Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA

Slater, Louise; Villarini, Gabriele; Bradley, A. Allen

doi:10.1007/s00382-016-3286-1

Cited by 67 publications

(68 citation statements)

References 54 publications

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“…Their effect can be seen in the mirrorimage between the skill score (blue) and the unconditional biases (red). Thus, the unconditional bias is clearly the primary source of bias across these eight models, as was also found in Bradley et al (2015) and Slater et al (2017). The conditional biases are also irregularly distributed across the different months of the year and lead times, and vary substantially from model to model.…”

Section: The Eight Single-model Ensembles: Low Skill and High Biasessupporting

confidence: 73%

“…Because of the large volumes of data that are produced within the NMME (Table 1), global-scale studies have focused on the evaluation of model skill at specific lead times Mo and Lettenmaier, 2014), or for specific seasons (Wang, 2014), models (Jia et al, 2015;Saha et al, 2014), or climate quantities (Barnston and Lyon, 2016;Mo and Lyon, 2015). Regional evaluations of NMME forecast skill have focused principally on North America (Infanti and Kirtman, 2016), the United States (Misra and Li, 2014;Roundy et al, 2015;Slater et al, 2017), the southeastern United States , but also China (Ma et al, 2015a(Ma et al, , 2015b, Iran (Shirvani and Landman, 2016) and South Asia (Sikder et al, 2015). Thus, most of the effort of the NMME model skill evaluation has been over the USA, and far less attention has been paid to Europe, with some exceptions, such as Thober et al (2015), who used NMME forecasts as input for the mesoscale hydrologic model (mHM).…”

Section: Introductionmentioning

confidence: 99%

“…Existing NMME multi-model approaches have mostly used equal weighting schemes, giving the same weight to each single-model ensemble (i.e., the mean of each model's members) or to all of the individual members, irrespective of their skill Hagedorn et al, 2005;Slater et al, 2017;Tian et al, 2014). The predictive skill of these equally weighted multi-models tends to be greater than or equal to the skill of the best model within the ensemble Hagedorn et al, 2005;Ma et al, 2015a;Slater et al, 2017;Thober et al, 2015;Wood et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The predictive skill of these equally weighted multi-models tends to be greater than or equal to the skill of the best model within the ensemble Hagedorn et al, 2005;Ma et al, 2015a;Slater et al, 2017;Thober et al, 2015;Wood et al, 2015). Generally, multi-model ensembles can outperform single-model ensembles when the individual models are overconfident, so the multi-model widens the ensemble spread and reduces the average ensemble-mean error (Weigel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…If the models are not independent, the multi-model will over-strengthen the forecasts issued by similar models (Olson et al, 2016). This is particularly true in the case of the NMME, where many of the participating models are different versions of similar models, e.g., CCSM3 and CCSM4, CanCM3 and CanCM4, or GFDL2.1 and FLORb01 (Table 1), so the forecasts exhibit notable similarities (e.g., Slater et al, 2017). Another problem is that of reproducing the correct dispersion (Raftery et al, 2005): single-model ensembles are likely to be underdispersive (Arritt and Rummukainen, 2011), as are multi-model ensembles when the models are correlated among themselves.…”

Section: Introductionmentioning

confidence: 99%

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Weighting of NMME temperature and precipitation forecasts across Europe

Slater

Villarini

Bradley

2017

Journal of Hydrology

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Highlights• NMME precipitation and temperature forecast skill are assessed across Europe• The forecasting skill of five weighted multi-models is compared• Equal model weighting preserves forecast skill, but with considerable biases• Bayesian updating reduces conditional biases and homogenizes the skill PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

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Section: The Eight Single-model Ensembles: Low Skill and High Biasessupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weighting of NMME temperature and precipitation forecasts across Europe

Slater

Villarini

Bradley

2017

Journal of Hydrology

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Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

Lehner

Wood

Llewellyn

et al. 2017

Geophysical Research Letters

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Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.

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Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

Narapusetty¹,

Collins²,

Murtugudde

et al. 2018

Atmospheric Science Letters

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Improvements in skill of summer forecasted precipitation as produced by the North American multi-model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2-m air temperature (T2m) forecasts to fully exploit the stronger co-variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real-time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m-precipitation relation will offer a framework for diagnosing poor model skill. K E Y W O R D Sbias correction to improve precipitation skill in NMME seasonal precipitation forecasts, drought skill improvement over CONUS, surface air temperatureprecipitation relationship in seasonal forecasts

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Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA

Cited by 67 publications

References 54 publications

Weighting of NMME temperature and precipitation forecasts across Europe

Weighting of NMME temperature and precipitation forecasts across Europe

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

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