Quantifying Daytime Heating Biases in Marine Air Temperature Observations from Ships

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN
T. Cropper, D. Berry, R. Cornes, Elizabeth C. Kent
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引用次数: 0

Abstract

Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model which quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover and relative wind speed are then used to estimate the heating bias ship-by-ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.
量化船舶海洋气温观测中的日间加热偏差
众所周知,由于船舶上层结构的能量储存,白天记录的船舶空气温度平均偏暖。这使得未经调整的日间观测不适合许多应用,包括监测海洋长期温度变化。在本文中,基于物理学的方法被用于估计ICOADS的船舶观测中的这种加热偏差。在这种方法下,经验确定的系数表示热预算模型的能量传递项,该模型量化了加热偏差,并作为云量和单个船舶上的相对风速的函数应用。每艘船的系数都是从相对于夜间气温的异常昼夜加热中得出的。然后使用模型系数、云量和相对风速逐船估计加热偏差,并生成夜间等效时间序列。测试了各种方法。该方法的应用使基于海洋气温的气候记录中能够包含一些白天的观测结果,与排除白天观测的现有记录相比,可以更早地开始日期,并增加空间覆盖范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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