ORCID Profile
0000-0002-5919-6701
Current Organisation
Barcelona Supercomputing Center
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Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-15594
Abstract: Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).& While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent & #8220 bridging& #8221 between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work.& Here, we propose and explore a new methodology & #8211 that we call the Xit (& #8220 cross-it& #8221 ) operator& #8211 based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to & #8220 bridge& #8221 forecasts at different timescales in a smooth and physically-consistent manner.& In summary, the Xit& operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which & #8220 intrinsic& #8221 periods/scales (i) in the timescale continuum (t) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times& #8212 i.e., potential windows of opportunity (Mariotti et al. 2020)& #8212 in the forecast period where wavelet entropy (uncertainty) is lower.& While the first component of the Xit& operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the Xit operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13156
Abstract: & & Decadal climate predictions are a new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and lead times) with skill that can be used to develop a climate service and inform users in several sectors. Also, it can help to monitor improvements in current forecast systems. The Decadal Climate Prediction Project Component A (DCPP-A) of the Coupled Model Intercom-parison Project Phase 6 (CMIP6) now provides the most comprehensive set of retrospective decadal predictions from multiple forecast systems. The increasing availability of these simulations leads to the question of how to best post-process the raw output from the forecast systems so that the most useful and reliable information is provided to users.& & & & This work evaluates the quality of deterministic and probabilistic forecasts for spatial fields of near-surface air temperature and precipitation, and time series of the Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available decadal predictions contributing to CMIP6/DCPP-A (169 members from 13 forecast systems). The predictions generally show high skill in predicting temperature and the AMV and GSAT time series, while the skill is more limited for precipitation. Also, different approaches for building a multi-model forecast are compared (pooling all ensemble members versus combining the averages from in idual forecast systems), finding small differences. Besides, the multi-model ensemble is compared to the in idual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the uninitialized historical climate simulations (195 members from the same forecast systems as the decadal prediction members) to estimate the impact of initialization. An added value is found for temperature over several ocean and land regions, and for the AMV and GSAT time series, while it is more reduced for precipitation. Moreover, the full DCPP-A ensemble is compared to a sub-ensemble of predictions that could be provided in near real-time for a potential operational product generation. The comparison shows a benefit of using a large ensemble over several regions, especially for temperature. Finally, the implications of these results in a climate services context are discussed.& & & & & & / &
Publisher: Elsevier BV
Date: 09-2023
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13357
Abstract: & & To strengthen the seasonal forecast adoption on the vine management, this work introduces an observation-forecast blending approach to wine-sector indicators over the Iberian Peninsula. Five bioclimatic indicators (temperature and precipitation based) were considered as highly important from the perspective of wine-sector users, and the predictions were prepared for each month of the growing season by combining with previous observations as the indicator period progressed. The performance of this approach was then assessed, for each initialization date throughout the entire growing season, with Pearson& #8217 s correlation coefficient and Fair Ranked Probability Skill Score. The results show a marked increase in the skill metrics during the growing season from the early combinations for all the indicators. This progressive improvement of the forecasting skill provides the users with an opportunity to ponder anticipation and confidence when finding the best moment to make a specific decision and, thus, to improve their management. Meanwhile, the environmental impact could be reduced with the thoughtful application derived from the customised knowledge of climate information& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2399
Abstract: Both global warming and internal climate variability modulate changes in the intensity and frequency of extreme climate events. Anticipating such variations years in advance may help minimise the impact on climate-vulnerable sectors and society, as well as enable short-term adaptation strategies and early-warning systems in a changing climate. Decadal climate predictions are a source of climate information for multi-annual timescales. They are provided by climate forecast systems similar to the models used for long-term climate projections but that have been initialised with the best estimate of the contemporaneous conditions of the climate system. However, before using the predictions, the forecast quality should be assessed. This is an essential step to evaluate the accuracy of the predictions and find windows of opportunity (variables/indices, regions and forecast times) to provide climate services with data of sufficient quality to satisfy the user requirements.We evaluate the deterministic and probabilistic forecast quality of the multi-model ensemble built with all the available decadal hindcasts (i.e., retrospective decadal predictions) contributing to CMIP6, which consists of a total of 133 ensemble members from 13 forecast systems. The forecast quality assessment has been performed for predictions of seasonal and annual indices of daily temperature and precipitation extremes for the forecast years 1-5. These indices measure the intensity and frequency of hot and cold temperature extremes, and the intensity and rainfall accumulation related to heavy precipitation extremes. The prediction skill for the temperature and precipitation extreme indices is further compared to the skill for mean temperature and precipitation, respectively. In order to assess the impact of the model initialisation, the predictions are compared against historical forcing simulations (i.e., retrospective climate projections) created with the same models, consisting of a total of 134 ensemble members from the same forecast systems as the decadal hindcasts.We find that the decadal hindcasts skillfully predict both mean and extreme temperature indices over most of the globe for multi-annual periods. The forecast quality for mean precipitation and extreme precipitation indices is generally low, and significant skill is found only over some limited regions. The reduced quality of the precipitation predictions with respect to temperature is due to the relatively smaller effect of human-induced warming for this variable. The comparison between the skill for mean variables and extreme indices shows that the extreme indices are generally predicted with lower skill, especially those related to the intensity of extreme events. We find generally small and region-dependent improvements from model initialisation compared to historical forcing simulations. The added value due to initialisation is higher for the mean variables than for the extreme indices. Besides, such skill differences differ between indices, especially those representing extreme temperature. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed about the trustworthiness of the forecasts for each specific region and extreme event. Also, comparing decadal hindcast and historical simulations may help climate services providers select the highest-quality information from these different data sources.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-11143
Abstract: The forecast quality of multi-model seasonal-to-decadal climate predictions, as measured by metrics of, among others, accuracy and reliability, has been traditionally estimated considering time-average products and products for event thresholds that do not target the occurrence of unusual events of either monthly or seasonal duration. However, there is an increasing interest in some user communities for products that represent extreme and unusual events. This presentation will discuss the differences in forecast quality between traditional forecast products, like mean seasonal temperature, and products for intraseasonal extremes (e.g., those measured with the 95th percentile of high-frequency temperature over periods like a month or a season) and monthly and seasonal unusual events (such as the frequency of exceeding the 90th percentile of the daily climatological distribution of temperature at a given time of the year). The results will be discussed in the context of their implications to address a number of user requirements from different sectors. The relevance of the forecast quality estimated from hindcast sets and the role of the observational uncertainty will be discussed when delivering forecast products in a climate service context. The implications of this work for the standardisation of climate services based on climate predictions will also be discussed.
No related grants have been discovered for Nube Gonzalez-Reviriego.