David G. DeWitt

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...

Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing?

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Nino- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of sta...

Multimodel Ensembling in Seasonal Climate Forecasting at IRI

The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) c...

Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts

Abstract This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly beca...

Retrospective Forecasts of Interannual Sea Surface Temperature Anomalies from 1982 to Present Using a Directly Coupled Atmosphere–Ocean General Circulation Model

Abstract A large number of ensemble hindcasts (or retrospective forecasts) of tropical Pacific sea surface temperature (SST) have been made with a coupled atmosphere–ocean general circulation model (CGCM) that does not employ flux correction in order to evaluate the potential skill of the model as a seasonal forecasting tool. Oceanic initial conditions are provided by an ocean data assimilation system. Ensembles of seven forecasts of 6-month length are made starting each month in the 1982 to 2002 period. Skill of the coupled model is evaluated from both a deterministic and a probabilistic perspective. The skill metrics are calculated using both the bulk method, which includes all initial condition months together, and as a function of initial condition month. The latter method allows a more objective evaluation of how the model has performed in the context in which forecasts are actually made and applied. The deterministic metrics used are the anomaly correlation and the root-mean-square error. The couple...

Ocean Data Assimilation, Initialization, and Predictions of ENSO with a Coupled GCM

Abstract A scheme for making seasonal to interannual predictions of El Nino–Southern Oscillation with a coupled atmosphere–ocean general circulation model that incorporates subsurface ocean measurements in the initial conditions is described. Anomaly initial conditions are used in order to reduce initial shock and climate drift. The ocean component of the prediction model has a nearly global domain, and the coupled model does not employ anomaly coupling or empirical statistical corrections. Initial conditions for the ocean were obtained from a near-global ocean analysis produced by an ocean data assimilation system. The assimilation system uses a variationally formulated optimal interpolation method to analyze oceanic fields from temperature observations and a first-guess field provided by integrating a global ocean general circulation model. The period of the analysis was 1986 through 1992. The anomaly initial conditions for the ocean were generated by adding the anomalies of the assimilated fields from ...

Retrospective ENSO Forecasts: Sensitivity to Atmospheric Model and Ocean Resolution

Abstract Results are described from a series of 40 retrospective forecasts of tropical Pacific SST, starting 1 January and 1 July 1980–99, performed with several coupled ocean–atmosphere general circulation models sharing the same ocean model—the Modular Ocean Model version 3 (MOM3) OGCM—and the same initial conditions. The atmospheric components of the coupled models were the Center for Ocean–Land–Atmosphere Studies (COLA), ECHAM, and Community Climate Model version 3 (CCM3) models at T42 horizontal resolution, and no empirical corrections were applied to the coupling. Additionally, the retrospective forecasts using the COLA and ECHAM atmospheric models were carried out with two resolutions of the OGCM. The high-resolution version of the OGCM had 1° horizontal resolution (1/3° meridional resolution near the equator) and 40 levels in the vertical, while the lower-resolution version had 1.5° horizontal resolution (1/2° meridional resolution near the equator) and 25 levels. The initial states were taken fro...

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the twotiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

Tropical pacific forcing of a 1998–1999 climate shift: observational analysis and climate model results for the boreal spring season

Observational data and climate model simulations and experiments are utilized to document an abrupt shift in Pacific sea surface temperatures (SSTs) and associated atmospheric conditions, which occurred in 1998–1999. Emphasis is placed on the March–May (MAM) season, as the motivation for the work is to extend a recent study that reported an abrupt decline in East African MAM rainfall at that time. An empirical orthogonal function analysis of MAM SSTs over the last century following the removal of the concurrent influence of the El Niño-Southern Oscillation and global warming trend by linear regression reveals a pattern of multidecadal variability in the Pacific similar to the Pacific Decadal Oscillation. Examination of MAM precipitation variations since 1940 indicates, among other findings, that recurrent drought events since 1999 in East Africa, central-southwest Asia, parts of eastern Australia and the southwestern US are all regional manifestations of a global scale multidecadal pattern. Associated shifts in the low-level wind field and upper-level stationary waves are discussed. Simulations using an atmospheric climate model forced with observed, global SSTs capture many of the salient precipitation and atmospheric circulation features associated with the observed shift. Further, when the model is forced only with observed SSTs from the tropical Pacific it also captures many of the observed atmospheric changes, including the abrupt shift in 1999. The results point to the fundamental role played by the tropical Pacific in driving the response to multidecadal variability of SSTs in the basin and provide important context for recent seasonal climate extremes in several regions of the globe.

Predictive Skill of AGCM Seasonal Climate Forecasts Subject to Different SST Prediction Methodologies

Abstract This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean–atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocea...