Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

This study introduces a new flow‐dependent distribution sampling (FDDS) scheme for air–sea coupling. The FDDS scheme is implemented in a climate model and used to improve the simulated mean and variability of atmospheric and oceanic surface fields and thus air–sea fluxes. Most coupled circulation models use higher resolutions in the sea ice and ocean compared to the atmospheric model component, thereby explicitly simulating the atmospheric subgrid‐scale at the interface. However, the commonly applied averaging of surface fields and air–sea fluxes tends to smooth fine‐scale structures, such as oceanic fronts. The stochastic FDDS scheme samples the resolved spatial ocean (and sea ice) subgrid distribution that is usually not visible to a coarser‐resolution atmospheric model. Randomly drawn nodal ocean values are passed to the corresponding atmospheric boxes for the calculation of surface fluxes, aiming to enhance surface flux variability. The resulting surface field perturbations of the FDDS scheme are based on resolved dynamics, displaying pronounced seasonality with realistic magnitude. The AWI Climate Model is used to test the scheme on interannual time‐scales. Our set‐up features a high ocean‐to‐atmosphere resolution ratio in the Tropics, with grid‐point ratios of about 60:1. Compared to the default deterministic averaging, changes are largest in the Tropics leading to an improved spatial distribution of precipitation with bias reductions of up to 50%. Enhanced sea‐surface temperature variability in boreal winter further improves the seasonal phase locking of temperature anomalies associated with the El Niño–Southern Oscillation. Mean 2m temperature, sea ice thickness and concentration react with a contrasting dipole pattern between hemispheres but a joint increase of monthly and interannual variability. This first approach to implement a flow‐dependent stochastic coupling scheme shows considerable benefits for simulations of global climate, and various extensions and modifications of the scheme are possible

Examining the relationship between daily changes in support and smoking around a self-set quit date

This study was funded by the Swiss National Foundation (100014_124516). We would like to thank all students who helped with data collection.Peer reviewedPostprin

Autonomous motivation is not enough: The role of compensatory health beliefs for the readiness to change stair and elevator use

Compensatory health beliefs (CHBs) are beliefs that an unhealthy behavior can be compensated with a healthy behavior. In line with the CHBs model, the aim of this study was twofold. First, the study investigated the relationship between autonomous motivation and CHBs that physical inactivity can be compensated by taking the stairs instead of the elevator. Second, the study focused on the associations between CHBs and readiness to use the stairs more often and stair and elevator use. Thus, a cross-sectional online questionnaire was designed that was filled out by 135 participants. Path analysis showed that individuals with stronger autonomous motivation to use the stairs strongly agreed that sedentary behavior could be compensated by taking the stairs instead of the elevator. Moreover, CHBs were positively related to readiness to change behavior, but not to self-reported stair and elevator use. Even though future research is necessary to replicate these findings, autonomous motivation seems to have a positive impact on CHBs which, in turn, might boost an intended behavior change. Thus, promoting possible compensation of physical inactivity might foster the readiness to change the unhealthy behavio

A simple ocean performance metrics applied to historical CMIP5 simulations

While in atmosphere models it is already common to define objective metrics to investigate how well an atmospheric model performs compared to observations, this is not too common for ocean models. Here we define a simple metrics encompassing the 3D structure of bias and absolute error to estimate the performance of ocean models and we apply it to the historical CMIP5 simulations from 1950 to 2005. Ocean model 3D temperature and salinity fields are compared to the PHC climatology for the major ocean basins. For each 3D grid point of the PHC dataset bias and absolute error of the model climatology are calculated and then volume- averaged over each ocean basin. An average CMIP5 model error is calculated for each ocean basin and used as a reference when investigating a particular model - similarly as has been done for the atmosphere by Reichler and Kim (2008) for CMIP3 models. Ocean surface temperature is generally reasonably well simulated by CMIP5 models and mean absolute errors amount to around 1 K which is comparable to the interannual variability. But in 500 to 1000 m - depending on the ocean basin and on the model - mean absolute errors of up to 4 K are detected which clearly exceed the interannual variability of generally below 1 K. For salinity mean absolute errors are in all levels clearly higher than the interannual variability. For example at the surface the mean absolute error amounts to up to 1 psu while the interannual variability is below 0.2 psu. Even if investigating biases which allows for cancelling out of errors within a basin instead of the mean absolute error this statement still holds in many cases. This means that there is a lot of scope for improvement of the simulation of the vertical structure of the ocean