Measures of extreme climate variability around Chesapeake Bay have changed over the past century (1895–2014), resulting in the need to establish new baselines for understanding future change. Here, observed climate variability is compared with Coupled Model Intercomparison Project fifth assessment climate models to evaluate ensemble model skill in this region. Observed trends include annual and seasonal declines in the percentage of cold days. Similarly, increases in the annual and seasonal percentages of warm days occurred in all seasons of the North Chesapeake (>38.2°N) but were only significant in spring and summer in the South (<38.2°N) demonstrating regional differences even at local scales. Precipitation intensity increased over the past century. Models using emissions scenarios RCP4.5 and 8.5 project these trends will continue, though they have little skill in precipitation extremes. Pacific and Atlantic climate modes are generally more correlated with climate extremes than mean temperature and precipitation suggesting potential predictability in the synoptic patterns underlying the extremes. However, they also drive the need for time series at least 60-years long for establishing climate trends. This local analysis differs from regional, state level, and local 2.5° × 3.75° grid analyses, highlighting the importance of local climate assessments that consider topographic and regional weather patterns.

中文翻译：

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切萨皮克湾周围的极端气候和变化：过去、现在和未来

过去一个世纪（1895-2014 年），切萨皮克湾周围极端气候变率的测量发生了变化，因此需要建立新的基线来了解未来的变化。在此，将观测到的气候变率与耦合模型比对项目第五次评估气候模型进行比较，以评估该地区的集合模型技能。观察到的趋势包括寒冷天数百分比的年度和季节性下降。同样，北切萨皮克 (>38.2°N) 的所有季节都出现了暖日的年度和季节百分比增加，但仅在南部 (<38.2°N) 的春季和夏季显着增加，这表明即使在局部尺度上也存在区域差异. 过去一个世纪降水强度增加。使用排放情景 RCP4.5 和 8.5 的模型预测这些趋势将继续，尽管他们在极端降水方面缺乏技能。与平均温度和降水相比，太平洋和大西洋气候模式与极端气候的相关性通常更高，这表明极端天气模式具有潜在的可预测性。然而，它们也推动了对至少 60 年时间序列的需求，以建立气候趋势。这种本地分析不同于区域、州级和本地 2.5° × 3.75° 网格分析，强调了考虑地形和区域天气模式的本地气候评估的重要性。它们还推动需要至少 60 年的时间序列来确定气候趋势。这种本地分析不同于区域、州级和本地 2.5° × 3.75° 网格分析，强调了考虑地形和区域天气模式的本地气候评估的重要性。它们还推动需要至少 60 年的时间序列来确定气候趋势。这种本地分析不同于区域、州级和本地 2.5° × 3.75° 网格分析，强调了考虑地形和区域天气模式的本地气候评估的重要性。