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 onetime 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 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.

RESUMENSe analiza el efecto de El Niño-Oscilación del Sur (ENSO, por sus siglas en inglés) sobre la precipitación en México. A diferencia de estudios anteriores, en éste se maneja una mayor cantidad de datos y se cubre más extensamente el territorio mexicano. En este trabajo se emplearon los datos de precipitación diaria de la base de datos CLICOM a partir de 1961, actualizada hasta 2015. El periodo de estudio se dividió en dos fases: 1961 a 1990 y 1991 a 2013, y se consideraron también separadamente las épocas fría y seca del año (noviembre-abril), y la cálida y húmeda (mayo-octubre). De esta manera, la cantidad de estaciones que superan los criterios de cantidad de información continua para un periodo determinado, aumenta considerablemente. Se utiliza el coeficiente de correlación de Pearson con una significancia de 5% para encontrar la relación entre la precipitación y el índice multivariado de ENSO (MEI, por sus siglas en inglés). Los resultados se presentan en mapas donde se observan regiones con precipitación por arriba o debajo del promedio. Claramente se identifica la región noroeste de México con una relación directa entre MEI y precipitación; mientras que se observa una relación inversa en la parte que se encuentra al sur del paralelo 22º N, durante los meses de verano. En el periodo invernal existe un aumento generalizado de la precipitación conforme aumenta el MEI. Se muestra la distribución de la lluvia para periodos normales tanto de invierno como de verano. ABSTRACTThe 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.

Integrated assessment models (IAMs) typically ignore the impact of climate change on economic growth, or simply scale down output and hence the entire future growth. In this manner, IAMs typically assume that the shocks caused by climate change impacts dissipate and have no persistence at all, affecting only the period when they occur. Clearly, this could lead to the underestimation of costs of climate change at global, regional, national and local scales. We adopt an empirical approach for analyzing the observed GDP series for different world regions in order to estimate the persistence of shocks on growth. We interpret the direct impact of climate change as such shocks, and use the estimated models to assess the implications for growth. We compare this to the scaling method pioneered by Nordhaus (Nordhaus and Boyer, 2000). A simple version of the widely used PAGE2002 model (Hope, 2006) is applied to conduct a sensitivity analysis varying the degree of a persistence measure in simulated future GDP. It is shown that when a persistence similar to the observed one is chosen, the economic impacts of climate change are considerably larger in comparison to the "zero persistence" implied by the original scaling method. If the persistence of shocks is ignored, as it is currently done by most IAMs, the economic impacts of climate change can be severely underestimated. Results are not sensitive to the selection of the discount rate. Moreover, it is shown that the original scaling method embedded in most IAMs can be interpreted as assuming an autonomous, costless, extremely large and effective (reactive) adaptation capacity.

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