Temperature trends and prediction skill in NMME seasonal forecasts

Krakauer, Nir Y.

doi:10.1007/s00382-017-3657-2

Cited by 22 publications

(11 citation statements)

References 55 publications

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“…While the CFSv1 and the two IRI models all used the same older ocean model, MOM3, the Canadian Seasonal to Interannual Prediction System models introduced different atmosphere and ocean models. Gains in temperature‐related skill may also have stemmed from the inclusion of newer models with a more accurate representation of the global warming trend over land (e.g., Krakauer, 2019).…”

Section: Discussionmentioning

confidence: 99%

Evolution of the North American Multi‐Model Ensemble

Becker

Kirtman

Pegion

2020

Geophysical Research Letters

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The North American Multi-Model Ensemble (NMME) has become integral scientific infrastructure in subseasonal and seasonal climate prediction, advancing applied prediction and scientific understanding of the mechanisms and predictability of short-term climate. The NMME was designed to evolve as old models were retired and new models joined the ensemble. This study examines the assumption that prediction skill will increase as the system evolves, focusing on 2 m temperature, precipitation rate, and sea surface temperature prediction. The common period of 1982-2010 is studied for four configurations of the NMME, approximately representing the operational model suites of 2011, 2012, 2014-2018, and 2019-present. Substantial improvement in temperature prediction over both land and ocean is observed, with little change in global precipitation prediction. Sea surface temperature prediction at longer leads has improved over much of the globe, with the notable exception of the central-eastern tropical Pacific, where prediction skill has declined.Plain Language Summary Predicting the climate more than a few weeks in advance requires forecasters to bet on more than one computer model, similar to the method of diversifying an investment portfolio. Many earlier studies have shown that the combination of predictions generated by different computer models, known as a multi-model ensemble, almost always achieves better forecast skill than using a single model alone. The North American Multi-Model Ensemble (NMME), a subseasonal and seasonal prediction system combining individual North American state-of-the-art climate prediction models, has become an integral part of subseasonal and seasonal research and applications. The NMME has continually evolved, as newer models replace older ones; it is assumed that this evolution will produce more skillful predictions over time. But, until now, this assumption has not been tested. We examine the skill of NMME predictions in four different model combinations, including the oldest configuration, two transitional suites, and the operational configuration as of early 2020. Temperature prediction over both land and ocean has improved noticeably through the exchange of older models for newer ones, but precipitation prediction has not substantially improved. Overcoming the difficulty in precipitation prediction may require higher-resolution climate models in the NMME.

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Section: Discussionmentioning

confidence: 99%

Evolution of the North American Multi‐Model Ensemble

Becker

Kirtman

Pegion

2020

Geophysical Research Letters

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“…We use eight NMME GCMs with a total of 94 model members (CanCM3, CanCM4, CCSM3, CCSM4, CFSv2, GFDL2.1, FLORb01, and GEOS5; see Table S1 for details). We compute the catchment ensemble forecast as the mean of the available members (for every site, month, and lead time) because the multimodel mean forecast tends to outperform any single model (Becker et al, 2014;Krakauer, 2017;. We compute the catchment ensemble forecast as the mean of the available members (for every site, month, and lead time) because the multimodel mean forecast tends to outperform any single model (Becker et al, 2014;Krakauer, 2017;.…”

Section: Seasonal Precipitation and Temperature Nmme Forecastsmentioning

confidence: 99%

“…Catchment-averaged time series of precipitation and temperature outputs are computed for every site, month, and lead time, for each of the 94 NMME members (Kirtman et al, 2014). We compute the catchment ensemble forecast as the mean of the available members (for every site, month, and lead time) because the multimodel mean forecast tends to outperform any single model (Becker et al, 2014;Krakauer, 2017;. The seasonal precipitation and temperature forecasts are computed by aggregating monthly forecasts for every initialization time (e.g., the spring forecast issued in March is the sum of the 0.5-lead forecast for March, the 1.5-lead forecast for April, and the 2.5-lead forecast for May; Figure S1).…”

Section: Seasonal Precipitation and Temperature Nmme Forecastsmentioning

confidence: 99%

Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

Slater

Villarini

2018

Geophysical Research Letters

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Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large‐scale climate indices as predictors in statistical models. In contrast, hybrid statistical‐dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon, and their skill is largely unknown. Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.

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“…For example, Jia and Lin (2013) showed seasonal forecasts from the second phase of the Canadian Historical Forecasting Project (HFP2) considerably underestimated the significant trend of the surface air temperature in winter on the Eurasian continent. Similarly, Krakauer (2017) noted the warming trend in monthly mean temperature forecasts from the North American Multi‐Model Ensemble (NMME) model was weaker than the observed trend.…”

Section: Introductionmentioning

confidence: 98%

Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs

Shao

Wang

Schepen

et al. 2020

Intl Journal of Climatology

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Accurate and reliable seasonal climate forecasts are frequently sought by climate-sensitive sectors to support decision-making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP-t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP-t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP-t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration.

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Temperature trends and prediction skill in NMME seasonal forecasts

Cited by 22 publications

References 55 publications

Evolution of the North American Multi‐Model Ensemble

Evolution of the North American Multi‐Model Ensemble

Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs

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