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© 2021 American Meteorological Society. All rights reserved.The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included.Anew methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particularGCMis compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s.

Currently the drinking water supply service system of the Metropolitan Area of Mexico City (ZMCM) faces serious problems in its operation, which generate highly negative impacts on the environmental, social and provider sectors of this system. Given the presence of climate change, we consider the possible scenario in which a decrease in average annual precipitation is generated in the area. To evaluate its impact on each of the sectors, the fuzzy cognitive map (FCM) associated with each of them was constructed. This framework allowed the study of a system that, in addition to being highly complex, the available information presented large ranges of uncertainty. Based on the resuls obtained, we present a mitigation measure for each sector, in order to provide efficient actions to decisions makers.

Rising temperatures can lead to the occurrence of a large-scale climatic event, such as the melting of Greenland ice sheet, weakening the AMOC and further increasing dissimilarities between current and future climate. The impacts of such an event are still poorly assessed. Here, we evaluate those impacts across megadiverse countries on 21,146 species of tetrapods and vascular plants using the pessimistic climate change scenario (RCP 8.5) and four different scenarios of Greenland's ice sheet melting. We show that RCP 8.5 emission scenario would lead to a widespread reduction in species' geographic ranges (28-48%), which is projected to be magnified (58-99%) with any added contribution from the melting of Greenland. Also, declines in the potential geographical extent of species hotspots (12-89%) and alterations of species composition (19-91%) will be intensified. These results imply that the influence of a strong and rapid Greenland ice sheet melting, resulting in a large AMOC weakening, can lead to a faster collapse of biodiversity across the globe.

Anthropogenic greenhouse gas emissions have led to sustained global warming over the last decades1. This is already reshaping the distribution of biodiversity across the world and can lead to the occurrence of large-scale singular events, such as the melting of polar ice sheets2,3. The potential impacts of such a melting event on species persistence across taxonomic groups – in terms of magnitude and geographic extent – remain unexplored. Here we assess impacts on biodiversity of global warming and melting of Greenland’s ice sheet on the distribution of 21,146 species of vascular plants and tetrapods across twelve megadiverse countries. We show that high global warming would lead to widespread reductions in species’ geographic ranges (median range loss, 35–78%), which are magnified (median range loss, 95–99%) with the added contribution of Greenland’s melting and its potentially large impact on oceanic circulation and regional climate changes. Our models project a decline in the geographical extent of species hotspots across countries (median reduction, 48–95%) and a substantial alteration of species composition in the near future (mean temporal dissimilarity, 0.26–0.89). These results imply that, in addition to global warming, the influence of Greenland’s melting can lead to the collapse of biodiversity across the globe, providing an added domino in its cascading effects.

In Colombia, a country with great climatic diversity, the water balance is affected in one way or another by climate change depending on the region. Thus, there may be increases and decreases in precipitation and, in all cases, a huge increase in temperature. This document presents some studies carried out in different areas of the country regarding the effects of climate change on water resources, including its influence on hydroelectric power generation, some changes in the water balance in arid areas, and the opportunity to ensemble climate change scenarios. Likewise, it outlines a possible future water supply-demand relationship, where supply is associated with a change in the water balance and demand with some crops, activities, and sectors that need water to survive. This allows to estimate some future status indices to see the overall picture of climate change in connection with the country’s water resources.

Extracted nonstationary oscillations and nonlinear trends of precipitation and sea surface temperature (SST) data reveal that rainfall variability in Mexico City is mainly composed by a long‐term positive trend, a multidecadal oscillation highly correlated with the Atlantic Multidecadal Oscillation (AMO), and year‐to‐year variability. The precipitation trend, lasting for more than a century, cannot be attributed to global warming or urbanization alone; rather, it can be thought of as part of a natural oscillation composed of alternating wet‐dry anomalies with a period of a couple of centuries, as past evidence indicates. To further test the dependence of the AMO‐related component, yearly SST time series were derived from a simplified model of the atmosphere‐ocean system forced by white noise. The simulated SST time series exhibits AMO‐like variability entirely consistent with the observed one, implying that North Atlantic SST multidecadal variability can be seen as the integrated response of surface ocean layers to external stochastic atmospheric forcing.

"The effect of El Niño-Southern Oscillation (ENSO) on precipitation in Mexico is analyzed. Unlike previous studies, the amount of data used is larger and the Mexican territory is more widely covered. In this paper, daily precipitation from the CLICOM database updated to 2015 was used. The studied period spans from 1961 to 2013 and was divided into two periods: 1961-1990 and 1991-2013. For the same periods two separated seasons were considered: the cold and dry (November-April), and the warm and wet (May-October). Thus, the number of stations that exceed the amount of continuous information criteria for a certain period increases considerably. The Pearson correlation coefficient with a significance of 5% was used in order to test for the existence of a relationship between precipitation and the Multivariate ENSO Index (MEI). The results are presented in maps where regions of precipitation above or below average are observed. During the summer/warm months, the northwestern region of Mexico is clearly identified with a direct relationship between MEI and precipitation, whereas an inverse relationship in the part that lies south of latitude 22º N is seen. In the winter/cold months, there is a general increase in precipitation with increasing MEI. Distributions of normal rainfall for both winter and summer are also shown."

Atmospheric pollution in cities is due to several human factors, for instance the number of cars in circulation, fuel efficiency and industrial waste, as well as orographic and meteorological conditions that determine air circulation. Ozone contingencies cause health disorders on the population, making it important to understand the factors that trigger such contingencies. Here, we analyze meteorological (wind, temperature, relative humidity) and atmospheric composition (ozone, and NOx) data of five atmospheric monitoring stations on Mexico City, from March 2004 to May 2018, comparing normal days with the extreme days in the 90th percentile of ozone. Moreover, we present the synoptic patterns of the seasonal differences of geopotential height at 500 hPa between extreme and control days. We found that, in the dry-hot season (from March to May) an atmospheric blockage with meteorological conditions of almost no wind, low relative humidity, and small temperature fluctuations occurs. Because the air in the city permanently contains large amounts of ozone precursors like NOx, this meteorological scenario raises ozone levels to those of an environmental contingency. Thus, during the dry-hot season on Mexico City, ozone contingencies are triggered by atmospheric blocking. This scenario will be present in cities surrounded by mountains with high levels of Ozone precursors.

Environmental problems are typically produced by the interactions of complex natural and human systems and can entail far-reaching and sometimes irreversible consequences. Their study is characterized by the existence of incomplete knowledge and high levels of uncertainty and, in some cases, ambiguousness in the definition of the problem itself and its boundaries. These so-called wicked problems openly challenge the traditional disciplinary approach commonly used in natural and social sciences and call for the development of interdisciplinary, integrated research, as well as for innovative methods and frameworks.

The urge for higher resolution climate change scenarios has been widely recognized, particularly for conducting impact assessment studies. Statistical downscaling methods have shown to be very convenient for this task, mainly because of their lower computational requirements in comparison with nested limited-area regional models or very high resolution Atmosphere-ocean General Circulation Models. Nevertheless, although some of the limitations of statistical downscaling methods are widely known and have been discussed in the literature, in this paper it is argued that the current approach for statistical downscaling does not guard against misspecified statistical models and that the occurrence of spurious results is likely if the assumptions of the underlying probabilistic model are not satisfied. In this case, the physics included in climate change scenarios obtained by general circulation models, could be replaced by spatial patterns and magnitudes produced by statistically inadequate models. Illustrative examples are provided for monthly temperature for a region encompassing Mexico and part of the United States. It is found that the assumptions of the probabilistic models do not hold for about 70 % of the gridpoints, parameter instability and temporal dependence being the most common problems. As our examples reveal, automated statistical downscaling "black-box" models are to be considered as highly prone to produce misleading results. It is shown that the Probabilistic Reduction approach can be incorporated as a complete and internally consistent framework for securing the statistical adequacy of the downscaling models and for guiding the respecification process, in a way that prevents the lack of empirical validity that affects current methods.

The methods employed by Magaña et al. (2012; Clim Res 51:171-184) in their analysis of the effects of climate change in northern Mexico are flawed. Our present analysis confirms the results in Estrada et al. (2012; Clim Change 110:1029-1046), demonstrating that the downscaling method in Magaña et al. (2012) is unacceptable for producing regional climate change scenarios. The method generates random spatial patterns and magnitudes that replace the climate signals produced by general circulation models, as well as those contained in the observed local-scale data. Consequently, any application that uses these regional scenarios should not be used for decision-making. The statistical downscaling literature provides a range of methods that are appropriate for producing regional climate change scenarios.

"Este artículo propone una metodología para descubrir patrones en datos climatológicos, particularmente temperaturas y precipitación, observados en unidades políticas subnacionales, usando un algoritmo de clasificación automática (un árbol de decisión producido por el algoritmo C4.5). Por lo tanto, los patrones representan árboles de clasificación, en el supuesto de que: 1) cada unidad de división política contiene al menos una estación climatológica y 2) los periodos de registro de las estaciones son relativamente similares en duración y en sus años iniciales y finales. Se produce una serie de modelos de clasificación mediante el uso de diferentes subconjuntos de un conjunto de datos experimentales. Este conjunto de datos contiene información de 3606 estaciones climatológicas en México cuyos periodos de registro tienen diversas duraciones, años iniciales y finales. La variable objetivo (dependiente) en todos estos modelos es el nombre de la unidad política (es decir, el estado). Los predictores son 36 características mensuales por cada estación climatológica: 12 corresponden a una temperatura mínima, 12 a una temperatura máxima y 12 a la precipitación acumulada. También se usó la altitud como predictor adicional a los 36 mencionados, pero sólo para cuantificar su contribución adicional al modelado. Los resultados muestran que los árboles de clasificación son modelos eficaces para describir y representar los patrones no triviales que caracterizan a las unidades de división política, con base en sus temperaturas y precipitación mensual. Uno de los hallazgos destacables es que la precipitación acumulada de mayo es la característica con el mayor poder discriminatorio en esta tarea de caracterización, lo cual es consistente con el trasfondo teórico de la climatología mexicana. Además, los árboles de clasificación ofrecen alta expresividad a personas poco familiarizadas con aprendizaje automático."

This paper presents a review of the methodology applied for generating the regional climate change scenarios utilized in important National Documents of Mexico, such as the Fourth National Communication to the United Nations Framework Convention on Climate Change, the Fourth National Report to the Convention on Biological Diversity and The Economics of Climate Change in Mexico. It is shown that these regional climate change scenarios, which are one of the main inputs to support the assessments presented in these documents, are an example of the erroneous use of statistical downscaling techniques. The arguments presented here imply that the work based on such scenarios should be revised and therefore, these documents are inadequate for supporting national decision- making. © 2011 Springer Science+Business Media B.V.

To characterize observed global and hemispheric temperatures, previous studies have proposed two types of data-generating processes, namely, random walk and trend-stationary, offering contrasting views regarding how the climate system works. Here we present an analysis of the time series properties of global and hemispheric temperatures using modern econometric techniques. Results show that: The temperature series can be better described as trend-stationary processes with a one-time permanent shock which cannot be interpreted as part of the natural variability; climate change has affected the mean of the processes but not their variability; it has manifested in two stages in global and Northern Hemisphere temperatures during the last century, while a second stage is yet possible in the Southern Hemisphere; in terms of Article 2 of the Framework Convention on Climate Change it can be argued that significant (dangerous) anthropogenic interference with the climate system has already occurred.

In the present work, we constructed a collective fuzzy cognitive map for the qualitative simulation of the Earth climate system. The map was developed by considering the subsystems on which the climate equilibrium depends, and by aggregating different experts opinions over this framework. The resulting network was characterized by graph indexes and used for the simulation and analysis of hidden patterns and model sensitivity. Then, linguistic variables were used to fuzzify the edges and aggregated to produce an overall linguistic weight for each one. The resulting linguistic weights were defuzzified using the center of gravity technique, and the current state of the Earth climate system was simulated and discussed. Finally, a nonlinear Hebbian learning algorithm was used for updating the edges of the map until a desired state was reached, defined by target values for the concepts. The results are discussed to explore possible policy implementation, as well as environmental decision making.

A model was built using Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the relationship between the natural suitability index of rainfed maize and yield per hectare and percentage of production area lost for the state of Puebla. The data used to build the model presented inconsistencies. The data of the INEGIs land use map presented more municipalities without rainfed maize agriculture than the database of SAGARPA. Also the SAGARPA data, in terms of the percentage of production area lost, do not mark any distinctions of the loss. Even with data inconsistencies ANFIS produced a coherent output reviewed by experts and local studies. The model shows that higher the percentage of production area lost and high yields, the higher the suitability index is. According to local studies this is due to the high degradation of the soils and confirmed with the second model built adding soil degradation information.

Linear evolving emission scenarios are used instead of those of IPCC. They preserve, indeed they cover, the ranges of the corresponding IPCC values for the concentrations, forcings and global temperatures. Then, through fuzzy rules among concentrations, climate sensitivity and global temperature change, a fuzzy model has been conformed and used to explore uncertainties due to: not knowing what the emissions are going to be in the future, the one related to the climate sensitivity of the models (this has to do with different parameterizations of processes used in the models) and the uncertainties in the temperature maps produced by the models. Furthermore we show maps corresponding to 1, 2, etc., degrees centigrade of global and regional temperature increase and discuss the timing of exceeding them. Instead of talk about the uncertainty in temperature at a certain date we talk about the uncertainty in the date certain temperature is reached.

In the present work we developed a soft computing model for the qualitative analysis of the Earth’s climate system dynamics throughout the implementation of fuzzy cognitive maps. For this purpose, we identified the subsystems in terms of which the dynamics of the whole system can be described. Then, with these concepts we built a cognitive map via the study of the documented relations among these concepts. Once the map was built, we used the technique of state vector and the adjacent matrix to found the hidden pattens, i.e the feedback processes among system’s nodes. Later on, we explored the sensibility of the model to changes in the weights of the edges, and also to changes in the input data values. Finally, we used fuzzy edges to analyze the causality flux among concepts and to explore possible solutions applied in specific edges.