ORCID Profile
0000-0002-6622-4280
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: 26-05-2023
DOI: 10.5194/EGUSPHERE-2023-962
Abstract: Abstract. Previous studies agree on an impact of the Atlantic Multidecadal Variability (AMV) on total seasonal rainfall amounts over the Sahel. However, whether and how AMV affects the distribution of rainfall or the timing of the West African Monsoon is not well known. Here we analyze daily rainfall outputs from atmosphere-ocean coupled models. Models show dry biases over the Sahel, where the mean intensity is consistently smaller than observations, and wet biases over the Guinea Coast, where they simulate too many rainy days. In addition, most models underestimate the average length of the rainy season over the Sahel, some due to a too late monsoon onset and others due to a too early cessation. In response to a persistent positive AMV pattern imposed in the Atlantic, following a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C), models show an enhancement in total summer rainfall over West African land mass, including the Sahel. Both the number of wet days and the intensity of daily rainfall events are enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event: the frequency of both moderate and heavy events increases over the Sahel’s northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Models with stronger negative biases in rainfall amounts tend to show weaker changes in response to AMV, suggesting systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed and the overall length of the monsoon season enhanced between 2 and 5 days with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the West of 10º W, with all models showing a statistically significant earlier onset, later demise and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.
Publisher: Copernicus GmbH
Date: 22-09-2023
Publisher: Copernicus GmbH
Date: 11-02-2021
DOI: 10.5194/GMD-2020-446
Abstract: Abstract. The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behaviour and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Publisher: Elsevier BV
Date: 2014
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: Elsevier BV
Date: 09-2015
Publisher: Copernicus GmbH
Date: 08-04-2022
Abstract: Abstract. The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Publisher: American Geophysical Union (AGU)
Date: 11-12-2021
DOI: 10.1029/2021GL094915
Abstract: Targeted adaptation to near‐term climate change requires accurate, reliable, and actionable climate information for the next few decades. Climate projections simulate the response to radiative forcing, but are subject to substantial uncertainties due to internal variability. Decadal climate predictions aim to reduce this uncertainty by initializing the simulations using observations, but are typically limited to the next 10 years. Here, we use decadal predictions to constrain climate projections beyond the next decade and demonstrate that accounting for climate variability improves regional projections of 20‐year average temperatures. Applying this constraint to climate projections of the near future until 2035, summer temperatures over land regions in Asia and Africa tend to show stronger changes within the warming range simulated by the larger, unconstrained, ensemble—consistent with a warm phase in North Atlantic variability. This improved regional climate information can enable tailored adaptation to climate changes in the coming decades.
Publisher: Copernicus GmbH
Date: 11-04-2022
DOI: 10.5194/EGUSPHERE-2022-98
Abstract: Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales. We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the ex le of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
Publisher: Springer Science and Business Media LLC
Date: 13-04-2021
Publisher: Copernicus GmbH
Date: 11-04-2022
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7894
Abstract: The Mediterranean region is often considered a climate change hotspot. State of the art climate projections display a large uncertainty due to factors such as the modeling approach or variability phasing, in particular for projections of the next few decades. With the great amount of climate information from the last Coupled Model Intercomparison Project (CMIP6), it is desirable to constrain the multi-model multi-member ensemble for the near-term future to obtain more robust and better performing projections. We explore different subsetting methods that select those members that better capture the variability at the starting date of the projection& #8217 s study period. To find the best information we explore variations of different parameters in the selection method based on relevant climate drivers in the Mediterranean region. Such parameters are the reference against which the best members are selected, and comprise: the time period used, the constraining regions considered and the variable or metric that drive the constraint. We find that these subsetting methods can improve the accuracy of near-term climate projections for the next 20 years compared to the unconstrained projections. This evaluation of the results allows us to make informed and robust decisions about the near-term future projections based on quality estimates borrowed from climate prediction practices.
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: 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.
Publisher: Copernicus GmbH
Date: 11-02-2021
Abstract: Abstract. In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and h er the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/DSJ-2021-019
Publisher: Springer Science and Business Media LLC
Date: 05-12-2012
Publisher: Copernicus GmbH
Date: 19-10-2022
Abstract: Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the ex le of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
No related grants have been discovered for Francisco Doblas-Reyes.