Regional moisture change over India during the past Millennium: A comparison of multi-proxy reconstructions and climate model simulations

AbstractThe Indian Monsoon Variability during the past Millennium has been simulated with the ECHAM5 model in two different time slices: Medieval Climate Anomaly and the Little Ice Age. The simulations are compared with new centennial-resolving paleo-reconstructions inferred from various well-dated multi-proxies from two core regions, the Himalaya and Central India. A qualitative moisture index is derived from the proxies and compared with simulated moisture anomalies.The reconstructed paleo-hydrological changes between the Little Ice Age and the Medieval Climate Anomaly depict a dipole pattern between Himalaya and Central India, which is also captured by the model.In the Medieval Climate Anomaly the model exhibits stronger (weaker) dipole signals during summer (winter) compared to Little Ice Age. In summer (winter) months of “Medieval Climate Anomaly minus Little Ice Age” the model simulates wetter conditions over eastern (western and central) Himalaya. Over Central India, a simulated weakening of Indian Summer Monsoon during warmer climate is coincident with reconstructed drying signal in the Lonar Lake record.Based on the model simulations, we can differentiate three physical mechanisms which can lead to the moisture anomalies: (i) the western and central Himalaya are influenced by extra-tropical Westerlies during winter, (ii) the eastern Himalaya is affected by summer variations of temperature gradient between Bay of Bengal and Indian subcontinent and by a zonal band of intensified Indian–East Asian monsoon link north of 25°N, and (iii) Central India is dominated by summer sea surface temperature anomalies in the northern Arabian Sea which have an effect on the large-scale advection of moist air masses. The temperatures in the Arabian Sea are linked to the Indo Pacific Warm Pool, which modulates the Indian monsoon strength

Projections of northern hemisphere extratropical climate underestimate internal variability and associated uncertainty

Internal climate variability will play a major role in determining change on regional scales under global warming. In the extratropics, large-scale atmospheric circulation is responsible for much of observed regional climate variability, from seasonal to multidecadal timescales. However, the extratropical circulation variability on multidecadal timescales is systematically weaker in coupled climate 1 models. Here we show that projections of future extratropical climate from coupled model simulations significantly underestimate the projected uncertainty range originating from large-scale atmospheric circulation variability. Using observational datasets and large ensembles of coupled climate models, we produce synthetic ensemble projections constrained to have variability consistent with the large-scale atmospheric circulation in observations. Compared to the raw model projections, the synthetic observationally-constrained projections exhibit an increased uncertainty in projected 21st century temperature and precipitation changes across much of the Northern extratropics. This increased uncertainty is also associated with an increase of the projected occurrence of future extreme seasons

Assessing observational constraints on future European climate in an out-of-sample framework

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in the European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections by combining the constrained projections, which are found to be competitive with the best-performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods

Assessing observational constraints on future European climate in an out-of-sample framework

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections, by combining the constrained projections, which are found to be competitive with the best performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods

Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses