인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC's sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
