Description: A regional coupled atmosphere–ocean model is developed to study the monsoon climate over South Asia. Most of the climate models (both GCM and RCM) underestimate precipitation over South Asia, but overestimate precipitation over the Bay of Bengal and the equatorial Indian Ocean. These systematic differences between the models may be related to a fundamental problem of atmospheric models: the inability to simulate intraseasonal variability. The intraseasonal oscillations of the South Asian monsoon play a major role in influencing the seasonal mean monsoon characteristics and their interannual variability (Goswami and Mohan, 2001). Several GCM studies with focus on the South Asian monsoonal region have concluded that GCMs have difficulties in simulatingthe mean monsoon climate (Turner and Annamalai, 2012). RCMs do simulate better orographic induced precipitation, but also show limited ability to simulate the land precipitation (Lucas-Picher et al., 2011; Kumar et al., 2013). For this study, differences in coupled and uncoupled simulations are analyzed to investigate the effect of coupling on the simulated climate, especially precipitation spatial patterns.

Description: Currently, Global Coupled Models (GCMs) have difficulty capturing key phenomena and achieving accurate climate projections on regional and local scales because limitations in computer po wer do not allow them to reach the necessary horizontal resolutions. Regional climate models (RCMs) provide dynamically downscaled climate information within the region of interest, improving this drawback of current GCMs. At this point, naturally raises the question of how much, if any, the RCM can improve the GCMs results. It has been argued that regional models can reproduce an observed climatology but are not able to predict the change of the climatology in response to a changing climate (e.g. Kerr, 2013). However, Feser et al. (2011) could demonstrate an added value in those parameters that exhibit high spatial variability such as near surf ace temperature in different regional atmospheric models. They show that the added value originates mainly from the higher resolved orography in the regional models.

Description: The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an ‘internal mode’ of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to ‘internal variability’ within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on ‘geographic’ reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system.