Regional Study of Future Temperature and Precipitation Changes Using Bias Corrected Multi-Model Ensemble Projections Considering High Emission Pathways

Abstract

IntroductionGlobal change of atmospheric temperature and precipitation patterns can have an adverse impact on both natural and human systems [1,2] Analysis of observed data showed a 0.6°C increase in average global temperature since the late 19 th century. The 5 th assessment report from IPCC (IPCC-AR5) also projected the potential for temperature rises of up to 4.8°C and sea level rise of up to 0.82 m by 2100 [3]. Potential impacts at the local and regional scale are a key concern to the scientific community. Changing climate at regional scales affect fundamental aspects of our life, including health and welfare, economy, and natural ecosystems. Evaluation of climate change is needed at a much higher spatial and temporal resolution for accurate impact assessment [4][5][6][7]. Effects of climate change at the global scale are already occurring in the forms of sea-ice loss, sea level rise, acute heat waves, etc. The state of New Jersey, USA lies along the east coast and the threat of sea level rise makes this state vulnerable to future climate change scenarios. Climate change will aggravate events such as flooding, storm damage, and intense heat or cold waves which in turn will lead to detrimental effects upon the increasing population and infrastructure development of the state. Thus, impact assessment based on climate change has increased significance for a vulnerable region like New Jersey.General Circulation Models or GCMs which simulate physical processes in the atmosphere, ocean as well as for land surface considering the response of the global climate system due to increasing greenhouse gas concentrations. A fully coupled atmospheric-ocean general circulation model (AOGCM) comprises of an atmospheric GCM (AGCM) and an ocean GCM (OGCM). GCMs depict the global climate typically having a horizontal resolution of between 250 and 1000 km. The complexity of the GCMs and need for long term ensemble scenarios result in high computing cost. To avoid that, GCMs usually adopt relatively coarse resolution grid spacing which result in inappropriate representation of topography and local climate [8,9]. Various hydrological processes such as radiation, convection, cloud microphysics etc. occur mainly on a finer scale. Due to their coarse resolution, they do not provide full representation of the actual regional climate scenario required for impact analysis. Therefore, downscaling of the coarse resolution GCM variables to regional scale is essential for better representation of regional climate [10,11]. Among two techniques of downscaling the climate variables from GCMs i.e., statistical and dynamical [12], the statistical downscaling techniques focus on developing quantitative relationships between atmospheric variables of coarse resolution and finer regional resolution [13]. In contrast, dynamic downscaling method uses regional climate models (RCMs) that are developed based on the same principles of dynamical and physical processes as GCMs but with a much finer resolution (10-50 km) that better capture the regional clim...