Two Approaches to Attribution of the 2010 Russian Heat Wave - Research Paper Example

Reconciling Two Approaches to Attribution of the Russian Heat Wave Summer Western Russia experienced a shocking turn of events as an extraordinary heat wave rocked the area with the region experiencing the warmest July ever since the record taking began. It was not certain what the chief cause of the event was and this brewed the question on whether it was as a result of anthropogenic climate . This made the subject a controversial issue. (Dole et al). reported that the heat wave was a natural occurrence and in comparison, Rahmstorf and Coumou recorded that there was a probability of an estimated 80% that the heat wave would not come to pass without the large-scale global warming starting in the 1908s. The global warming epidemic led to an increase in the anthropogenic greenhouse gas concentrations in the atmosphere. Their conclusive outcome highly contradicted those of (Dole et al.) they stated that they used information gathered from a large ensemble recreation experiment encompassing an atmospheric and general circulation replica to exhibit that there is no substantive challenge between the two papers. In other words, the same event can occur as a result of internal generation in stipulations of magnitude and external drives in provisions of occurrence probability. The distinction in the conclusions of the papers indicates the significance of accurate and concise specifications of the questions asked in addressing the issue in the context of acknowledgment of individual weather patterns to the external drivers of climate change. There were speculations that there are contradictory explanations in answering the question of whether the heat wave occurred naturally or the extent of the anthropogenic gas emissions directed the cause. However, given the statistical data cumulated where 55,000 people lost their lives, a drop in the annual crop production by 25%, and an overall loss in the economy of an approximated 15billion dollar prompted the investigation of an answer to the society. The heat wave began at the start of July and reached its record temperatures late July and the temperatures started decreasing in August. August 19th was the last break in the high heat. The persistence posed by the anomalous high temperatures extending over a month was likely due to a jamming state of affairs not uncommon in these areas. In 2010, the blocking soaring was immensely persistent and intense accompanied by temperatures 50C above the normal mean. Taking into mind the socioeconomic and ecological factors collisions of such events they are of interest whether, or to what degree, anthropogenic gaseous emissions contributed to the likelihood of the event and if it was possible to predict. D11 concluded that natural viability was the primary foundation for the event while RC11 reported that there was an 80% prospect it would not have come about without the contribution of the climate warming. Also, there is a suggestion of a clearer formulation for this finality and the fact is that it increased by 80% of the current risk when one refers to the external trends. D11 concentrates their analysis mainly on the enormity of the occurrence in data experiential data collected and processed annually and two 50 affiliate atmospheric collective flow ensembles for July 2011. RC11 analyzes the frequencies of occurrences of heat waves through a detailed and thorough comparison between Monte Carlo simulations of unwavering climate alongside those that demonstrate inclinations and using the Russian heat wave as an instance. In this regard, it is indispensable to note that RC11 inquires the rate of recurrence of evidence breaking heat wave, thus the level of the heat wave is considerably irrelevant in their experiments while it is imperative to D11. Therefore, these studies focus on arguing the results and impose a sense that the results need not contradict each other as natural climate variability can explain the event of the heightened level. It would be inconsiderate to assume the increased global warming crisis not being a factor in contributing to the incident and these anthropogenic gaseous emissions account for the summer waves in 2003 in Europe not forgetting the heat wave of heat in the autumn of 2006. Furthermore, D11 seeks to concentrate on whether the occurrence was in any way predictable in close reference to the seasonal time scale. The derived ending is that there are no viable and valid predictors except the global warming scenario. Despite this, for intrinsically low probability happenings the question of whether the heat wave was conventional is completely separate from the query of what fraction of jeopardy is attributable to external forcing. It is also greatly central to highlight that it is not right to assess the actual tiny proportion of risks leading to anthropogenic climate diversification as it would necessitate a thorough evaluation of uncertainties and errors and also depict a trial designed to answer the inquiry and provide illustrative findings. The technique needs to access an adequately large tally of simulations to make it achievable for statistics of rare events with much accrued confidence. The weather at home plan distributes a big group employing publicly volunteered strewn computing. D11 used information from the area that includes 50°–60°N, 35°–55° E. So as to decisively scrutinize the leeway of characterizing the heat wave of 2010 in Russia, the regularity of episodes of this scale is the central interest. First, one conducts an analysis of the observable data to appraise if the distribution plays out proportionally to the existence of trends. On the other hand, one needs to account for changes in the return times of rare events and hefty ensembles are a requirement and in this regard the main analytical exercise puts the large GCM collection as the basis of foundation. Assuming a stationary climate with no changes whatsoever in the mean temperatures, the viewed monthly mean temperatures for the month of July 2010 are improbable in the context of allocation documented between 1950 and 2009. A Generalized Pareto Distribution fits over 20% of the highest levels defined a distribution where the return time of the observed rate in July 2010 estimated to about 100 years. This was a lower bound of 95% assurance interval ranging to about 250 years. In abstract, this means that without the warming fashion in Russia the heat wave would resonate as an unusual turn of event. D11 illustrates that there are no significant long term local temperature drifts in July mean temperatures looking at the 130 year period between 1880 and 2009 whilst applying valuable differences in mean temperatures between the first and second halves of the record. The technique uses a non-linear style inclusive of a more sensitive quantification, the regression on the worldwide mean temperature, also smoothened with a 3 year running mean to lower the effects of ENSO. It also restricts itself to surveillances after 1950 that are more reliable with spatial homogeneity of location data trends improved prior to the 1950s due to relocation of weather stations away from the city centers to the airports. RC11 gave an idea about the recent decade being relevant years with respect to regional trends. In conclusion, D11 focuses its perspective of answering the question of whether the Russian heat wave that occurred in July 2010 was anticipation from the season forecasting point of view by thoroughly analyzing atmospheric conditions and regional data culminating to the heat wave. In comparison, RC11 indulges in a diverse method by fitting non-linear trends in central Russian forecasting temperatures and depicting that the warming that took place in the expanse since the 1960s increased the insurgent risks of heat waves and thereby setting new records of temperatures by a 5 factor in correspondence of a FAR of 0.8. This is just a partial explanation study as they do not address the reason that brought about the heat wave fashion since the 1960s. They note that other affiliate studies associate the warming over the period in the circumstance of anthropogenic rise green house concentrations. Although they are singular, they are complementary in approximating the function of human influence to the Russian heat wave. Figure 1(a). A scatter of Russian’s mean temperatures and mean geospatial heights and (b) shows rectified bias corrected in the Russian temperature anomalies and the regression of normalized geospatial height anomalies against the synoptic structure regression pattern. The blue line represents the one to one line of perfect correlation. Figure 2. Regression maps representing the synoptic structures of the summer heat waves as experienced in Europe in August 2003 and a contrast of the autumn heat wave occurring in Russia in July 2010. Figure 3. A representation of the daily average temperatures experienced in Moscow, Russia. The red shaded area represents the rise in the temperatures while the blue shaded area represents the lower temperatures experienced. RC11 insists on using this collected information to make deductive reasoning decisions on what actually led to the occurrence of the July heat wave in July 2010. Figure 4. Return periods of temperature-geopotential height conditions in the model for the 1960s (green) and the 2000s (blue) and in ERA-Interim for 1979–2010 (black). The vertical black arrow shows the anomaly of the Russian heat wave 2010 (black horizontal line) compared to the Julymean temperatures of the 1960s (dashed line). The vertical red arrow gives the increase in the magnitude of the heat wave due to the shift of the distribution whereas the horizontal red arrow shows the change in the return period. Works cited Allen, M., P. Pall, D. Stone, P. Stott, D. Frame, S. Min, T. Nozawa, and S. Yukimoto, Scientific challenges in the attribution of harm to human influence on climate, Pa: Law Rev., 2007. Barriopedro, D., E. M. Fischer, J. Luterbacher, R. M. Trigo, and R. García-Herrera, The hot summer of 2010: Redrawing the temperature record map of Europe: Science, 2011. Read More