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

陆地和海洋的表面温度是衡量“全球变暖”的主要指标。海洋表面温度(SST)的测量已经有200多年的历史了，最初是在帆船上进行的，现在是在船舶和浮标(漂浮和系泊)的混合体上进行的。在这段时间里，技术发生了戏剧性的变化，这引发了一些严肃的问题，即随着时间的推移，技术的变化是否会误导人们对气温是如何变化的，从而也就是气候是如何变化的。人们首先测量了装在木桶里的海水样本的温度。水桶现在是由绝缘橡胶制成的。现在，大多数直接的SST测量都是通过卫星从漂浮的浮标上发送的。还使用了许多其他测量方法。不同的方法得出的SST值并不完全相同，而且因为全球变暖是一个渐进的变化，这些细微的差异(或“偏差”)可能会扭曲我们关于全球变暖的时间和幅度的图景。因此，我们必须确保我们了解用于测量SST的不同方法是如何影响观测的。SST中的这些偏差多年来一直是一个已知的问题，那么为什么我们相信我们可以解决它？其中一个原因是，最近从历史资料中检索到了更多的观察结果。许多包含天气观测的船舶航海日志已被数字化。这几乎是二战前观测次数的两倍。另一个原因是地球轨道卫星上的传感器对SST进行了新的、稳定的观测。大多数卫星传感器给出了SST模式的详细图像，并调整到漂流浮标SST，以提供合理的精度。但与全球变暖的微妙趋势相比，它们在年复一年和大范围内还不够稳定。通过对特定系列传感器的SST测量进行重新处理，新的高质量SST测量足够准确和稳定。更好的是，它们不依赖于船舶或浮标SST观测，因此我们可以将它们用作独立的参照点。一个主要的挑战是，对于不同的测量方法，船舶上产生的SST偏差是不同的，我们并不总是知道使用了什么方法。但我们确实知道，我们预计每种方法的偏差会随着太阳供热量和风速等因素的不同而变化。我们将使用每艘船或浮标的这些偏差变化来为观测分配测量方法(或者，在不明确的情况下，分配方法是一种或另一种类型的可能性)。例如，我们可能有80%的把握认为某艘船是用帆布桶取的水，但有20%的可能是用了木桶。然后我们可以根据方法对预期的偏差进行调整，并指出我们的调整可能有多不确定。下一步将是将分散的观测结果合并成全海洋月平均SST图。我们还必须计算这些月度地图中的不确定性程度。19thC的观测数据很少，因此全球SST图需要复杂的填补缺口的方法。最后一步是将我们的SST地图与其他科学家绘制的地图进行比较。通常，当进行这样的比较时，很难理解数据集之间差异的来源。是因为输入数据不同吗？或者不同的偏差调整？还是填补空白的方式？通过与其他数据集生产者合作，我们将分离这些不同的效果。例如，我们都将使用相同的输入，并隔离不同填充方法的效果。这也将考验我们的不确定性估计--如果遗漏了影响SST偏差的重要因素，那么不确定性估计可能太小，无法解释不同群体绘制的SST图之间的差异。这类问题可能会误导我们解释气候变化。我们将使用新的SST历史来重新评估对20摄氏度期间气候变暖阶段的解释。

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.