A causal flow approach for the evaluation of global climate models

© 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.© 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability

Audio fingerprint parameterization for multimedia advertising identification

This article follows step by step a general framework for fingerprint extraction in order to develop a system for advertisements' monitoring. The parameterization process uses some spatial and spectral characteristics measured over 600 advertisements that contain various types of sounds. Key factors such as accuracy, process time, and granularity are analyzed together in order to enhance the system performance. At the end, the algorithm shows an accuracy of 99% using three seconds of granularity samples, and also the best compromise between processing time and performance is achieved. This study suggests a set of parameterization steps which could be successfully implemented in other related audio applications. © 2017 IEEE.This article follows step by step a general framework for fingerprint extraction in order to develop a system for advertisements' monitoring. The parameterization process uses some spatial and spectral characteristics measured over 600 advertisements that contain various types of sounds. Key factors such as accuracy, process time, and granularity are analyzed together in order to enhance the system performance. At the end, the algorithm shows an accuracy of 99% using three seconds of granularity samples, and also the best compromise between processing time and performance is achieved. This study suggests a set of parameterization steps which could be successfully implemented in other related audio applications. © 2017 IEEE.SALINA

Finding teleconnections from decomposed rainfall signals using dynamic harmonic regressions: a tropical andean case study

Global climate is a multi-scale system whose subsystems interact complexly. Notably, the Tropical-Andean region has a strong rainfall variability because of the confluence of many global climate processes altered by morphological features. An approach for a synthetical climate description is the use of global indicators and their regional teleconnections. However, typically this is carried out using filters and correlations, which results in seasonal and inter-annual teleconnections information, which are difficult to integrate into a modeling framework. A new methodology, based on rainfall signal extraction using dynamic-harmonic-regressions (DHR) and stochastic-multiple-linear-regressions (SMLR) between rainfall components and global signals for searching intra-annual and inter-annual teleconnections, is proposed. DHR gives non-stationary inter-annual trends and intra-annual quasi-periodic oscillations for monthly rainfall measurements. Time-variable amplitudes of quasi-periodical oscillations are crucial for finding intra-annual teleconnections using SMLR, while trends are better suited for the case of inter-annual ones. The methodology is tested over a Tropical-Andean region in southern Ecuador. The following results were obtained: (1) trans-Niño-Index (TNI) and Tropical-South-Atlantic signals are strongly connected to inter-annual and intra-annual time-scales. (2) However, TNI progressively weakens its relation with intra-annual components; meanwhile, El-Niño-Southern-Oscillation 3 gains ground for such time-scales. (3) Finally, an inter-annual connection with the North-Atlantic-Oscillation (NAO) is revealed. These results are consistent with previous literature, although the TNI and NAO connections are interesting findings, taking into account the differences in the connected scales. These results show the methodology’s capability of unraveling global teleconnections in different space and time scales using attributes embedded in an integral mathematical framework, which could be interesting for other purposes—such as the analysis of climate mechanisms or climate modeling. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Global climate is a multi-scale system whose subsystems interact complexly. Notably, the Tropical-Andean region has a strong rainfall variability because of the confluence of many global climate processes altered by morphological features. An approach for a synthetical climate description is the use of global indicators and their regional teleconnections. However, typically this is carried out using filters and correlations, which results in seasonal and inter-annual teleconnections information, which are difficult to integrate into a modeling framework. A new methodology, based on rainfall signal extraction using dynamic-harmonic-regressions (DHR) and stochastic-multiple-linear-regressions (SMLR) between rainfall components and global signals for searching intra-annual and inter-annual teleconnections, is proposed. DHR gives non-stationary inter-annual trends and intra-annual quasi-periodic oscillations for monthly rainfall measurements. Time-variable amplitudes of quasi-periodical oscillations are crucial for finding intra-annual teleconnections using SMLR, while trends are better suited for the case of inter-annual ones. The methodology is tested over a Tropical-Andean region in southern Ecuador. The following results were obtained: (1) trans-Niño-Index (TNI) and Tropical-South-Atlantic signals are strongly connected to inter-annual and intra-annual time-scales. (2) However, TNI progressively weakens its relation with intra-annual components; meanwhile, El-Niño-Southern-Oscillation 3 gains ground for such time-scales. (3) Finally, an inter-annual connection with the North-Atlantic-Oscillation (NAO) is revealed. These results are consistent with previous literature, although the TNI and NAO connections are interesting findings, taking into account the differences in the connected scales. These results show the methodology’s capability of unraveling global teleconnections in different space and time scales using attributes embedded in an integral mathematical framework, which could be interesting for other purposes—such as the analysis of climate mechanisms or climate modeling. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

Extreme rainfall variations under climate change scenarios. Case of study in an andean tropical river basin

Maximum rainfall events have triggered hazards that harm ecosystems and populations. Climate change could modify these extreme events, becoming more severe and frequent. Knowing the patterns of Spatio-temporal changes in the distribution of extreme rainfall in Andean regions represents a research challenge due to the complex climate behavior in the tropical mountain basins. The study aimed to analyze future Spatio-temporal changes in maximum daily rainfall patterns. The methods and analysis were performed in the Paute river basin in Ecuador through observed and simulated data from 1985 to 2005. The outputs of an ensemble regional climate model of Ecuador (RCM) based on CMIP5 models were used with two representative concentrations pathways (RCP), scenarios 4.5 and 8.5, in two future periods; future 1 from 2011 to 2040 and future 2 from 2041 to 2070. The General Extreme Value (GEV) distribution was used to fit the maximum annual daily rainfall. The maximum rainfall change factor between historical and future periods was calculated for 5,10,30, 60, and 100 years return periods. The results showed an increment of maximum rainfall spatial average in all return periods for RCP 4.5 and 8.5 in the future 1. Future 2 presented an increment of maximum rainfall spatial average in all return periods for RCP 4.5 and 8.5 scenarios except for the 30,60 and 100 years return periods of the RCP 4.5 scenario, displaying a decrease of maximum rainfall spatial average. Knowing rainfall pattern projections could help formulate actions to diminish the risks of extreme rainfall

Wavelet analyses of neural networks based river discharge decomposition

The problem of discharge forecasting using precipitation as input is still very active in Hydrology, and has a plethora of approaches to its solution. But, when the objective is to simulate discharge values without considering the phenomenology behind the processes involved, Artificial Neural Networks, ANN give good results. However, the question of how the black box internally solve this problem remains open. In this research, the classical rainfall‐runoff problem is approached considering that the total discharge is a sum of components of the hydrological system, which from the ANN perspective is translated to the sum of three signals related to the fast, middle and slow flow. Thus, the present study has two aims (a) to study the time‐frequency representation of discharge by an ANN hydrologic model and (b) to study the capabilities of ANN to additively decompose total river discharge. This study adds knowledge to the open problem of the physical interpretability of black‐box models, which remains very limited. The results show that total discharge is adequately simulated in the time frequency domain, although less power spectrum is evident during the rainy seasons in the ANN model, due to fast flow underestimation. The wavelet spectrum of discharge represents well the slow, middle and fast flow components of the system with transit times of 256, 12–64 and 2–12 days, respectively. Interestingly, these transit times are remarkably similar to those of the soil water reservoirs of the studied system, a small headwater catchment in the tropical Andes. This result needs further research because it opens the possibility of determining MMT on a fraction of the cost of isotopic based methods. The cross‐power spectrum indicates that the error in the simulated discharge is more related to the misrepresentation of the fast and the middle flow components, despite limitations in the recharge period of the slow flow component. With respect to the representation of individual signals of the slow, middle and fast flows components, the three neurons were uncapable to individually represent such flows. However, the combination of pairs of these signals resemble the dynamics and the spectral content of the aforementioned flows signals. These results show some evidence that signal processing techniques may be used to infer information about the hydrological functioning of a basin.The problem of discharge forecasting using precipitation as input is still very active in Hydrology, and has a plethora of approaches to its solution. But, when the objective is to simulate discharge values without considering the phenomenology behind the processes involved, Artificial Neural Networks, ANN give good results. However, the question of how the black box internally solve this problem remains open. In this research, the classical rainfall‐runoff problem is approached considering that the total discharge is a sum of components of the hydrological system, which from the ANN perspective is translated to the sum of three signals related to the fast, middle and slow flow. Thus, the present study has two aims (a) to study the time‐frequency representation of discharge by an ANN hydrologic model and (b) to study the capabilities of ANN to additively decompose total river discharge. This study adds knowledge to the open problem of the physical interpretability of black‐box models, which remains very limited. The results show that total discharge is adequately simulated in the time frequency domain, although less power spectrum is evident during the rainy seasons in the ANN model, due to fast flow underestimation. The wavelet spectrum of discharge represents well the slow, middle and fast flow components of the system with transit times of 256, 12–64 and 2–12 days, respectively. Interestingly, these transit times are remarkably similar to those of the soil water reservoirs of the studied system, a small headwater catchment in the tropical Andes. This result needs further research because it opens the possibility of determining MMT on a fraction of the cost of isotopic based methods. The cross‐power spectrum indicates that the error in the simulated discharge is more related to the misrepresentation of the fast and the middle flow components, despite limitations in the recharge period of the slow flow component. With respect to the representation of individual signals of the slow, middle and fast flows components, the three neurons were uncapable to individually represent such flows. However, the combination of pairs of these signals resemble the dynamics and the spectral content of the aforementioned flows signals. These results show some evidence that signal processing techniques may be used to infer information about the hydrological functioning of a basin

Local rainfall modelling based on global climate information: a data-based approach

Modelar el clima es complejo debido a las interacciones de múltiples escalas y las fuertes no linealidades. Sin embargo, las señales climáticas suelen ser cuasi periódicas y es probable que dependan de variables exógenas. Motivados por esta idea, proponemos una estrategia para eludir la complejidad del modelado basada en las siguientes ideas. 1) Las señales observadas se pueden descomponer en tendencias no estacionarias y cuasi-periodicidades a través de Regresiones Dinámicas-Armónicas (DHR). 2) Las frecuencias principales y las señales descompuestas se pueden utilizar para construir un modelo armónico con parámetros variables según las variables exógenas. 3) La técnica de parámetros dependientes del estado (SDP) permite la estimación dinámica de estos parámetros. El enfoque combinado DHR-SDP resultante se aplica al modelado de lluvia mensual, utilizando señales climáticas globales como variables exógenas. Como resultado, 1) el modelo produce mejores predicciones que las técnicas alternativas estándar; 2) el modelo es sólido con respecto a las limitaciones de los datos y útil para la previsión de varios pasos por delante; 3) Se obtienen relaciones interesantes entre los estados climáticos globales y la estacionalidad de la precipitación local a partir de las funciones estimadas del SDP.Modelling climate is complex due to multi-scale interactions and strong nonlinearities. However, climate signals are typically quasi-periodical and are likely to depend on exogenous-variables. Motivated by this insight, we propose a strategy to circumvent modelling complexity based on the following ideas. 1) The observed signals can be decomposed into non-stationary trends and quasi-periodicities through Dynamic-Harmonic-Regressions (DHR). 2) The main-frequencies and decomposed signals can be used for constructing a harmonic model with varying parameters depending on exogenous-variables. 3) The State-Dependent-Parameter (SDP) technique allows for the dynamical estimation of these parameters. The resulting DHR-SDP combined approach is applied to rainfall- monthly modelling, using global-climate signals as exogenous-variables. As a result, 1) the model yields better predictions than standard alternative techniques; 2) the model is robust regarding data limitations and useful for several-steps-ahead forecasting; 3) interesting relations between global-climate states and the local rainfall’s sea- sonality are obtained from the SDP estimated functions

Climate change influences of temporal and spatial drought variation in the andean high mountain basin

Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981−2005) and future period (2011−2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades.Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981−2005) and future period (2011−2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades