North American Multimodel Ensemble Models Research Articles

Improved Predictability of the Indian Ocean Dipole Using a Stochastic Dynamical Model Compared to the North American Multimodel Ensemble Forecast

AbstractThis study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.

Early prediction of the Indian summer monsoon rainfall by the Atlantic Meridional Mode

Indian summer monsoon rainfall (ISMR) provides 80% of India’s total annual precipitation and thus has a tremendous impact on water resource management, agricultural yield and,consequently, India’s gross domestic product. Because of its importance, different studies have examined climatic controls on ISMR. Here we found a link between the spring (March–May; MAM) Atlantic meridional mode (AMM) and the ISMR, with the strengthening (weakening) of ISMR during the positive (negative) phase of the AMM. During the positive phase of the spring AMM, warmer sea surface temperatures (SSTs) persist over the tropical north Atlantic Ocean, strengthening anomalous cyclonic circulation and strengthening convection over the Sahel region; this process, in turn, modulates the winds over the Western Indian Ocean, cooling SST and strengthening monsoon circulation over India. However, during the negative phase of the spring AMM, we notice a weakening of the monsoon circulation over India. As a way of highlighting the importance of this climate connection, we compare the skill in forecasting ISMR using the information from the pre-monsoon AMM against the forecasts from the North American Multi-Model Ensemble (NMME). Over the years 1981–2017, our simple model produces more accurate results than the NMME models, and further highlights the crucial role played by pre-monsoon AMM in modulating the IMSR. This new finding will allow for Indian farmers and policy makers to better anticipate water availability from ISMR.

Subseasonal to Seasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by S2S and NMME Models

AbstractIn this study, the prediction of wintertime extratropical cyclone activity from subseasonal to seasonal timescale in current dynamical models' reforecasts is investigated. On seasonal time scales, the North American Multi‐Model Ensemble (NMME) models show skillful predictions over the eastern North Pacific, North America and the western North Atlantic with at least 5 months lead. The prediction skill is highly related to El Nino‐Southern Oscillation (ENSO), as using the ENSO‐related SST pattern gives rise to prediction skill with very similar spatial pattern and amplitude. On subseasonal time scales, models in the Seasonal to Sub‐seasonal Prediction (S2S) dataset have skillful predictions up to 4 weeks lead over regions from the eastern North Pacific to the western Atlantic, as well as northern Europe, the eastern Atlantic and East Asia. Generally, forecast skill improves with a larger ensemble size. The subseasonal prediction skill from the Pacific to the western Atlantic is related to ENSO, and that over eastern Atlantic, Europe and East Asia are associated with stratospheric polar vortex anomalies. Current models do not show much skill from the Madden‐Julian Oscillation (MJO), as the MJO impact on extratropical cyclone activity is not well captured by the models. European Centre for Medium‐Range Weather Forecasts (ECMWF) model has the highest single model subseasonal prediction skill. The prediction skill in the ECMWF model is higher than its estimated potential predictability, likely because the signal‐to‐noise ratio is too low in the model hindcasts.

RAINFALL FORECAST WITH BEST AND FULL MEMBERS OF THE NORTH AMERICAN MULTI-MODEL ENSEMBLE

The North American Multi-Model Ensemble (NMME) is a multi-model seasonal forecasting system consisting of models from combined US modelling centres. The NMME is expected to generate better rainfall prediction than a single model. However, the NMME forecasts are underdispersive or overdispersive, and calibration is needed to produce more accurate forecasting. This research examined the monthly rainfall data in Surabaya generated by nine NMME models and further calibrated them with bayesian model averaging (BMA). The purpose of this research was to assess the performance of the calibration results using the best four models and the full ensemble. The four models are CanCM3, CanCM4, CCSM3, and CCSM4, which were selected based on their skills. Both calibration results were evaluated using the continuous range probability score (CRPS) and the percentage of captured observations. The calibration with four models produced an average CRPS of 6.27 with 88.16% coverage, while with nine models an average CRPS of 5.23 with 92.11% coverage was obtained. This result suggests using the full ensemble to generate more accurate probabilistic forecasts .

Analysis of NMME system in simulating Indian summer monsoon rainfall

Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India.

Seasonal Forecasting of Wind and Waves in the North Atlantic Using a Grand Multimodel Ensemble

Abstract This study assesses the skill of multimodel forecasts of 10-m wind speed, significant wave height, and mean wave period in the North Atlantic for the winter months. The 10-m winds from four North American multimodel ensemble models and three European Multimodel Seasonal-to-Interannual Prediction project (EUROSIP) models are used to force WAVEWATCH III experiments. Ten ensembles are used for each model. All three variables can be predicted using December initial conditions. The spatial maps of rank probability skill score are explained by the impact of the North Atlantic Oscillation (NAO) on the large-scale wind–wave relationship. Two winter case studies are investigated to understand the relationship between large-scale environmental conditions such as sea surface temperature, geopotential height at 500 hPa, and zonal wind at 200 hPa to the NAO and the wind–wave climate. The very strong negative NAO in 2008/09 was not well forecast by any of the ensembles while most models correctly predicted the sign of the event. This led to a poor forecast of the surface wind and waves. A Monte Carlo model combination analysis is applied to understand how many models are needed for a skillful multimodel forecast. While the grand multimodel ensemble provides robust skill, in some cases skill improves once some models are not included.

Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study

A suite of statistical linear inverse models (LIMs) are used to understand the remote and local SST variability that influences SST predictions over the North Pacific region. Observed monthly SST anomalies in the Pacific are used to construct different regional LIMs for seasonal to decadal predictions. The seasonal forecast skills of the LIMs are compared to that from three operational forecast systems in the North American Multi-Model Ensemble (NMME), revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The data are also bandpass filtered into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. Moreover, we investigate the influence of the tropics and extra-tropics in the predictability of the SST over the region. The Extratropical North Pacific seems to be a source of predictability for the tropics on seasonal to interannual time scales, while the tropics enhance the forecast skill for the decadal component. These results indicate the importance of temporal scale interactions in improving the predictions on decadal timescales. Hence, we show that LIMs are not only useful as benchmarks for estimates of statistical skill, but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.

Diagnosing sea ice from the north american multi model ensemble and implications on mid-latitude winter climate

Arctic sea ice plays an important role in the climate system, moderating the exchange of energy and moisture between the ocean and the atmosphere. An emerging area of research investigates how changes, particularly declines, in sea ice extent (SIE) impact climate in regions local to and remote from the Arctic. Therefore, both observations and model estimates of sea ice become important. This study investigates the skill of sea ice predictions from models participating in the North American Multi-Model Ensemble (NMME) project. Three of the models in this project provide sea-ice predictions. The ensemble average of these models is used to determine seasonal climate impacts on surface air temperature (SAT) and sea level pressure (SLP) in remote regions such as the mid-latitudes. It is found that declines in fall SIE are associated with cold temperatures in the mid-latitudes and pressure patterns across the Arctic and mid-latitudes similar to the negative phase of the Arctic Oscillation (AO). These findings are consistent with other studies that have investigated the relationship between declines in SIE and mid-latitude weather and climate. In an attempt to include additional NMME models for sea-ice predictions, a proxy for SIE is used to estimate ice extent in the remaining models, using sea surface temperature (SST). It is found that SST is a reasonable proxy for SIE estimation when compared to model SIE forecasts and observations. The proxy sea-ice estimates also show similar relationships to mid-latitude temperature and pressure as the actual sea-ice predictions.

Improved ENSO Forecasting Using Bayesian Updating and the North American Multimodel Ensemble (NMME)

Abstract This study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Niño-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short lead months. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Niño-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Niño-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1–12; particularly for short leads) and target months (from January to December). However, for Niño-3, the BU-Model does not outperform NMME-EM forecasts for leads 7–11 and target months from June to October in terms of correlation and RMSE. Last, the authors test further potential improvements by preselecting “good” models (BU-Model-0.3) and by using principal component analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Niño-3/-3.4 for the 2015/16 El Niño event.

Combination of Multimodel Probabilistic Forecasts Using an Optimal Weighting System

Abstract In this study, an optimal weighting system is developed that combines multiple seasonal probabilistic forecasts in the North American Multimodel Ensemble (NMME). The system is applied to predict temperature and precipitation over the North American continent, and the analysis is conducted using the 1982–2010 hindcasts from eight NMME models, including the CFSv2, CanCM3, CanCM4, GFDL CM2.1, Forecast-Oriented Low Ocean Resolution (FLOR), GEOS5, CCSM4, and CESM models, with weights determined by minimizing the Brier score using ridge regression. Strategies to improve the performance of ridge regression are explored, such as eliminating a priori models with negative skill and increasing the effective sample size by pooling information from neighboring grids. A set of constraints is put in place to confine the weights within a reasonable range or restrict the weights from departing wildly from equal weights. So when the predictor–predictand relationship is weak, the multimodel ensemble forecast returns to an equal-weight combination. The new weighting system improves the predictive skill from the baseline, equally weighted forecasts. All models contribute to the weighted forecasts differently based upon location and forecast start and lead times. The amount of improvement varies across space and corresponds to the average model elimination percentage. The areas with higher elimination rates tend to show larger improvement in cross-validated verification scores. Some local improvements can be as large as 0.6 in temporal probability anomaly correlation (TPAC). On average, the results are about 0.02–0.05 in TPAC for temperature probabilistic forecasts and 0.03–0.05 for precipitation probabilistic forecasts over North America. The skill improvement is generally greater for precipitation probabilistic forecasts than for temperature probabilistic forecasts.

Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?

Abstract Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October–December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today’s coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data.

Seasonal predictions using a simple ocean initialization scheme

In this study, a simple ocean data assimilation scheme was applied to initialize a set of seasonal hindcasts, which started from each January, April, July and October during 1982–2010 with six ensemble members. In the scheme, sea surface temperature (SST) was the only observed information used to estimate the ocean initial states. Predictions of SST, 2-m temperature (T2m) and precipitation (Prate) over land were assessed and compared with hindcasts from the North American Multimodel Ensemble (NMME) project which were all based on sophisticated ocean initialization schemes with subsurface observations assimilated. The skill comparison indicated that, for all variables evaluated, the prediction skill by the simple ocean initialization procedure was well within the range of skills from individual NMME models. The result suggests that even though sophisticated initialization schemes have the potential to best capture the seasonal climate predictability, most present-day capabilities of seasonal predictions can also be accomplished by utilizing SST only. Further, significant seasonal dependence of prediction skill was also identified in hindcasts by the simple initialization scheme. Specifically, the ENSO SST predictions were featured by the significant “spring barrier” problem. It is argued that the ENSO prediction skill seasonality could be due to “double dip” contribution from the seasonality of climatic feedbacks in the tropical Pacific. In particular, these feedbacks not only play a role during the forecast leading to the largest errors in spring, but also cause the largest errors in the SST-derived ocean initial conditions in spring. Complementary aspects of seasonal forecasts based on simple initialization scheme, and use of low-resolution models are also discussed.

ENSO Precipitation and Temperature Forecasts in the North American Multimodel Ensemble: Composite Analysis and Validation

Abstract In this study, precipitation and temperature forecasts during El Niño–Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982–2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble

Abstract Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Oscillation (ENSO) teleconnections. Knowledge of the initial drought state is critical in SPI prediction, and there are considerable differences in observed SPI values between different datasets. Root-mean-square differences between datasets can exceed typical thresholds for drought, particularly in the tropics. This is particularly problematic for precipitation products available in near–real time. Thus, in the near term, the largest advances in the global prediction of meteorological drought are obtainable from improvements in near-real-time precipitation observations for the globe. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.

Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China

There is an increasing focus on the usefulness of climate model–based seasonal precipitation forecasts as inputs for hydrological applications. This study reveals that most models from the North American Multi-Model Ensemble (NMME) have potential to forecast seasonal precipitation over 17 hydroclimatic regions in continental China. In this paper, we evaluated the NMME precipitation forecast against observations. The evaluation indices included the correlation coefficient (R), relative root-mean-square error (RRMSE), rank histogram (RH), and continuous ranked probability skill score (CRPSS). We presented the RRMSE-R diagram to distinguish differences between the performances of individual models. We find that the predictive skill is seasonally and regionally dependent, exhibiting higher values in autumn and spring and lower values in summer. Higher predictive skill is observed over most regions except the southeastern monsoon regions, which may be attributable to local climatology and variability. Among the 11 NMME models, CFS, especially CFSv2, exhibits the best predictive skill. The GFDL and NASA models, which are followed by CMC, perform worse than CFS. The performances of IRI and CCSM3 are relatively worse than that of the other models. The forecast skills are significantly improved in multi-model mean forecasts based on simple model averaging (SMA). The improvement is more obvious for Bayesian model averaging (BMA), which is employed to further improve the forecast skill and address model uncertainty using multiple model outputs, than individual model and SMA.

Statistical Downscaling Multimodel Forecasts for Seasonal Precipitation and Surface Temperature over the Southeastern United States

Abstract This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

The seasonal climate predictability of the Atlantic Warm Pool and its teleconnections

AbstractOur study reveals that there is promising seasonal predictability of the Atlantic Warm Pool (AWP; defined as the area enclosed by the 28.5°C isotherm in tropical western Atlantic) and its associated teleconnections of late summer‐early fall seasonal rainfall anomalies over the continental US displayed by a subset of the North American Multi‐Model Ensemble (NMME). We find that of the four NMME models at least two models (CFSv2 and GFDL) exhibit consistently useful deterministic and probabilistic skill in predicting the July‐October (JASO) seasonal anomalies of the area of the AWP at lead times of up to four months. These two models shown are shown to beat the observed persistence skill by a significant margin at all lead times with the exception of lead time zero. The two NMME models also skillfully predict the teleconnections of the JASO AWP anomalies with the corresponding seasonal JASO rainfall anomalies in the eastern Mississippi valley. These prediction skills are significant improvement in warm season rainfall predictability over the continental US.