Long-term water table rise is evident in the groundwater data, and the rate appears to be consistent with local rates of sea level rise. Linear extrapolation of long-term observed groundwater levels to 2060 suggest roughly a doubling of the number of days when groundwater levels exceed 0.9m NAVD and a threefold increase in the number of days when levels exceed 1.5m NAVD. Projected sea level rise of 0.61m by 2060 together with increased rainfall lead to a model prediction of frequent groundwater-related flooding in areas<0.9m NAVD. However, current simulations do not consider the range of rainfall events that have led to water table elevations>1.5m NAVD and widespread flooding of the area in the past. Tidal fluctuations in the water table are predicted to be more pronounced within 600m of a tidally influenced water control structure that is hydrodynamically connected to Biscayne Bay. The inland influence of tidal fluctuations appears to increase with increased sea level, but the principal driver of high groundwater levels under the 2060 scenario conditions remains groundwater recharge due to rainfall events.