Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change

Shao, Yawen; Wang, QJ; Schepen, Andrew; Ryu, Dongryeol

doi:10.1175/mwr-d-20-0318.1

Cited by 11 publications

(21 citation statements)

References 37 publications

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“…Although these models are proven to be effective in correcting biases in raw forecasts, assuming a static climatology may have hindered the utilization of predictable information in the raw forecasts. This investigation and our previous calibration of seasonal temperature forecasts (Shao et al, 2020(Shao et al, , 2021, suggest that reconstructing trends in calibrated forecasts is an effective solution for capturing the non-stationary behavior of the climate system for more robust statistical calibrations of seasonal climate forecasts.…”

Section: Implications For Improving Statistical Calibration Modelsmentioning

confidence: 71%

“…In the BJP-ti model, informative priors are applied to set boundaries for inferred trends to avoid extreme values for each grid cell, month, and lead time. This informative prior distribution ( ) for trend parameters and o is formulated as follows (Shao et al, 2021):…”

Section: Forecast Calibration With the Bjp-ti Modelmentioning

confidence: 99%

“…In addition to these decadal-scale calibrations, recent studies suggested that seasonal climate forecasting could also benefit from correcting time-dependent errors. For example, Shao et al (2021) improved the BJP model by adding trendreconstruction algorithms to deal with time-dependent errors. The new algorithm allows for the reconstruction of observed trends in calibrated forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…In the Middle East, seasonal temperature forecasts by the Climate Forecast System version 2 (CFSv2) model overestimated the warming trend in reference data by approximately 0.4° decade -1 (Alizadeh- Choobari et al, 2019). In Australia, evaluations of the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model demonstrated significant discrepancies between observed and forecasted trends in temperature (Shao et al, 2020(Shao et al, , 2021.Forecasts of fire weather index (calculated with forecasts of precipitation, wind speed, temperature, and humidity) based on the ECMWF System 4 model demonstrated significant inconsistencies with observations in temporal trends in Europe during 1981-2010(Bedia et al, 2018. As a result, it is unlikely that raw ETo forecasts constructed with raw forecasts of these climate variables would faithfully reproduce the observed climate trends.…”

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confidence: 99%

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confidence: 99%

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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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Section: Implications For Improving Statistical Calibration Modelsmentioning

confidence: 71%

Section: Forecast Calibration With the Bjp-ti Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Yang

Wang

Western

et al. 2021

Preprint

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Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing

Shao

Wang

Schepen

et al. 2022

Intl Journal of Climatology

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Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend-aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30-year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30-year trends is to robustly represent long-term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20-year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day-of-year climatology, raw week-1 forecasts reproduce temperature trends of the 20-year observations in many regions while raw week-4 forecasts do not exhibit the 20-year observed trends. After trendaware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week-1 forecasts show apparent trends consistent with the 20-year observations, as the calibration transfers forecast skill and embeds the 20-year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week-4 forecasts exhibit the 30-year observed trends, as the calibration reverts the forecasts to the 30-year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend-aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend-aware method can be applied to deliver high-quality subseasonal forecasts and support decisionmaking in a changing climate.

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Music Industry Trend Forecasting Based on MusicBrainz Metadata

Kopel¹,

Kreisich²

2022

Intelligent Information and Database Systems

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Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change

Cited by 11 publications

References 37 publications

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing

Music Industry Trend Forecasting Based on MusicBrainz Metadata

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