The research area of climate field reconstructions has developed strongly during the past 20 years, motivated by the need to understand the complex dynamics of the earth system in a changing climate. Climate field reconstructions aim to build a consistent gridded climate reconstruction of different variables, often from a range of climate proxies, using either statistical tools or a climate model to fill the gaps between the locations of the proxy data. Commonly, large-scale climate field reconstructions covering more than 500 years are of annual resolution. In this method study, we investigate the potential of seasonally resolved climate field reconstructions based on oxygen isotope records from Greenland ice cores and an isotope-enabled climate model. Our analogue-type method matches modeled isotope patterns in Greenland precipitation to the patterns of ice core data from up to 14 ice core sites. In a second step, the climate variables of the best-matching model years are extracted, with the mean of the best-matching years comprising the reconstruction. We test a range of climate reconstructions, varying the definition of the seasons and the number of ice cores used. Our findings show that the optimal definition of the seasons depends on the variability in the target season. For winter, the vigorous variability is best captured when defining the season as December–February due to the dominance of large-scale patterns. For summer, which has weaker variability, albeit more persistent in time, the variability is better captured using a longer season of May–October. Motivated by the scarcity of seasonal data, we also test the use of annual data where the year is divided during summer, that is, not following the calendar year. This means that the winter variability is not split and that the annual data then can be used to reconstruct the winter variability. In particularly when reconstructing the sea level pressure and the corresponding main modes of variability, it is important to take seasonality into account, because of changes in the spatial patterns of the modes throughout the year. Targeting the annual mean sea level pressure for the reconstruction lowers the skill simply due to the seasonal geographical shift of the circulation modes. Our reconstructions based on ice core data also show skill for the North Atlantic sea surface temperatures, in particularly during winter for latitudes higher than 50^∘ N. In addition, the main modes of the sea surface temperature variability are qualitatively captured by the reconstructions. When testing the skill of the reconstructions using 19 ice cores compared to the ones using eight ice cores, we do not find a clear advantage of using a larger data set. This could be due to a more even spatial distribution of the eight ice cores. However, including European tree-ring data to further constrain the summer temperature reconstruction clearly improves the skill for this season, which otherwise is more difficult to capture than the winter season.

中文翻译：

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结合冰芯数据和同位素气候模型的季节性重建–季节性，气候模式和代理数据选择的方法学含义

在过去的20年中，由于需要了解不断变化的气候中地球系统的复杂动态，因此气候场重建研究领域得到了长足发展。气候场重建的目标是使用统计工具或气候模型来填补代理数据位置之间的空白，从而通常通过一系列气候代理来构建不同变量的一致网格化气候重建。通常，涵盖500多年的大规模气候场重建具有年度分辨率。在此方法研究中，我们根据格陵兰冰芯的氧同位素记录和启用同位素的气候模型，研究了季节性解析的气候场重建的潜力。我们的模拟方法将格陵兰岛降水中的同位素模式与多达14个冰芯站点的冰芯数据模式进行匹配。第二步，提取最佳匹配年份的气候变量，并以重建的最佳匹配年份的平均值为基础。我们测试了一系列的气候重建方法，改变了季节的定义和所使用的冰芯数量。我们的发现表明，季节的最佳定义取决于目标季节的可变性。对于冬季，由于大型模式的优势，当将季节定义为12月至2月时，可以很好地捕捉剧烈变化。对于夏季，尽管时间较持久，但变异性较弱，但使用5月至10月的较长季节可以更好地捕获变异性。由于季节性数据的稀缺性，我们还测试了年度数据的使用情况，即在夏季（即不跟随日历年）划分年份的年份。这意味着冬季可变性不会分裂，因此年度数据可用于重建冬季可变性。特别是在重建海平面压力和相应的主要变化模式时，重要的是要考虑到季节性，因为全年的模式空间格局都会发生变化。仅仅由于循环模式的季节性地理偏移，针对重建的年平均海平面压力降低了技能。我们基于冰芯数据的重建也显示了北大西洋海表温度的技巧，尤其是在冬季，纬度高于50^∘N  .此外，海面温度变化的主要模式通过重建定性地捕获。在测试使用19个冰芯的重建技巧与使用8个冰芯的重建技巧时，我们没有发现使用较大数据集的明显优势。这可能是由于八个冰芯的空间分布更加均匀。但是，包括欧洲树木年轮数据 进一步限制夏季温度的重建明显提高了本季的技术水平，否则比冬季更难捕获。