Statistical adjustment of decadal predictions in a changing climate

Kharin, Viatcheslav; Boer, G. J.; Merryfield, William J.; Scinocca, John; Lee, W.-S.

doi:10.1029/2012gl052647

Cited by 107 publications

(112 citation statements)

References 11 publications

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“…This in itself may reduce conditional bias. As the prediction evolves, however, its response to increasing greenhouse gasses may tend toward that of the uninitialized hindcasts over the course of an adjustment period during which the influence of the initial conditions is ''forgotten'' (Kharin et al 2012). For both sets of hindcasts the correlation with the observations would be 1.0 at all lead times, but they are not accurate because the model has a bias in the magnitude of its response to the forcing.…”

Section: Deterministic Metricsmentioning

confidence: 99%

A verification framework for interannual-to-decadal predictions experiments

Goddard¹,

et al. 2012

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Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic

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Section: Deterministic Metricsmentioning

confidence: 99%

A verification framework for interannual-to-decadal predictions experiments

Goddard¹,

et al. 2012

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“…In this case a bias correction that depends on the forecast start date may be necessary (e.g. Kharin et al, 2012;Fučkar et al, 2014). The most appropriate way to achieve this, especially for regional predictions, is a research question, and several methods could be considered (e.g.…”

Section: Trendsmentioning

confidence: 99%

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

et al. 2016

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Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al

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“…Each experiment is carried out for 10 yr and has four ensemble members. In all of the CFS experiments, the ocean and the atmosphere components are 'full-field' (Kharin et al, 2012;Goddard et al, 2013) initialised with CFS reanalysis data (Saha et al, 2013). The observed SSTs, used for verification are obtained from extended reconstructed data as discussed by Smith et al (2008).…”

Section: Methodsmentioning

confidence: 99%

“…Several evaluation metrics were provided in the current literature along with suggestions to correct the model drift from the observed state (e.g. Corti et al, 2012;Kharin et al, 2012;Goddard et al, 2013). This is an important step in understanding the current models' ability to capture the observed decadal variability.…”

Section: Introductionmentioning

confidence: 99%

Bias correction methods for decadal sea-surface temperature forecasts

Narapusetty

Stan

Kumar

2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Two traditional bias correction techniques:(1) systematic mean correction (SMC) and (2) systematic leastsquares correction (SLC) are extended and applied on sea-surface temperature (SST) decadal forecasts in the North Pacific produced by Climate Forecast System version 2 (CFSv2) to reduce large systematic biases. The bias-corrected forecast anomalies exhibit reduced root-mean-square errors and also significantly improve the anomaly correlations with observations. The spatial pattern of the SST anomalies associated with the Pacific area average (PAA) index (spatial average of SST anomalies over 208Á608N and 1208EÁ1008W) is improved after employing the bias correction methods, particularly SMC. Reliability diagrams show that the bias-corrected forecasts better reproduce the cold and warm events well beyond the 5-yr lead-times over the 10 forecasted years. The comparison between both correction methods indicates that: (1) prediction skill of SST anomalies associated with the PAA index is improved by SMC with respect to SLC and (2) SMC-derived forecasts have a slightly higher reliability than those corrected by SLC.

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Statistical adjustment of decadal predictions in a changing climate

Cited by 107 publications

References 11 publications

A verification framework for interannual-to-decadal predictions experiments

A verification framework for interannual-to-decadal predictions experiments

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

Bias correction methods for decadal sea-surface temperature forecasts

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