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Historical Ocean Surface Temperatures: Adjustment, Characterisation and Evaluation (HOSTACE)

Historical Ocean Surface Temperatures: Adjustment, Characterisation and Evaluation (HOSTACE)
历史海洋表面温度:调整、表征和评估 (HOSTACE)
批准号:
NE/J02306X/1
负责人:
Christopher Merchant
金额:
$50.79万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2012
资助国家:
英国
项目状态:
已结题
起止时间:
2012 至 --

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中文摘要
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英文摘要
The surface temperature of the land and sea is the main measure of "global warming". Measurements of sea surface temperature (SST) have been made for more than 200 years, first on sailing ships, now on a mixture of ships and buoys (drifting and moored). Technology has changed dramatically over this period, raising serious questions about whether technology changes over time give a misleading impression of how the temperature has changed - and therefore how climate has changed. People first measured the temperature of a seawater sample hauled up in a wooden bucket. Buckets are now made of insulating rubber. Most direct SST measurements are now sent via satellites from drifting buoys. Many other measurement methods have also been used. Different methods don't yield precisely the same SST values, and because global warming is a gradual change, these subtle discrepancies (or "biases") could distort our picture about the timing and magnitude of global warming. So, we must be sure that we understand how the different methods used to measure SST have affected the observations.These biases in SST have been a known problem for years, so why do we believe we can solve it? One reason is that recently many more observations have been retrieved from historical sources. Many ships' logbooks containing weather observations have been digitised. This has nearly doubled the number of observations before World War 2. Another reason is new, stable observations of SST from sensors on satellites orbiting Earth. Most satellite sensors give a detailed picture of patterns in SST and are tuned to drifting buoy SSTs to give reasonable accuracy. But compared to the subtle trends of global warming, they are not stable enough from year to year and across large distances. New high-quality SST measurements from a reworking of the SST measurements of a particular series of sensors are accurate and stable enough. Even better, they do not rely on ship or buoy SST observations, so we can use them as an independent point of reference. A major challenge is that the biases in SST made on ships are different for different measurement methods and we don't always know what methods were used. But we do know how we expect the biases for each method to vary with factors like the amount of heating by the Sun and wind speed. We will use these variations of the biases for each ship or buoy to assign measurement methods to observations (or, where it is not clear cut, the likelihood that the method is one or another type). E.g., we might be 80% confident that a particular ship used a canvas bucket to sample the water, but allow a 20% chance that a wooden bucket was used. We can then adjust for the expected biases according to method, and indicate how uncertain our adjustment may be. The next step will be to combine the scattered observations into maps of monthly average SST over the whole ocean. We must also calculate our degree of uncertainty in these monthly maps. There are few observations in the 19thC, so a global SST map requires sophisticated gap-filling methods. The final step is to compare our maps of SST with those produced by other scientists. Normally when such comparisons are made it is hard to understand the source of differences between the datasets. Was it due to different input data? Or different bias adjustments? Or the way the gaps were filled? Collaborating with other dataset producers, we will separate these different effects. For example, we will all use identical inputs, and isolate the effects of different gap-filling methods. This will also test our the uncertainty estimates - if important factors affecting the SST biases have been missed, then estimates of uncertainty may be too small to explain the differences between the SST maps produced by different groups.Such problems can mislead us in interpreting climate changes. We will use the new SST history to reassess explanations of phases of climate warming during in the 20th C.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1002/2017gl076475
发表时间: 2018-01-16
期刊: GEOPHYSICAL RESEARCH LETTERS
影响因子: 5.2
作者: [Carella, G., Kennedy, J. J., Kent, E. C.]
通讯作者: Kent, E. C.
DOI: 10.1016/j.rse.2018.12.015
发表时间: 2019-03-01
期刊: REMOTE SENSING OF ENVIRONMENT
影响因子: 13.5
作者: [Fiedler, Emma K., McLaren, Alison, Donlon, Craig]
通讯作者: Donlon, Craig
Demonstrating the potential of real-time EO for hydrological situation monitoring and early warning in the Sentinel era
  • 批准号:
    NE/N020499/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $0.42万
  • 财政年份:
    2016
  • 负责人:
    Christopher Merchant
  • 依托单位:
Global Observatory of Lake Responses to Environmental Change (GloboLakes)
  • 批准号:
    NE/J023345/2
  • 项目类别:
    Research Grant
  • 资助金额:
    $31.93万
  • 财政年份:
    2013
  • 负责人:
    Christopher Merchant
  • 依托单位:
Research Network for Surface Temperature
  • 批准号:
    NE/I030127/2
  • 项目类别:
    Research Grant
  • 资助金额:
    $14.48万
  • 财政年份:
    2013
  • 负责人:
    Christopher Merchant
  • 依托单位:
Historical Ocean Surface Temperatures: Adjustment, Characterisation and Evaluation (HOSTACE)
  • 批准号:
    NE/J02306X/2
  • 项目类别:
    Research Grant
  • 资助金额:
    $45.66万
  • 财政年份:
    2013
  • 负责人:
    Christopher Merchant
  • 依托单位:
国内基金
海外基金
Identification and quantification of primary phytoplankton functional types in the global oceans from hyperspectral ocean color remote sensing
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    160万元
  • 批准年份:
    2022
  • 负责人:
    李忠平
  • 依托单位: