Reassessing statistical downscaling techniques for their robust application under climate change conditions

The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.Thiswork has been funded by the Spanish I1D1i 2008-11 Program: A strategic action for energy and climate change (ESTCENA, code 200800050084078) and the project CGL2010-21869 (EXTREMBLES). S.B. was supported by a JAE PREDOC grant (CSIC, Spain). The authors would like to especially thank the three anonymous reviewers who helped to considerably improve this manuscript

Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods

This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutierrez et al., 2013), the present manuscript deals with precipitation and considers an ensemble of twelve SDMs from the analog, weather typing, and regression (GLM) families. In the first part, we assess the performance of the methods with perfect (reanalysis) predictors, screening different geographical domains and predictor sets. To this aim, standard accuracy and distributional similarity scores, and a test for extrapolation capability based on dry observed historical periods are considered. As in Part I, the results are highly dependent on the predictor sets, with optimum configurations including information of middle tropospheric humidity (in particular Q850). As a result of this analysis, deficient SDMs are discarded in order to properly assess the spread (uncertainty) of future climate projections, avoiding the noise introduced by unsuitable models. In the second part, the resulting ensemble of SDMs is applied to four Global Circulation Models (GCMs) from the ENSEMBLES (CMIP3) project to obtain historical (1961-2000, 20C3M scenario) and future (2001-2100, A1B) regional projections. The obtained results are compared with those produced by an ensemble of Regional Climate Models (RCMs) driven by almost the same GCMs in the ENSEMBLES project. In general, the mean signal is similar with both methodologies (with the exception of Summer, where the RCMs project drier conditions) but the spread is larger for the SDM results. Finally, the contribution of the GCM and SDM-derived components to the total spread is assessed using a simple analysis of variance previously applied to the ENSEMBLES RCM ensemble. Results show that the main contributor to the spread is the choice of the GCM, except for the autumn results in the Atlantic sub-region of Spain and the Autumn and Summer results in the Mediterranean sub-region, where the choice of the SDM dominates the uncertainty during the second half of the 21st century due mainly to the different projections obtained from different families of SDM techniques. The most noticeable difference with the RCMs is the magnitude of the interaction terms, which is larger in all cases in the present study.This work has been funded by the strategic action for energy and climate change by the Spanish R&D 2008–2011 program ‘‘Programa coordinado para la generación de escenarios regionalizados de cambio climático: Regionalización Estadística (esTcena),’’ code 200800050084078, and the project CGL2015-66583-R (MINECO/FEDER). The RCM simulations used in this study were obtained from the European Union–funded FP6 Integrated Project ENSEMBLES (Contract 505539)

Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981–2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal–bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to “delta-change” estimates differing by up to 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of local-scale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of the GCM and/or downscaling method). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed.The authors are grateful to the free distribution of the ECMWF ERA-Interim (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim), JMA JRA-25 (http://jra.kishou.go.jp/JRA-25/index_en.html), and MPI-ECHAM5 data (http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=ENSEMBLES_MPEH5_SRA1B_3_D) and acknowledge PAGASA for the observational data provided. This study was supported by the EU projects QWeCI and SPECS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreements 243964 and 308378, respectively. RM also acknowledges the EU project EUPORIAS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreement 308291. SB is grateful to the CSIC-JAE-Predoc Program for financial support

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware

The weather roulette: a game to communicate the usefulness of probabilistic climate predictions

Climate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between climate scientists and users, the difficulties of dealing with probabilistic outcomes for decision-making, and the lower skill of climate predictions compared to the skill of weather forecasts. This paper presents a gaming approach to break communication and understanding barriers through the application of the Weather Roulette conceptual framework. In the game, the player can choose between two forecast options, one that uses ECMWF seasonal predictions against one using climatology-derived probabilities. For each forecast option, the bet is spread proportionally to the predicted probabilities, either in a single year game or a game for the whole period of 33 past years. This paper provides skill maps of forecast quality metrics commonly used by the climate prediction community (e.g., ignorance skill score and ranked probability skill score), which in the game are linked to metrics easily understood by the business sector (e.g., interest rate and return on investment). In a simplified context, we illustrate how in skillful regions the economic benefits of using ECMWF predictions arise in the long term and are higher than using climatology. This paper provides an example of how to convey the usefulness of climate predictions and transfer the knowledge from climate science to potential users. If applied, this approach could provide the basis for a better integration of knowledge about climate anomalies into operational and managerial processes.The research leading to these results has received funding from the EU FP7 Programme under Grant Agreement 308291 (EUPORIAS), the EU H2020 Programme under Grant Agreements 776787 (S2S4E) and 776613 (EUCP), and the Ministerio de Economía y Competitividad (MINECO) as part of the CLINSA Project CGL2017-85791-R. It is also part of the Copernicus Climate Change Service (C3S) (Framework Agreement C3S_441_Lot2_CEA), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission

Impact of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients: A nationwide study in Spain

Objective To assess the effect of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients in Spain. Settings The initial flood of COVID-19 patients overwhelmed an unprepared healthcare system. Different measures were taken to deal with this overburden. The effect of these measures on neurosurgical patients, as well as the effect of COVID-19 itself, has not been thoroughly studied. Participants This was a multicentre, nationwide, observational retrospective study of patients who underwent any neurosurgical operation from March to July 2020. Interventions An exploratory factorial analysis was performed to select the most relevant variables of the sample. Primary and secondary outcome measures Univariate and multivariate analyses were performed to identify independent predictors of mortality and postoperative SARS-CoV-2 infection. Results Sixteen hospitals registered 1677 operated patients. The overall mortality was 6.4%, and 2.9% (44 patients) suffered a perioperative SARS-CoV-2 infection. Of those infections, 24 were diagnosed postoperatively. Age (OR 1.05), perioperative SARS-CoV-2 infection (OR 4.7), community COVID-19 incidence (cases/10 5 people/week) (OR 1.006), postoperative neurological worsening (OR 5.9), postoperative need for airway support (OR 5.38), ASA grade =3 (OR 2.5) and preoperative GCS 3-8 (OR 2.82) were independently associated with mortality. For SARS-CoV-2 postoperative infection, screening swab test <72 hours preoperatively (OR 0.76), community COVID-19 incidence (cases/10 5 people/week) (OR 1.011), preoperative cognitive impairment (OR 2.784), postoperative sepsis (OR 3.807) and an absence of postoperative complications (OR 0.188) were independently associated. Conclusions Perioperative SARS-CoV-2 infection in neurosurgical patients was associated with an increase in mortality by almost fivefold. Community COVID-19 incidence (cases/10 5 people/week) was a statistically independent predictor of mortality. Trial registration number CEIM 20/217

All-cause mortality in the cohorts of the Spanish AIDS Research Network (RIS) compared with the general population: 1997Ł2010

Abstract Background: Combination antiretroviral therapy (cART) has produced significant changes in mortality of HIVinfected persons. Our objective was to estimate mortality rates, standardized mortality ratios and excess mortality rates of cohorts of the AIDS Research Network (RIS) (CoRIS-MD and CoRIS) compared to the general population. Methods: We analysed data of CoRIS-MD and CoRIS cohorts from 1997 to 2010. We calculated: (i) all-cause mortality rates, (ii) standardized mortality ratio (SMR) and (iii) excess mortality rates for both cohort for 100 personyears (py) of follow-up, comparing all-cause mortality with that of the general population of similar age and gender. Results: Between 1997 and 2010, 8,214 HIV positive subjects were included, 2,453 (29.9%) in CoRIS-MD and 5,761 (70.1%) in CoRIS and 294 deaths were registered. All-cause mortality rate was 1.02 (95% CI 0.91-1.15) per 100 py, SMR was 6.8 (95% CI 5.9-7.9) and excess mortality rate was 0.8 (95% CI 0.7-0.9) per 100 py. Mortality was higher in patients with AIDS, hepatitis C virus (HCV) co-infection, and those from CoRIS-MD cohort (1997. Conclusion: Mortality among HIV-positive persons remains higher than that of the general population of similar age and sex, with significant differences depending on the history of AIDS or HCV coinfection

The METACLIP semantic provenance framework for climate products

Having an effective way of dealing with data provenance is a necessary condition to ensure reproducibility, helping to build trust and credibility in research outcomes and the data products delivered. METACLIP (METAdata for CLImate Products) is a language-independent framework envisaged to tackle the problem of climate product provenance description. The solution is based on semantics exploiting the web standard Resource Description Framework (RDF), building on domain-specific extensions of standard vocabularies (e.g., PROV-O) describing the different aspects involved in climate product generation. We illustrate METACLIP through an example application within the open source R computing environment, generating a climate product for which full provenance information is recorded. Finally, the METACLIP Interpreter, a web-based interactive front-end for metadata visualization is presented, helping a diversity of users with different levels of expertise to trace and understand the provenance of a wide variety of climate data products, and to fully reproduce them.We are grateful to the three anonymous reviewers and to the Editor for their insightful comments, helping to improve the original manuscript. Our colleague Prof. Antonio Cofiño (University of Cantabria) has helped with valuable comments to the development of the METACLIP framework. Dr. Jonas Bhend (MeteoSwiss) has contributed to the design of the vocabulary for seasonal forecast verification. This work has been partially funded by the European Union’s Earth Observation Programme COPERNICUS, through the ITT C3S-51-Lot3 (QA4Seas Project, “Quality Assurance for Seasonal Forecast Products”), the Spain's Research and Innovation Programme under project INSIGNIA (co-funded by MINECO and FEDER, grant no. CGL2016-79210-R) and the European Union Research and Innovation Programme under projects IS-ENES2 (FP7 grant agreement no. 312979) and IS-ENES3 (H2020 grant agreement no. 824084