Carlos Gay-García

A time-series analysis of the 20th century climate simulations produced for the IPCC's Fourth Assessment Report.

In this paper evidence of anthropogenic influence over the warming of the 20th century is presented and the debate regarding the time-series properties of global temperatures is addressed in depth. The 20th century global temperature simulations produced for the Intergovernmental Panel on Climate Change’s Fourth Assessment Report and a set of the radiative forcing series used to drive them are analyzed using modern econometric techniques. Results show that both temperatures and radiative forcing series share similar time-series properties and a common nonlinear secular movement. This long-term co-movement is characterized by the existence of time-ordered breaks in the slope of their trend functions. The evidence presented in this paper suggests that while natural forcing factors may help explain the warming of the first part of the century, anthropogenic forcing has been its main driver since the 1970’s. In terms of Article 2 of the United Nations Framework Convention on Climate Change, significant anthropogenic interference with the climate system has already occurred and the current climate models are capable of accurately simulating the response of the climate system, even if it consists in a rapid or abrupt change, to changes in external forcing factors. This paper presents a new methodological approach for conducting time-series based attribution studies.

The new national climate change documents of Mexico: what do the regional climate change scenarios represent?

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.

Climate patterns of political division units obtained using automatic classification trees

This article proposes a methodology to discover patterns in observed climatologic data, particularly temperatures and rainfall, in subnational political division units using an automatic classification algorithm (a decision tree produced by the C4.5 algorithm). Thus, the patterns represent classification trees, assuming that: (1) every political division unit contains at least one climatological station, and (2) the recording periods of the stations are relatively similar in duration and in their initial and ending years. A series of classification models are produced by using different subsets from an experimental dataset. This dataset contains information from 3606 climatological stations in Mexico with recording periods whose durations, initial and ending years are diverse. The target (dependent) variable in all these models is the name of the political unit (i.e., the state). The predictors are 36 monthly features per each climatological station: 12 features corresponding to a minimum temperature, 12 to a maximum temperature, and 12 to cumulative rainfall. The altitude feature is also used as one of the predictors, in addition to the other 36; however, it is used only to quantify its additional contribution to the modelling. The results show that classification trees are effective models for describing and representing non-trivial patterns to characterize the political division units based on their monthly temperatures and rainfalls. One of the remarkable findings is that the cumulative rainfall of May is the feature with highest discrimination capability to the characterization task, which is consistent with the theoretical background on Mexican climatology. In addition, classification trees offer higher expressivity to non-experts in machine learning.

Fuzzy Cognitive Mapping and Nonlinear Hebbian Learning for the Qualitative Simulation of the Climate System, from a Planetary Boundaries Perspective

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.

An estimate of global temperature increase by means of a fuzzy logic model

The climate change scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) indicate a wide range of future concentration of greenhouse gases and the corresponding range of temperature increases. From these data, it can be inferred that higher temperature increases are directly related to higher emission levels of greenhouse gases and to the increase in their atmospheric concentrations. It is also evident that lower temperature increases are related to smaller amounts of emissions and, therefore, to lower greenhouse gases concentrations. In this work, simple linguistic rules are extracted by means of visual inspection of the IPCCs Fourth Assessment Report. These rules describe the relations between the greenhouse gases emissions, their concentrations, the radiative forcing associated with concentrations, and the corresponding temperature changes as would be obtained by expert opinion. These rules are used to build a fuzzy model, which uses emission and concentration values of greenhouse gases as input variables and gives, as output, the temperature increase projected at year 2100. A second fuzzy model based on Zadehs extension principle is also build using temperature values obtained from a simple, deterministic climate system model. Both fuzzy models are very attractive because their simplicity and capability to integrate the uncertainties associated to the input and output variables. These simple models contain all the information of much more complex determinist models, characteristics that make easier to understand the behavior of the system and help to produce climate change scenarios that could be more meaningful for policy-makers.

Effects of El Niño in Mexico during rainy and dry seasons: an extended treatment

The effect of El Nino-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.

Preface to the thematic issue on Climate, economics and statistics

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.

Adaptive Neuro Fuzzy Inference System Used to Build Models with Uncertain Data: Study Case for Rainfed Maize in the State of Puebla (Mexico)

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.

Using fuzzy cognitive mapping and nonlinear Hebbian Learning for modeling, simulation and assessment of the climate system, based on a planetary boundaries framework

In the present work a fuzzy cognitive map for the qualitative assessment of the Earth climate system is developed by considering subsystems on which the climate equilibrium depends. The cognitive map was developed as a collective map by aggregating different experts opinions. The resulting network was characterized by graph indexes and used for simulation and analysis of hidden pattens and model sensitivity. Linguistic variables were used to fuzzify the edges and were aggregated to produce an overall linguistic weight for each edge. The resulting linguistic weights were defuzzified using the “Center of Gravity”, 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. The overall results are discussed to explore possible policy implementation, environmental decision making and management.

The economics of climate change in Mexico: implications for national/regional policy

The recent Mexican government study, The Economics of Climate Change in Mexico (ECCM), which has largely influenced Mexicos stance on climate change issues and international negotiations, is critically reviewed. Whilst the importance of such government-supported national studies as a first attempt to provide estimates on the anticipated costs of climate change is recognized, there is scope to strengthen the underlying analysis. It is argued that some of the key policy recommendations of ECCM are weakly supported by its analysis, that it has some methodological weaknesses, and that there are inconsistencies with the approach adopted in the Stern Review. Furthermore, it is likely that the estimated costs severely under-represent future climate change damages in the case of Mexico, which could deter drastic mitigation and adaptation efforts. New estimates of the costs of climate change are presented based on the impact functions of two integrated assessment models. Policy relevance Due to its large influence in building a regional view of what climate change could imply for Latin America, the analysis of the ECCM highlights the need to strengthen the analysis of national climate documents to ensure they properly support national/regional policy making. The academic evaluation of national climate change documents is a necessary prerequisite for the development of sound climate policy.