Detection of Regional Surface Temperature Trends

Abstract

Trends in surface temperature over the last 100, 50, and 30 yr at individual grid boxes in a 5° latitude–longitude grid are compared with model estimates of the natural internal variability of these trends and with the model response to increasing greenhouse gases and sulfate aerosols. Three different climate models are used to provide estimates of the internal variability of trends, one of which appears to overestimate the observed variability of surface temperature at interannual and 5-yr time scales. Significant warming trends are found at a large fraction of the individual grid boxes over the globe, a much larger fraction than can be explained by internal climate variations. The observed warming trends over the last 50 and 30 yr are consistent with the modeled response to increasing greenhouse gases and sulfate aerosols in most of the models. However, in some regions, the observed century-scale trends are significantly larger than the modeled response to increasing greenhouse gases and sulfate aerosols in the atmosphere. Warming trends consistent with the response to anthropogenic forcing are detected at scales on the order of 500 km in many regions of the globe.

Time of emergence of trends in ocean biogeochemistry

“…The ToE method has been applied to a number of physical variables such as surface air temperature (Karoly and Wu, 2005;Diffenbaugh and Scherer, 2011;Mahlstein et al, 2011Mahlstein et al, , 2012Hawkins and Sutton, 2012;Mora et al, 2013) and precipitation (Giorgi and Bi, 2009), the combination of these two variables being indicative of future climate change hotspots (Diffenbaugh and Giorgi, 2012), or the imminent shift of climate regions (Mahlstein et al, 2013). A common approach to estimate ToE is the comparison of modeled noise (usually the standard deviation of an unforced control simulation) and observed (Karoly and Wu, 2005) or modeled (Mahlstein et al, 2011;Hawkins and Sutton, 2012) trends.…”

“…A common approach to estimate ToE is the comparison of modeled noise (usually the standard deviation of an unforced control simulation) and observed (Karoly and Wu, 2005) or modeled (Mahlstein et al, 2011;Hawkins and Sutton, 2012) trends. Other approaches derive both signal and noise from the same observational time series (Mahlstein et al, 2011(Mahlstein et al, , 2012 or forced model simulation (Giorgi and Bi, 2009;Diffenbaugh and Scherer, 2011;Diffenbaugh and Giorgi, 2012;Mora et al, 2013).…”

Climate Change detection over different land surface vegetation classes

“…Zhang et al (2005) found clear detectable anthropogenic forcing effects in nine spatial regions ranging from global scale to country-wide (such as southern Canada and China). Finally, Gillett et al (2004a) demonstrated that anthropogenic greenhouse gases and sulfate aerosols have had a detectable influence on fire-season warming of the Canadian forest region, while Karoly and Wu (2005) detected anthropogenic warming trends over many parts of the globe at scales of 500 km. The purpose of this paper is to quantitatively understand the anthropogenic influence on recent surface temperature changes at regional scales through a detection and attribution study.…”

Modeling European hot spells using extreme value analysis

“…However, temporal correlation between the stations, arising from a spatially extensive atmospheric signal, will result in overly optimistic confidence intervals. Karoly & Wu (2005) addressed this through a bootstrap resample of the field significance for gridbox squares, determining a spatial significance level of 19% for trends in surface temperatures with reference to the local significance level of 5% at each site. As resampling each 60-yr time series to improve the confidence interval estimates was computationally expensive, we made an allowance for temporal correlation by accepting field significance where the number of significant stations exceeded the nominal test level by at least 10% (Brown et al 2008).…”