Present‐day interannual variability of surface climate in CMIP3 models and its relation to future warming

Abstract

ABSTRACT:Interannual variability (IAV) of 2m temperature (T ), sea level pressure (SLP ) and precipitation (P ) in the CMIP3 20th century model simulations are compared with IAV in observational and reanalysis data sets using standard deviation based variability indices. Further, the relation between the representation of T IAV and the amplitude of future warming is investigated. In the Northern Hemisphere (NH) extratropics, T and SLP IAV are (in contrast to P ) in general well represented although a few models perform much worse than others. General problem regions are: (1) sea ice boundary regions, where well-known biases in the mean states exist; and (2) the Pacific Ocean and Central Africa where SLP IAV is consistently underestimated. T and SLP IAV discrepancies are often found in similar regions and are large in well-known bias problem regions in the tropics and subtropics and high mountain regions. 'Bad' IAV representation also occurs in regions with small biases. T IAV is in general better reproduced over land than over sea and in the extratropics than in the tropics. Among the 'good' IAV models there is no robust relation between the tropics (sea only) and the extratropics (land only). The relation between the model's ability to correctly represent T IAV and projected temperature changes is slightly negative (more warming for better IAV representation) but except for the NH summer season not significant when the worst models in terms of IAV representation are omitted. This suggests that aggregated over large regions (with exception of NH summer) no robust relations are found between the model's ability to correctly represent T IAV and the projected temperature change.

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

“…For climate variables such as land surface temperature and precipitation, we calculate the model variability index (MVI) (Gleckler et al, 2008;Scherrer, 2011). The model (mod) variability at every grid point i is compared against the observed (obs) variability and then averaged over the globe in the following way:…”

“…The definition of a limit to decide if a model performs well or poor is rather arbitrary. However, Scherrer (2011) and Anav et al (2013) have used a threshold of MVI < 0.5. For a number of carbon-related variables, we calculate the inter-annual variability (IAV), defined as the standard deviation of detrended annual-mean values.…”

Effects of regional climate change on rural fires in Portugal

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“…The numbers of large fires, amount of burnt area and fire severity have lately increased in Portugal (Marques et al 2011, Pereira et al 2011. Despite the severity of the problem, few studies have focused on the effects of climate change on wildfire risk in Portugal (Pereira et al 2002, Durão & Corte-Real 2006, Carvalho et al 2008, 2010. Pereira et al (2005) pointed out that the interannual variability of burnt area in Portugal is largely determined by 2 different atmospheric factors, namely (1) the amount of precipitation during spring (i.e.…”

Probabilistic Projections of Climate Change over China under the SRES A1B Scenario Using 28 AOGCMs

“…Before calculating the STD, there is no special filtering applied (Scherrer 2010); that is, interannual variability also contains some decadal variability strictly speaking, although we think the contribution from decadal variability is small. As in the study by Gleckler et al (2008) and Santer et al (2009), we calculate a ''symmetric'' variability statistic (M2), which has the same numeric values for a model that simulates half and twice the observed variability as follows:…”