Abstract. Nitrogen and argon stable-isotope data extracted from ancient air in ice cores provides the possibility to reconstruct Greenland past temperatures when inverting firn-densification and heat-diffusion models (firn-models) to fit the gas-isotope data (δ¹⁵N, δ⁴⁰Ar, δ¹⁵N_excess). This study uses the Döring and Leuenberger (2018) fitting-algorithm coupled on two state of the art firn-models to fit multiple Holocene gas-isotope data measured on the GISP2 ice core. We present for the first time the resulting temperature estimates when fitting δ¹⁵N, δ⁴⁰Ar and δ¹⁵N_excess as single targets with misfits generally in the low permeg level. Whereas the comparison between the reconstructions using δ¹⁵N and δ⁴⁰Ar shows a high agreement, the use of δ¹⁵N_excess for reconstructing temperature is problematic, due to higher statistical and systematic data uncertainty influencing especially multi-decadal to multi-centennial signals, and results in an unrealistic temperature estimate that differs significantly from the two other reconstructions. We find evidence for systematic too high δ⁴⁰Ar data in the early- and late-Holocene potentially caused by post coring gas-loss or an insufficient correction of this mechanism. Next, we compare the performance of the Goujon et al. (2003) firn-model and the Schwander et al. (1997) firn-model for Holocene temperature reconstructions. Besides small differences of the reconstructed temperature anomalies – potentially caused by slightly different implementation of firn physics and parameters in the two models – the reconstructed temperature anomalies are highly comparable. We were able to quantify the contribution of the firn-model difference to the uncertainty budget of our reconstruction. Furthermore, the fractions of uncertainty on the reconstructed temperatures, arising from the non-perfect reproducibility of the fitting algorithm and from the remaining final misfits (low permeg level), were quantified. Together with the published measurement uncertainty of the gas-isotope data and the analysis of the impact of accumulation-rate uncertainty on the reconstruction, we were able to calculate the mean uncertainty (2σ) for the nitrogen and the argon based temperature estimates with 2σ_T = 0.80 ... 0.88 K for T(δ¹⁵N), and 2σ_T = 0.87 ... 1.81 K for T(δ⁴⁰Ar), respectively. Finally, we compare our reconstructed temperatures to two recent reconstructions based on the same gas-isotope data as used here, but following different reconstruction strategies: first the study of Buizert et al. (2018), which uses a combination of δ¹⁸O_ice-calibration and δ¹⁵N-fitting, and second the study of Kobashi et al. (2017), where δ¹⁵N_excess was fitted in order to conduct the temperature reconstruction. We find generally higher agreement between our T(δ¹⁵N) estimate and the Buizert et al. (2018) temperature – in terms of variability and correlation in three investigated periodic-time bands (multi-decadal, multi-centennial and multi-millennial) – as if our T(δ¹⁵N) reconstruction is compared to the Kobashi et al. (2017) temperature. However, all three reconstruction strategies lead to distinct temperature realizations.

中文翻译：

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基于GISP2多气体同位素测量的全新世温度重建的比较

摘要。反相粒雪-压实和热扩散模型（粒雪的模型），以适应气体同位素数据（δ当从古代空气在冰核提取氮气和氩气稳定同位素的数据提供给重建格陵兰过去温度的可能性¹⁵ N，δ ⁴⁰的Ar，δ ^15倍ñ_过量）。这项研究使用了Döring和Leuenberger（2018）拟合算法，并结合了两个最先进的燃料模型，以拟合在GISP2冰芯上测得的多个全新世气体同位素数据。我们提出了第一次安装时δ所得的温度估计值¹⁵ N，δ ⁴⁰ Ar和δ ^15倍Ñ_过量作为单个目标，通常具有较低的Permeg水平。而重建之间的比较使用δ ¹⁵ N和δ ⁴⁰氩示出了一致性高，使用δ的¹⁵ Ñ_过量用于重构温度是有问题的，由于较高的统计和系统数据的不确定性影响尤其多年代到多百年信号，并导致不切实际的温度估算值，该估算值与其他两个重构有很大差异。我们发现的证据进行系统的过高δ ⁴⁰全新世早期和晚期的Ar数据可能是由于取芯后气体损失或对此机制的校正不足而引起的。接下来，我们比较Goujon等人的性能。（2003）firn-model和Schwander等。（1997年）全新世温度重建的firn模型。除了重建温度异常的细微差异（可能是由于两种模型中具体的物理性质和参数实施方式略有不同所引起的）外，重建温度异常也具有很高的可比性。我们能够量化佛罗伦萨模型差异对我们重建不确定性预算的贡献。此外，由于拟合算法的非完美可重复性以及剩余的最终失配（低Permeg级别），导致重构温度的不确定性分数很小，被量化。结合已发布的气体同位素数据的测量不确定度以及对累积速率不确定度对重建的影响的分析，我们能够计算出基于氮气和氩气的温度估算值的平均不确定度（2σ），其中2σ_Ť  = 0.80 ... 0.88 K中T（δ ¹⁵ N），和2σ _Ť  = 0.87 ... 1.81 K中T（δ ⁴⁰ AR），分别。最后，我们根据与此处使用的相同的气体同位素数据，但遵循不同的重建策略，将重建温度与最近的两次重建进行了比较：首先是Buizert等人的研究。（2018），其使用δ的组合¹⁸ ö_冰-calibration和δ ¹⁵ N-二配合，和第二小桥等人的研究。（2017），其中δ ¹⁵ Ñ_过量装配在为了进行温度重建。我们发现我们的T与普遍较高协议（δ ¹⁵N）估计和Buizert等人。（2018）温度-在三个变异性和相关性的方面研究周期性时间频带（多年代，多百年和多千年） -仿佛我们的T（δ ¹⁵ N）重构相比，小桥等人。（2017）温度。但是，所有这三种重建策略都会导致不同的温度实现。