A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California - Part I: Model evaluation

被引:3
|
作者
Najibi, Nasser [1 ,3 ]
Perez, Alejandro J. [2 ]
Arnold, Wyatt [2 ]
Schwarz, Andrew [2 ]
Maendly, Romain [2 ]
Steinschneider, Scott [1 ]
机构
[1] Cornell Univ, Dept Biol & Environm Engn, 111 Wing Dr,Riley Robb Hall, Ithaca, NY 14853 USA
[2] Calif Dept Water Resources, 715 P St, Sacramento, CA 95814 USA
[3] 111 Wing Dr,Riley Robb Hall 325, Ithaca, NY 14853 USA
关键词
Stochastic weather generator; Weather regimes; Climate change; Bottom; -up; Downscaling; Water resources; California; ATMOSPHERIC RIVERS; PROBABILISTIC FUNCTIONS; DAILY PRECIPITATION; UNITED-STATES; VARIABILITY; DATASET;
D O I
10.1016/j.cliser.2024.100489
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study is the first of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 1 of this series, we present the details of model development and validation. The model is based on the identification and simulation of weather regimes, or large-scale patterns of atmospheric flow, which are then used to condition the simulation of local, daily weather at a 6 km resolution across the state. We conduct a thorough validation of a baseline, 1000-year model simulation to evaluate its ability to accurately simulate daily precipitation and minimum and maximum temperature at various spatial scales (grid cell, river basin) and temporal scales (daily, event-based, monthly, annual, inter-annual to decadal). Results show that the model effectively reproduces a large suite of climate statistics at these scales across the entire state, including moments, spells, dry and wet extremes, and extreme hot and cold periods. Moreover, the model successfully maintains spatial correlations and inter-variable relationships, enabling the use of model simulations in hydrologic and water resources analyses that span multiple watersheds across California. The weather generator can simulate physically plausible extreme events (e.g., multi-day extreme precipitation and severe drought) that extend beyond the worst case conditions observed historically, independent of climate change. Thus, the baseline simulation can be used to understand the impacts of natural climate variability on both flood and drought risk in regional water systems. Scenarios of climate change are discussed in Part 2.
引用
收藏
页数:18
相关论文
共 7 条
  • [1] A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California - Part II: Thermodynamic and dynamic climate change scenarios
    Najibi, Nasser
    Perez, Alejandro J.
    Arnold, Wyatt
    Schwarz, Andrew
    Maendly, Romain
    Steinschneider, Scott
    CLIMATE SERVICES, 2024, 34
  • [2] A Weather-Regime-Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States
    Steinschneider, Scott
    Ray, Patrick
    Rahat, Saiful Haque
    Kucharski, John
    WATER RESOURCES RESEARCH, 2019, 55 (08) : 6923 - 6945
  • [3] Influence of Meteorological Variables by Weather Generator to the Estimation of Biomass Carbon and Water Balance using Process-based Ecosystem Model
    Miyauchi, Tatsuya
    Machimura, Takashi
    Furubayashi, Tomoya
    Kondo, Shogo
    ECO-ENGINEERING, 2020, 32 (02) : 23 - 31
  • [4] Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis
    Tao, Fulu
    Yokozawa, Masayuki
    Zhang, Zhao
    AGRICULTURAL AND FOREST METEOROLOGY, 2009, 149 (05) : 831 - 850
  • [5] Bridging the Gap Between Top-Down and Bottom-Up Climate Vulnerability Assessments: Process Informed Exploratory Scenarios Identify System-Based Water Resource Vulnerabilities
    Kucharski, J.
    Steinschneider, S.
    Herman, J.
    Olszewski, J.
    Arnold, W.
    Rahat, S.
    Maendly, R.
    Ray, P.
    WATER RESOURCES RESEARCH, 2024, 60 (11)
  • [6] A new process-based and scale-aware desert dust emission scheme for global climate models - Part I: Description and evaluation against inversemodeling emissions
    Leung, Danny M.
    Kok, Jasper F.
    Li, Longlei
    Okin, Gregory S.
    Prigent, Catherine
    Klose, Martina
    Garcia-Pando, Carlos Perez
    Menut, Laurent
    Mahowald, Natalie M.
    Lawrence, David M.
    Chamecki, Marcelo
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2023, 23 (11) : 6487 - 6523
  • [7] A new process-based and scale-aware desert dust emission scheme for global climate models - Part II: Evaluation in the Community Earth System Model version 2 (CESM2)
    Leung, Danny M.
    Kok, Jasper F.
    Li, Longlei
    Mahowald, Natalie M.
    Lawrence, David M.
    Tilmes, Simone
    Kluzek, Erik
    Klose, Martina
    Garcia-Pando, Carlos Perez
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2024, 24 (04) : 2287 - 2318