While many authors have described the adverse health effects of poor air quality and meteorological extremes, there remain inconsistencies on a regional scale as well as uncertainty about the single and joint effects of atmospheric predictors. In this context, we investigated the short-term impacts of weather and air quality on moderate extreme cancer-related mortality events for the urban area of Augsburg, Southern Germany, during the period 2000–2017. First, single effects were uncovered by applying a case-crossover routine. The overall impact was assessed by performing a Mann–Whitney U testing scheme. We then compared the results of this procedure to extreme noncancer-related mortality events. In a second step, we found periods with contemporaneous significant predictors and carried out an in-depth analysis of these joint-effect periods. We were interested in the atmospheric processes leading to the emergence of significant conditions. Hence, we applied the Principal Component Analysis to large-scale synoptic conditions during these periods. The results demonstrate a strong linkage between high-mortality events in cancer patients and significantly above-average levels of nitrogen dioxide (NO2) and particulate matter (PM2.5) during the late winter through spring period. These were mainly linked to northerly to easterly weak airflow under stable, high-pressure conditions. Especially in winter and spring, this can result in low temperatures and a ground-level increase and the accumulation of air pollution from heating and traffic as well as eastern lateral advection of polluted air. Additionally, above-average temperatures were shown to occur on the days before mortality events from mid-summer through fall, which was also caused by high-pressure conditions with weak wind flow and intense solar radiation. Our approach can be used to analyse medical data with epidemiological as well as climatological methods while providing a more vivid representation of the underlying atmospheric processes.

No abstract

Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

<p>Atmospheric water residence time, here defined as time between the original evaporation and the returning of its respective water masses to the land surface as precipitation, is <span lang="EN-US">a</span> measure of the speed of the atmospheric hydrological cycle. Traditional <span lang="EN-US">analytical </span>methods are generally limited by crude assumptions in the coupling between the land surface and the atmosphere<span lang="EN-US">, and hence are not applicable to</span> <span lang="EN-US">regions </span>with complex monsoon systems<span lang="EN-US"> under a changing climate. To this end, we have implemented the age-weighted water tracers into t</span>he Weather Research and Forecasting WRF model<span lang="EN-US">, namely, WRF-age,</span> <span lang="EN-US">to </span>follow the atmospheric water pathways and to derive atmospheric <span lang="EN-US">water </span>residence times<span lang="EN-US"> accordingly</span>. <span lang="EN-US">The newly developed, physics-based WRF-age is used to regionally downscale the reanalysis of ERA-Interim and the </span>MPI-ESM Representative Concentration Pathway 8.5 scenario (RCP8.5)<span lang="EN-US"> simulation for </span>an East Asian monsoon region<span lang="EN-US">, i.e., the Poyang Lake basin, for two 10-year slices of historical (1980-1989) and future (2040-2049) times. In comparison to the historical WRF-age simulation, the future 2-meter air temperature rises by 1.3 °C and precipitation decreases by 38% under RCP8.5 on average. In this context, global warming leads to decreased atmospheric residence times of the column-integrated water vapor (from 22 to 13 hours) and column-integrated condensed moisture (from 26 to 14 hours) in the atmosphere over the basin, but slightly increased atmospheric residence times of surface precipitation (from 12 to 15 hours) in agreement with reduced the precipitation amounts. Our findings demonstrate that global warming increases the complexity of regional atmospheric water cycle, especially the associated changes in the residence times of atmospheric water states of matter.</span></p>