首页> 外文期刊>Progress in Oceanography >Seasonal sea surface temperature anomaly prediction for coastal ecosystems
【24h】

Seasonal sea surface temperature anomaly prediction for coastal ecosystems

机译:沿海生态系统季节性海表温度异常的预测

获取原文
获取原文并翻译 | 示例
           

摘要

Sea surface temperature (SST) anomalies are often both leading indicators and important drivers of marine resource fluctuations. Assessment of the skill of SST anomaly forecasts within coastal ecosystems accounting for the majority of global fish yields, however, has been minimal. This reflects coarse global forecast system resolution and past emphasis on the predictability of ocean basin-scale SST variations. This paper assesses monthly to inter-annual SST anomaly predictions in coastal "Large Marine Ecosystems" (LMEs). We begin with an analysis of 7 well-observed LMEs adjacent to the United States and then examine how mechanisms responsible for prediction skill in these systems are reflected in predictions for LMEs globally. Historical SST anomaly estimates from the 1/4 degrees daily Optimal Interpolation Sea Surface Temperature reanalysis (OISST.v2) were first found to be highly consistent with in-situ measurements for 6 of the 7 U.S. LMEs. Thirty years of retrospective forecasts from climate forecast systems developed at NOAA's Geophysical Fluid Dynamics Laboratory (CM2.5-FLOR) and the National Center for Environmental Prediction (CFSv2) were then assessed against OISST.v2. Forecast skill varied widely by LME, initialization month, and lead but there were many cases of high skill that also exceeded that of a persistence forecast, some at leads greater than 6 months. Mechanisms underlying skill above persistence included accurate simulation of (a) seasonal transitions between less predictable locally generated and more predictable basin-scale SST variability; (b) seasonal transitions between different basin-scale influences; (c) propagation of SST anomalies across seasons through sea ice; and (d) re-emergence of previous anomalies upon the breakdown of summer stratification. Globally, significant skill above persistence across many tropical systems arises via mechanisms (a) and (b). Combinations of all four mechanisms contribute to less prevalent but nonetheless significant skill in extratropical systems. While continued refinement of global climate forecast systems and observations are needed to improve coastal SST anomaly prediction and extend predictions to other ecosystem relevant variables (e.g., salinity), present skill warrants close examination of forecasts for marine resource applications. Published by Elsevier Ltd.
机译:海面温度(SST)异常通常既是海洋资源波动的主要指标,也是重要的驱动因素。然而,对占全球鱼类总产量大部分的沿海生态系统中的SST异常预报技能的评估很少。这反映了粗略的全球预报系统分辨率,以及过去对海盆尺度海表温度变化可预测性的重视。本文评估了沿海“大型海洋生态系统”(LME)中每月至每年的SST异常预测。我们首先分析与美国相邻的7个观测良好的LME,然后研究全球这些LME的预测如何反映这些系统中负责预测技能的机制。最初发现,根据每天1/4度最佳插值海面温度再分析(OISST.v2)的历史SST异常估计,与美国7个LME中的6个的现场测量高度一致。然后根据OISST.v2对来自NOAA的地球物理流体动力学实验室(CM2.5-FLOR)和国家环境预测中心(CFSv2)开发的气候预测系统进行的30年回顾性预测进行了评估。预测技能因LME,初始化月份和潜在客户的不同而有很大差异,但是有很多高技能案例也超过了持久性预测,有些案例的潜在客户超过6个月。高于持久性的基本技能的机制包括:(a)难以预测的局部生成和更可预测的盆地尺度海表温度变化之间的季节转换; (b)不同流域尺度影响之间的季节性过渡; (c)通过海冰跨季节传播海表温度异常; (d)夏季分层破裂后,以前的异常再次出现。在全球范围内,通过机制(a)和(b)可以在许多热带系统中获得超越持久性的重要技能。这四种机制的组合有助于在温带系统中普及程度较低,但仍具有重要的技能。虽然需要继续完善全球气候预报系统和观测以改善沿海SST异常预测并将预测范围扩大到其他与生态系统有关的变量(例如盐度),但本技能需要对海洋资源应用的预测进行仔细检查。由Elsevier Ltd.发布

著录项

  • 来源
    《Progress in Oceanography》 |2015年第sepaptaa期|219-236|共18页
  • 作者单位

    Princeton Univ, NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08540 USA.;

    George Mason Univ, Dept Atmospher Ocean & Earth Sci, Fairfax, VA 22030 USA.;

    Princeton Univ, NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08540 USA.;

    NOAA, Earth Syst Res Lab, Div Phys Sci, Boulder, CO 80305 USA.;

    Princeton Univ, NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08540 USA.;

    Univ Washington, Joint Inst Study Atmos & Ocean, Seattle, WA 98115 USA.;

    NOAA, NMFS, Northeast Fisheries Sci Ctr, Woods Hole, MA 02543 USA.;

    Princeton Univ, NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08540 USA.;

    Inst Marine Res, N-5024 Bergen, Norway.;

    NOAA, CPC Natl Ctr Environm Predict, NCWCP, College Pk, MD 20740 USA.;

    Univ Corp Atmospher Res, Boulder, CO 80305 USA.;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号