Abstract. The flow of carbon through terrestrial ecosystems and the response to climate is a critical but highly uncertain process in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle’s response to land use and climate change. Uncertainty in temperature limitation on productivity pose a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on growing season onset of gross primary production (GPP) using nearly two decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado USA. We implement the CARDAMOM model-data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of integrated sensitivity of GPP to temperature – we introduced a cold temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperature below 6.0 °C (±2.6 °C) and completely inhibited below −7.1 °C (±1.1 °C). The addition of this scaling factor improved the model’s ability to replicate spring GPP at interannual and decadal time scales (r = 0.88), relative to the nominal CARDAMOM configuration (r = 0.47), and improved spring GPP model predictability outside of the data assimilation training period (r = 0.88) . While cold temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

中文翻译：

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使用模型数据融合框架解决常绿针叶林春季生产力的温度限制

摘要。通过陆地生态系统的碳流动和对气候的响应是全球碳循环中一个关键但高度不确定的过程。然而，随着大量原位和卫星数据的迅速扩大，有机会提高我们对碳 (C) 循环对土地利用和气候变化的响应的机制理解。温度限制对生产力的不确定性对预测生态系统碳通量对气候变化的响应构成了重大挑战。在这里，我们使用美国科罗拉多州亚高山常绿林近二十年的气象和碳通量数据（2000-2018）诊断和定量解决初级生产总值（GPP）生长季节开始的环境限制。我们实现了 CARDAMOM 模型-数据融合网络来解决弹簧 GPP 的温度敏感性。为了捕捉 GPP 温度限制——GPP 对温度的综合敏感性的一个关键组成部分——我们在 CARDAMOM 中引入了一个冷温标度函数来调节光合生产力。我们发现 GPP 在低于 6.0 °C (±2.6 °C) 的温度下逐渐被抑制，而在低于 -7.1 °C (±1.1 °C) 时被完全抑制。相对于标称 CARDAMOM 配置 (r = 0.47)，添加此比例因子提高了模型在年际和年代际时间尺度 (r = 0.88) 复制 spring GPP 的能力，并提高了数据同化训练之外的 spring GPP 模型可预测性期间 (r = 0.88) 。虽然低温限制对春季 GPP 有重要影响，它对综合生长季 GPP 没有显着影响，表明其他环境控制，如降水，在年生产力中发挥更重要的作用。这项研究强调了生长季节开始温度是冬季休眠常绿森林春季生长的关键限制因素，这对于了解未来对气候变化的反应至关重要。