A projection of extreme climate events in the 21st century over east Asia using the community climate system model 3

Abstract

A series of coupled atmosphere-ocean-land global climate model (GCM) simulations using the National Center for Atmospheric Research (NCAR) Community Climate System Model 3 (CCSM3) has been performed for the period 1870-2099 at a T85 horizontal resolution following the GCM experimental design suggested in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). First, a hindcast was performed using the atmospheric concentrations of three greenhouse gases (CO 2 , CH 4 , N 2 O) specified annually and globally on the basis of observations for the period 1870-1999. The hindcast results were compared with observations to evaluate the GCM's reliability in future climate simulations. Second, climate projections for a 100-year period were made using six scenarios of the atmospheric concentrations of the three greenhouse gases according to the A1FI, A1T, A1B, A2, B1, and B2 emission profiles of the Special Report on Emissions Scenarios. The present CCSM simulations are found to be consistent with IPCC's AR4 results in the temporal and spatial distributions for both the present-day and future periods. The GCM results were used to examine the changes in extreme temperatures and precipitation in East Asia and Korea. The extreme temperatures were categorized into warm and cold events: the former includes tropical nights, warm days, and heat waves during summer (June-July-August) and the latter includes frost days, cold days, and cold surges during winter (December-January-February). Focusing on Korea, the results predict more frequent heat waves in response to future emissions: the projected percentage changes between the present day and the late 2090s range from 294% to 583% depending on the emission scenario. The projected global warming is predicted to decrease the frequency of cold extreme events; however, the projected changes in cold surge frequency are not statistically significant. Whereas the number of cold surges in the A1FI emission profile decreases from the present-day value by up to 24%, the decrease in the B1 scenario is less than 1%. The frequency and intensity of extreme precipitation events year-round were examined. Both the frequency and the intensity of these events are predicted to increase in the region around Korea. The present results will be helpful for establishing an adaptation strategy for possible climate change nationwide, especially extreme climate events, associated with global warming.

Projection of future hot weather events and potential population exposure to this in South Korea

“…IAF contains frequency information associated with hot weather intensity and duration, which is closely related to mortality (Son et al 2012). Ho et al (2011) also estimated the projection of heat waves. Fig.…”

“…(2) There are uncertainties associated with the climate model schemes and downscaling method, which requires further study, including comparisons with multi-model scenarios, including those from the KMA (Jeung et al 2013, Park et al 2013, because the projection of heat wave mortality could be highly variable, depending on the exact climate model used (Ho et al 2011, Peng et al 2011.…”

“…The climate model results inherently contain uncertainties, which can be amplified during the downscaling (Ho et al 2011). It is agreed that downscaled surface variables, such as precipitation and temperature, should be comparable to observations to ascertain credibility in the projected scenarios .…”

Hot Spots of Climate Extremes in the Future

“…For RECCI in JJA, the three most prominent contributors to the RECCI are TX10, TX90 and TN90 in both 2016-2035 Morak et al (2011) attributed the increase in the frequency of TN90 over East Asia to both the anthropogenic influences and internal variability during 1950-2000. Ho et al (2011) reported that the frequency of TX90 would increase throughout the 21st century over East Asia in the context of global warming. Similar as in JJA, the three most important factors for RECCI will also change with time in DJF.…”

Water Resource Vulnerability Characteristics by District’s Population Size in a Changing Climate Using Subjective and Objective Weights

“…The climate exposure data were acquired from the climate model and hydrologic model simulations under the A1B climate change scenario. These climate data were set by downscaling the outputs from the National Center for Atmospheric Research (NCAR) Community Climate System Model 3 with the PSU/NCAR Mesoscale Model (MM5) [34,35]. The outputs from the MM5 were used to derive hydrologic models of the land-surface process model and a hydrologic simulation program in FORTRAN, which in turn provided the hydrologic data.…”