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有可能预测未来100年的海洋盐度吗?

古盐度是由孔隙流体的氯度(Adkins 2002）.

当前数据由CORA数据集．

除了对主要问题的回答之外，我还想了解以下几点:

如何预测海洋的盐度?
预报误差和预报视界之间有什么关系?
有没有现有的全球盐度预报?
预测未来100年盐度的不确定性来源是什么?

回答

主要问题的答案是，我们有能力预测未来100年的盐度。更相关的问题可能是我们目前预测盐度的能力如何。首先，让我们看看目前我们所知道的盐度是如何变化的。全球盐度是地表淡水通量(蒸发减去降水)、陆地径流以及海洋中混合和平流过程之间平衡的结果。盐度的变化与全球水循环的变化有内在联系，预计是气候变化的结果([Held and Soden, 2006][1])。已经确定了一种普遍的变化，亚热带表面地区变得更咸，高纬度地区变得更新鲜([Antonov et al.， 2002][2];[Boyer et al.， 2005][3])。[Durack and Wijffels(2010)][4]发现在以蒸发为主的区域盐度增加，而在以降水为主的区域发生清新。他们认为，这种变化是全球水循环加剧的结果。这些结果是[IPCC 2013年报告]中结论的基础 Figure 1][6]). ![IPCC TFE.1, Figure 1 - Changes in sea surface salinity are related to the atmospheric patterns of evaporation minus precipitation (E – P) and trends in total precipitable water][7] The [global climate models][8] that are used to evaluate future climate scenarios are calibrated to reproduce the past ocean and atmospheric conditions, which includes trying to match the described salinity trend. They likely constitute the best source of future global salinity estimates. Their ocean salinity forecast is based on the estimation of the global hydrological cycle evolution, especially evaporation, precipitation, river discharge and ocean mixing. With regard to the quality of the salinity estimates, there is probably a large uncertainty associated with those fields. There is going to be substantial improvement in the quality of the salinity forecast because of the recent availability of better global salinity products. The remote data obtained from satellites ([SMOS][9] and [Aquarius][10]) have been providing a much better global view of salinity changes. Also, the ARGO profilers are expanding our understanding of the ocean interior. In terms of evaluating uncertainty, I like the approach of [Meier et al., 2006][11] looking at multi-model ensemble estimates. They combined down-scaled regional models with global simulations under several scenarios and provided several metrics for salinity uncertainties. The relationship between forecast errors and forecast horizons is quite complex and in fact represents an entire field of study. There is a lot of information in the [IPCC 2013 report][5] dealing with prediction quality, ensemble generation and size, and forecast quality assessment (for instance Chapter 11). [![salinity trend][12]][12] Source: [Aretxabaleta et al. (2017)][13] Using a little bit of self-promotion, I can also say that estimating what the current salinity trend from observations is a tough problem. In this recently published [article][13], we show that the changes in trend are spatially complex and changing in time. We argue that, as mentioned before, the changes in salinity field give a good representation of the long-term changes in the global hydrological cycle. [1]: http://dx.doi.org/10.1175/JCLI3990.1 [2]: http://dx.doi.org/10.1029/2001JC000964 [3]: http://dx.doi.org/10.1029/2004GL021791 [4]: http://dx.doi.org/10.1175/2010JCLI3377.1 [5]: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf [6]: http://www.greenfacts.org/en/climate-change-ar5-science-basis/figtableboxes/figure-tfe1.htm [7]: http://i.stack.imgur.com/t66rn.gif [8]: http://cmip-pcmdi.llnl.gov/cmip5/ [9]: https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/smos [10]: http://aquarius.nasa.gov/ [11]: http://dx.doi.org/10.1029/2006GL026488 [12]: https://i.stack.imgur.com/ed3BT.jpg [13]: http://www.mdpi.com/2077-1312/5/4/57/htm

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