Di Cosmo, N., Wagner, S., & Büntgen, U. (2021): Climate and environmental context of the Mongol invasion of Syria and defeat at ‘Ayn Jālūt (1258–1260 CE). Erdkunde, 2021, Vol. 75, Issue 2, pp 87-104, doi:10.3112/erdkunde.2021.02.02
After a successful conquest of large parts of Syria in 1258 and 1259 CE, the Mongol army lost the battle of ‚Ayn Jālūt against Mamluks on September 3, 1260 CE. Recognized as a turning point in world history, their sudden defeat triggered the reconfiguration of strategic alliances and geopolitical power not only in the Middle East, but also across much of Eurasia. Despite decades of research, scholars have not yet reached consensus over the causes of the Mongol reverse. Here, we revisit previous arguments in light of climate and environmental changes in the aftermath of one the largest volcanic forcings in the past 2500 years, the Samalas eruption ~1257 CE. Regional tree ring-based climate reconstructions and state-of-the-art Earth System Model simulations reveal cooler and wetter conditions from spring 1258 to autumn 1259 CE for the eastern Mediterranean/Arabian region. We therefore hypothesize that the post-Samalas climate anomaly and associated environmental variability affected an estimated 120,000 Mongol soldiers and up to half a million of their horses during the conquest. More specifically, we argue that colder and wetter climates in 1258 and 1259 CE, while complicating and slowing the campaign in certain areas, such as the mountainous regions in the Caucasus and Anatolia, also facilitated the assault on Syria between January and March 1260. A return to warmer and dryer conditions in the summer of 1260 CE, however, likely reduced the regional carrying capacity and may therefore have forced a mass withdrawal of the Mongols from the region that contributed to the Mamluks’ victory. In pointing to a distinct environmental dependency of the Mongols, we offer a new explanation of their defeat at ‚Ayn Jālūt, which effectively halted the further expansion of the largest ever land-based empire.
Pyrina, M., Nonnenmacher, M., Wagner, S., & Zorita, E. (2021): Statistical seasonal prediction of European summer mean temperature using observational, reanalysis and satellite data. AMS Journals, doi:10.1175/WAF-D-20-0235.1
Statistical climate prediction has sometimes demonstrated higher accuracy than coupled dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) over Europe and sea surface temperatures (SSTs) of three North Atlantic (NA) regions as statistical predictors of European mean summer temperature (t2m). We set up two statistical-learning (SL) frameworks, based on methods commonly applied in climate research. The SL models are trained with gridded products derived from station, reanalysis, and satellite data (ERA-20C, ERA-Land, CERA, COBE2, CRU, and ESA-CCI). The predictive potential of SM anomalies in statistical forecasting had so far remained elusive. Our statistical models trained with SM achieve high summer t2m prediction skill in terms of Pearson correlation coefficient (r), with r≥0.5 over Central and Eastern Europe. Moreover, we find that the reanalysis and satellite SM data contain similar information that can be extracted by our methods and used in fitting the forecast models. Furthermore, the predictive potential of SSTs within different areas in the NA basin was tested. The predictive power of SSTs might increase, as in our case, when specific areas are selected. Forecasts based on extratropical SSTs achieve high prediction skill over South Europe. The combined prediction, using SM and SST predictor data, results in r≥0.5 over all European regions south of 50°N and east of 5°W. This is a better skill than the one achieved by other prediction schemes based on dynamical models. Our analysis highlights specific NA mid-latitude regions that are more strongly connected to summer mean European temperature.