Recalibrating Decadal Climate Predictions – What is an adequate model for the drift?

Pasternack, Alexander; Grieger, Jens; Rust, Henning W.; Ulbrich, Uwe

doi:10.5194/gmd-2020-191

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…However, this is just one of many possible options that could be considered. A more efficient strategy for model selection based on boosting [36] is currently under investigation for this use case.…”

Section: Model Selectionmentioning

confidence: 99%

Estimating IDF Curves Consistently over Durations with Spatial Covariates

Ulrich

Jurado

Peter

et al. 2020

Water

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Given that long time series for temporally highly resolved precipitation observations are rarely available, it is necessary to pool information to obtain reliable estimates of the distribution of extreme precipitation, especially for short durations. In this study, we use a duration-dependent generalized extreme value distribution (d-GEV) with orthogonal polynomials of longitude and latitude as spatial covariates, allowing us to pool information between durations and stations. We determine the polynomial orders with step-wise forward regression and cross-validated likelihood as a model selection criterion. The Wupper River catchment in the West of Germany serves as a case study area. It allows us to estimate return level maps for arbitrary durations, as well as intensity-duration-frequency curves at any location—also ungauged—in the research area. The main focus of the study is evaluating the model performance in detail using the Quantile Skill Index, a measure derived from the popular Quantile Skill Score. We find that the d-GEV with spatial covariates is an improvement for the modeling of rare events. However, the model shows limitations concerning the modeling of short durations d≤30min. For ungauged sites, the model performs on average as good as a generalized extreme value distribution with parameters estimated individually at the gauged stations with observation time series of 30–35 years available.

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Section: Model Selectionmentioning

confidence: 99%

Estimating IDF Curves Consistently over Durations with Spatial Covariates

Ulrich

Jurado

Peter

et al. 2020

Water

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“…Meanwhile, improvements in trends effectively adjusted the long-term climate behavior in forecasts to match observations (Kharin et al, 2012). To correct errors associated with the representation of temporal changes and variability, Pasternack et al (2020) adopted a time-varying mean to characterize the climate trend in the calibration of decadal temperature forecasts. In addition to these decadal-scale calibrations, recent studies suggested that seasonal climate forecasting could also benefit from correcting time-dependent errors.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Yang

Wang

Western

et al. 2021

Preprint

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Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from General Circulation Models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in ETo forecasts. However, time-dependent errors, resulting from GCM’s misrepresentations of climate trends, have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian Joint Probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 % in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modelling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasts, and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

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Recalibrating decadal climate predictions – what is an adequate model for the drift?

Pasternack¹,

Grieger²,

Rust³

et al. 2021

Geosci. Model Dev.

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Abstract. Near-term climate predictions such as multi-year to decadal forecasts are increasingly being used to guide adaptation measures and building of resilience. To ensure the utility of multi-member probabilistic predictions, inherent systematic errors of the prediction system must be corrected or at least reduced. In this context, decadal climate predictions have further characteristic features, such as the long-term horizon, the lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typical pairs of hindcasts and observations are available to estimate calibration parameters. With DeFoReSt (Decadal Climate Forecast Recalibration Strategy), Pasternack et al. (2018) proposed a parametric post-processing approach to tackle these problems. The original approach of DeFoReSt assumes third-order polynomials in lead time to capture conditional and unconditional biases, second order for dispersion and first order for start time dependency. In this study, we propose not to restrict orders a priori but use a systematic model selection strategy to obtain model orders from the data based on non-homogeneous boosting. The introduced boosted recalibration estimates the coefficients of the statistical model, while the most relevant predictors are selected automatically by keeping the coefficients of the less important predictors to zero. Through toy model simulations with differently constructed systematic errors, we show the advantages of boosted recalibration over DeFoReSt. Finally, we apply boosted recalibration and DeFoReSt to decadal surface temperature forecasts from the German initiative Mittelfristige Klimaprognosen (MiKlip) prototype system. We show that boosted recalibration performs equally as well as DeFoReSt and yet offers a greater flexibility.

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Recalibrating Decadal Climate Predictions – What is an adequate model for the drift?

Cited by 3 publications

References 32 publications

Estimating IDF Curves Consistently over Durations with Spatial Covariates

Estimating IDF Curves Consistently over Durations with Spatial Covariates

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Recalibrating decadal climate predictions – what is an adequate model for the drift?

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