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|Title:||Global Climate Change: Analyzing Anthropogenic Warming and Causality|
|Authors:||STIPS Adolf; MACIAS MOY DIEGO; COUGHLAN CLARE; GARCIA GORRIZ Elisa|
|Type:||Articles in periodicals and books|
|Abstract:||During the past five decades, global air temperatures have been warming at a rather high rate (IPCC-2013) resulting in scientific and social concern. This warming trend is observed in field and model data and affects air temperatures both over land and over the ocean. IPCC attributes this temperature increase to the total increase in radiative forcing and maintains that this is primarily caused by the increase in the atmospheric concentration of CO2 during the last 200 years. However, the warming rate changes with time and this has led to discussion on the causes underlying the observed trends. Another major problem is related to the relatively large uncertainty in the different external forcing components used for global climate simulations. ‘Detection’ and ‘attribution’ are therefore regarded as key priorities in climate change research. IPCC defines ‘detection’ as the process of demonstrating that climate has changed in some statistical sense, where the likelihood of occurrence by chance due to internal variability alone is small. The more challenging problem is then to ‘attribute’ this detected climate change to the most likely external causes within some defined level of confidence, which we will address in this contribution. Here, we analyze recent measured data on global mean surface air temperature anomalies (GMTA) and various external forcings covering the last 160 years using newly developed techniques that allow discrimination between correlation and causality. This evaluation is based on a new concept for calculating the information flow between time series. Calculating, for example, the information flow in nat (natural unit of information) per unit time from the time series of global CO2 concentration to GMTA we get 0.348 [nat/year] and -0.006 [nat/year] in the reverse direction. Causality is expressed by an information flow significantly different to 0.0, whereas an information flow close to 0.0 indicates that the two time series are not causally related. Our result demonstrates one-way causality in the sense that the CO2 increase is causing the temperature increase and not the other way around. The positive value of the information flow indicates further that CO2 has a positive feedback and therefore a destabilizing effect on GMTA; more CO2 would lead to a stronger increase in GMTA. The results of investigating the information flow between the major radiative forcings and the GMTA time series clearly show that total Green House Gases (GHG), dominated in particular by CO2 forcing, is the main driver of changing global surface air temperature. Radiative forcing caused by aerosols and clouds is still important, but significantly smaller. Neither forcing by solar irradiance nor volcanic forcing contributes in a significant manner to the GMTA development. In summary, total anthropogenic forcing (GHG and aerosols) is the main causal factor in determining surface air temperature. Finally, we applied the same causality analysis to the globally-gridded GMTA product in order to assess regional “sensitivity” to anthropogenic forcings versus natural modes of variability. Assuming that the available time series are long enough to contain sufficient statistics we can demonstrate the inherent one-way causality between the main anthropogenic radiative forcing and the GMTA time series, a result that cannot be inferred from traditional time delayed correlation analysis.|
|JRC Directorate:||Sustainable Resources|
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