Armineh Barkhordarian

Consistency of observed near surface temperature trends with climate change projections over the Mediterranean region

We examine the possibility that anthropogenic forcing (Greenhouse gases and Sulfate aerosols, GS) is a plausible explanation for the observed near-surface temperature trends over the Mediterranean area. For this purpose, we compare annual and seasonal observed trends in near-surface temperature over the period from 1980 to 2009 with the response to GS forcing estimated from 23 models derived from CMIP3 database. We find that there is less than a 5% chance that natural (internal) variability is responsible for the observed annual and seasonal area-mean warming except in winter. Using additionally two pattern similarity statistics, pattern correlation and regression, we find that the large-scale component (spatial-mean) of the GS signal is detectable (at 2.5% level) in all seasons except in winter. In contrast, we fail to detect the small-scale component (spatial anomalies about the mean) of GS signal in observed trend patterns. Further, we find that the recent trends are significantly (at 2.5% level) consistent with all the 23 GS patterns, except in summer and spring, when 9 and 5 models respectively underestimate the observed warming. Thus, we conclude that GS forcing is a plausible explanation for the observed warming in the Mediterranean region. Consistency of observed trends with climate change projections indicates that present trends may be understood of what will come more so in the future, allowing for a better communication of the societal challenges to meet in the future.

Detection and Attribution

We present a first assessment of the detection of a signal of temperature change over the Mediterranean domain, using HadCRUT3v observation dataset and model outputs from the CMIP3 climate simulations. For this study we have used two new formal detection methodologies, the ‘Regularized Optimal Fingerprint’ and the ‘Temporal Optimal Detection’, developed within the context of the CIRCE project and aiming at improving the ability to detect a climate change signal at the regional scale. We have also applied the ‘Consistency’ method that allows to answer the question whether a given forcing is a plausible explanation of an observed change. The results show the detection of a change on spatially centered temperatures, that allows to identify a regional structure of change additional to the global warming. The formal detection findings also extend to the winter and summer spatial patterns of temperature change. By applying the ‘Consistency’ method, we also detect the GS (Greenhouse gases and Sulfate aerosol) signal in observed annual and seasonal area-mean warming except in winter. Further we find that the recent trends in near-surface temperature are significantly consistent with the simulated GS patterns. Concerning precipitation, we cannot detect formally a signal of climate change on Mediterranean precipitation using 17 series of monthly precipitation from Croatian, French and Italian coastal stations and the CMIP3 climate simulations. However this may be due, at least partly, to the limited extent of the region covered with the precipitation series.

The Influence of Recurrent Modes of Climate Variability on the Occurrence of Monthly Temperature Extremes over South America

The associations between extreme temperature months and four prominent modes of recurrent climate variability are examined over South America. Associations are computed as the percent of extreme temperature months concurrent with the upper and lower quartiles of the El Nino–Southern Oscillation (ENSO), the Atlantic Nino, the Pacific Decadal Oscillation (PDO), and the Southern Annular Mode (SAM) index distributions, stratified by season. The relationship is strongest for ENSO, with nearly every extreme temperature month concurrent with the upper or lower quartiles of its distribution in portions of northwestern South America during some seasons. The likelihood of extreme warm temperatures is enhanced over parts of northern South America when the Atlantic Nino index is in the upper quartile, while cold extremes are often association with the lowest quartile. Concurrent precipitation anomalies may contribute to these relations. The PDO shows weak associations during December, January, and February, while in June, July, and August its relationship with extreme warm temperatures closely matches that of ENSO. This may be due to the positive relationship between the PDO and ENSO, rather than the PDO acting as an independent physical mechanism. Over Patagonia, the SAM is highly influential during spring and fall, with warm and cold extremes being associated with positive and negative phases of the SAM, respectively. Composites of sea level pressure anomalies for extreme temperature months over Patagonia suggest an important role of local synoptic scale weather variability in addition to a favorable SAM for the occurrence of these extremes.

An attempt to deconstruct recent climate change in the Baltic Sea basin

We investigate whether the recently observed temperature and precipitation trends over the Baltic Sea Basin are consistent with state-of-the-art regional climate model projections. To address this question we use several data sources: 1) multi-decadal trends derived from various observational data sets, 2) estimates of natural variability provided by a 2,000-year paleoclimatic model simulation, and 3) response to greenhouse gas forcing derived from regional climate simulations driven by the A1B and RCP4.5 scenarios (from ENSEMBLES and CORDEX projects). Results indicate that, over the past decades, the climate in the Baltic Sea Basin has undergone a change that is beyond the estimated range of natural variability. We test the hypothesis that this change may be understood as a manifestation of global warming due to increasing concentrations of greenhouse gases (GHGs). We find that changes in near-surface temperature support our hypothesis that the effect of GHG is needed to simulate the observed changes. The pattern correlation and regression results clearly illustrate the concerted emergence of an anthropogenic signal consistent with the GHG signal in summer and autumn in the 21st century. However, none of the 19 regional climate simulations used in this study reproduce the observed warming. The observed trends in precipitation and surface solar radiation are also partially inconsistent with the expected changes due to GHG forcing. We conclude that, besides the regional response to GHG forcing, other human-made drivers have had an imprint. Regional emission of industrial aerosols has been strongly reduced in this region, and we suggest that this reduction may be the missing driver.

Simultaneous Regional Detection of Land‐Use Changes and Elevated GHG Levels: The Case of Spring Precipitation in Tropical South America

A decline in dry season precipitation over tropical South America has a large impact on ecosystem health of the region. Results here indicate that the magnitude of negative trends in dry season precipitation in the past decades exceeds the estimated range of trends due to natural variability of the climate system defined in both the preindustrial climate and during the 850–1850 millennium. The observed drying is associated with an increase in vapor pressure deficit. The univariate detection analysis shows that greenhouse gas (GHG) forcing has a systematic influence in negative 30-year trends of precipitation ending in 1998 and later on. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and land-use change are attributed as key causes for the observed drying during 1983–2012 over the southern Amazonia and central Brazil. We further show that the effect of GS signal (GHG and sulfate aerosols) based on RCP4.5 scenario already has a detectable influence in the observed drying. Thus, we suggest that the recently observed “drier dry season” is a feature which will continue and intensify in the course of unfolding anthropogenic climate change. Such change could have profound societal and ecosystem impacts over the region. Plain Language Summary This study uses statistical techniques to attribute the recently observed “drier dry season” over tropical South America to external drivers of climate change, both human-induced and naturally occurring. A decline in dry season precipitation has a large impact on ecosystem health of the region. Thus, attributing the forced components of the observed “drier dry season” to external drivers of climate change is of great practical importance to societies. Results indicate that the observed drying is well beyond the range of trends due to natural variability of the climate system and is found to be systematically and externally forced. The forcing by elevated greenhouse gas levels and land-use change (mainly deforestation) are attributed as key causes for the observed drying over the southern Amazonia and central Brazil. We further demonstrate that the recently observed “drier dry season” is a feature which will continue and intensify in the course of unfolding anthropogenic climate change. Such an assessment is critical for adaptation planning and mitigation strategies.