Trend Analysis of Temperature and Precipitation Extremes during Winter Wheat Growth Period in the Major Winter Wheat Planting Area of China

The Impact of Foehn Wind on Mental Distress among Patients in a Swiss Psychiatric Hospital

Psychiatric patients are particularly vulnerable to strong weather stimuli, such as foehn, a hot wind that occurs in the alps. However, there is a dearth of research regarding its impact on mental health. This study investigated the impact of foehn wind among patients of a psychiatric hospital located in a foehn area in the Swiss Alps. Analysis was based on anonymized datasets obtained from routine records on admission and discharge, including the Brief Symptom Checklist (BSCL) questionnaire, as well as sociodemographic parameters (age, sex, and diagnosis). Between 2013 and 2020, a total of 10,456 admission days and 10,575 discharge days were recorded. All meteorological data were extracted from the database of the Federal Office of Meteorology and Climatology of Switzerland. We estimated the effect of foehn on the BSCL items using a distributed lag model. Significant differences were found between foehn and non-foehn admissions in obsession-compulsion, interpersonal sensitivity, depression, anxiety, phobic anxiety, paranoid ideation, and general severity index (GSI) (p < 0.05). Our findings suggest that foehn wind events may negatively affect specific mental health parameters in patients. More research is needed to fully understand the impact of foehn's events on mental health.

Impact of the dynamic vegetation on climate extremes during the wheat growing period over China

Extreme temperature and precipitation indices have important implications for the crop growing season. Whether a coupled regional model with carbon-nitrogen cycling (CN) and vegetation dynamics (DV) can better represent these indices during the growing season compared with a model without these modules remains unknown. This study evaluates the performance of extreme indices in three wheat planting regions (including northeast spring wheat, north winter wheat and south winter wheat regions) over China in the period of 1990-2009 using the Regional Climate Model (RegCM) coupled with the Community Land Model (CLM), which include CN and DV. The results show that relative to the RegCM-CLM, both the RegCM-CLM-CN and RegCM-CLM-CN-DV perform better in simulating summer days (SU), consecutive dry days (CDD), consecutive wet days (CWD), and the interannual variability in all the extreme indices in the three regions but produce larger biases on frost days (FD). The trends of extreme indices in the high-impact risk region of wheat are also better captured by the RegCM-CLM with CN or CN-DV compared with the model without these modules. In the northeast spring wheat and southern winter wheat regions, the greater cold bias of mean daily minimum temperature between RegCM-CLM-CN-DV and RegCM-CLM is consistent with the leaf area index (LAI) difference, which may increase evaporative cooling and thus increasing FD biases. Overestimation of the LAI may have a weaker effect than the surface albedo on the mean daily maximum temperature, leading to decreased SU biases in RegCM-CLM-CN-DV relative to RegCM-CLM.

Weather and Climate Extremes: Current Developments

An increasing number of extreme events have been observed around the world over the past few decades, some of them attributed to global warming [...]

Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.

Modeling Climate Change Impacts on Rice Growth and Yield under Global Warming of 1.5 and 2.0 °C in the Pearl River Delta, China

In this study, the potential climate change impacts on rice growth and rice yield under 1.5 and 2.0 °C warming scenarios, respectively, are simulated using the Ceres-Rice Model based on high-quality, agricultural, experimental, meteorological and soil data, and the incorporation of future climate data generated by four Global Climate Models (GCMs) in the Pearl River Delta, China. The climatic data is extracted from four Global Climate Models (GCMs) namely: The Community Atmosphere Model 4 (CAM4), The European Centre for Medium-Range Weather Forecasts-Hamburg 6 (ECHAM6), Model for Interdisciplinary Research On Climate 5 (MIROC5) and the Norwegian Earth System Model 1 (NorESM1). The modeling results show that climate change has major negative impacts on both rice growth and rice yields at all study sites. More specifically, the average of flowering durations decreases by 2.8 days (3.9 days), and the maturity date decreases by 11.0 days (14.7 days) under the 1.5 °C and (2.0 °C) warming scenarios, respectively. The yield for early mature rice and late mature rice are reduced by 292.5 kg/ha (558.9 kg/ha) and 151.8 kg/ha (380.0 kg/ha) under the 1.5 °C (2.0 °C) warming scenarios, respectively. Adjusting the planting dates of eight days later and 15 days earlier for early mature rice and late mature rice are simulated to be adaptively effective, respectively. The simulated optimum fertilizer amount is about 240 kg/ha, with different industrial fertilizer and organic matter being applied.