Suppr超能文献

基于 CMIP6 模型的婆罗洲气候带的观测和未来变化。

Observed and future shifts in climate zone of Borneo based on CMIP6 models.

机构信息

Centre for Environmental Sustainability and Water Security (IPASA), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Sekudai, Johor, Malaysia; Department of Water & Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.

Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.

出版信息

J Environ Manage. 2024 Jun;360:121087. doi: 10.1016/j.jenvman.2024.121087. Epub 2024 May 11.

Abstract

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

摘要

气候变化显著改变了气候带的特征,给生态系统和生物多样性带来了巨大挑战,尤其是在婆罗洲,其单位面积内的物种密度非常高。本研究旨在根据长期平均行为将该地区划分为同质气候组。利用了高分辨率每日网格化普林斯顿气候数据集(1950-2014 年)中的最有效参数,包括降雨、相对湿度(RH)、温度(Tavg、Tmin、Tmax 和昼夜温差(DTR))以及 0.25°分辨率的海拔数据。FCM 聚类方法在多标准评估基础上对婆罗洲气候区进行分类时,优于 K-Mean 和两种 Ward 分层方法(WardD 和 WardD2),表现为所有气候参数的平均距离最低(2.172-2.180)和基于补偿规划指数(CPI)的聚类平均值相关性排名最高。婆罗洲的气候区分为四类:“潮湿和寒冷”(WC)和“潮湿”(W)代表较潮湿的区域,以及“潮湿和炎热”(WH)和“干燥和炎热”(DH)代表较炎热的区域,每个区域都有明确的边界。对于未来预测,在 961 个网格点的所有气候参数中,EC-Earth3-Veg 排名第一,是表现最佳的模型。线性缩放(LS)校正后的 EC-Earth3-Veg 模型,如泰勒图所示,与观测数据集密切吻合,有助于未来气候区的重新分类。根据 MAE(35.8-94.6%)、MSE(57.0-99.5%)、NRMSE(42.7-92.1%)、PBIAS(100-108%)、MD(23.0-85.3%)、KGE(21.1-78.1%)和 VE(5.1-9.1%),模型性能得到了明显提高,对经验概率分布函数(PDF)曲线的复制也更加准确。在未来,婆罗洲的气候区将发生显著变化,WC 沿着山脉的脊柱向南延伸,W 在中北部山脉上形成一个飞地,WH 向北和内陆收缩,DH 沿着西海岸向北扩展。在 SSP5-8.5 情景下,WC 在中期和远期中分别预计将扩大 39%和 11%,而 W 将收缩 46%。WH 预计在中期和远期中将分别扩大 2%和 8%。相反,DH 预计在远期中将扩大 43%,但在中期将收缩 42%。本研究通过增加有效参数数量并利用 CMIP6 情景下的先进聚类方法(FCM)对婆罗洲气候区进行重新定义,填补了这一空白,并预测了未来的变化。重要的是,本研究通过在多个气候参数上使用 RIMs 和 CPI 对 GCM 进行排名,填补了 GCM 评估的一个先前空白,为 GCM 评估做出了贡献。本研究的结果可以通过提供对气候动态的共同理解并为联合环境管理和灾害应对工作提供信息,促进跨境合作。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验