Effect of uncertainty in surface mass balance-elevation feedback on projections of the future sea level contribution of the Greenland ice sheet
Effect of uncertainty in surface mass balance-elevation feedback on projections of the future sea level contribution of the Greenland ice sheet
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DOI：
10.5194/tc-8-195-2014
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发表时间：
2014-01-01
期刊：
影响因子：
5.2
通讯作者：
Ritz, C.
中科院分区：
文献类型：
--
作者：
Edwards, T. L.;Fettweis, X.;Ritz, C.
我们在MAR区域气候模型（爱德华兹等人，2014年）中对格陵兰冰盖（GrIS）表面物质平衡（SMB：表面积累和表面消融之和）与表面高程之间的反馈应用一种新的参数化方法，并使用五个冰盖模型（ISMs）对未来气候变化进行预测。MAR（区域大气模型：费特维斯，2007年）的气候预测是针对2000 - 2199年的，在SRES A1B排放情景下由ECHAM5和HadCM3全球气候模型（GCMs）驱动。由于SMB - 高程反馈，在ECHAM5的五个ISM预测和HadCM3的三个预测中平均得到的额外海平面贡献在2100年为4.3%（最佳估计；95%置信区间为1.8 - 6.9%），在2200年为9.6%（最佳估计；95%置信区间为3.6 - 16.0%）。在所有结果中，高程反馈显著为正，相对于冰盖地形固定的MAR预测，放大了GrIS对海平面的贡献：我们海平面贡献的95%置信区间（CIs）下限对于所有的ISM和GCM都大于“无反馈”情况。我们的方法在海平面预测方面是新颖的，因为我们在一个连贯的实验设计和统计框架内，沿着从SRES情景到海平面的因果链传播了三种类型的建模不确定性——GCM和ISM结构不确定性以及高程反馈参数化不确定性。对不确定性的相对贡献取决于所关注的时间尺度。在2100年，GCM不确定性最大，但到2200年，ISM和参数化不确定性都更大。我们还使用一个ISM进行了一个参数扰动集合，以估计预测的海平面概率分布形状；我们的结果表明，概率密度略微偏向更高的海平面贡献。
We apply a new parameterisation of the Greenland ice sheet (GrIS) feedback between surface mass balance (SMB: the sum of surface accumulation and surface ablation) and surface elevation in the MAR regional climate model (Edwards et al., 2014) to projections of future climate change using five ice sheet models (ISMs). The MAR (Modele Atmospherique Regional: Fettweis, 2007) climate projections are for 2000-2199, forced by the ECHAM5 and HadCM3 global climate models (GCMs) under the SRES A1B emissions scenario.The additional sea level contribution due to the SMB-elevation feedback averaged over five ISM projections for ECHAM5 and three for HadCM3 is 4.3% (best estimate; 95% credibility interval 1.8-6.9 %) at 2100, and 9.6% (best estimate; 95% credibility interval 3.6-16.0 %) at 2200. In all results the elevation feedback is significantly positive, amplifying the GrIS sea level contribution relative to the MAR projections in which the ice sheet topography is fixed: the lower bounds of our 95% credibility intervals (CIs) for sea level contributions are larger than the "no feedback" case for all ISMs and GCMs.Our method is novel in sea level projections because we propagate three types of modelling uncertainty - GCM and ISM structural uncertainties, and elevation feedback parameterisation uncertainty - along the causal chain, from SRES scenario to sea level, within a coherent experimental design and statistical framework. The relative contributions to uncertainty depend on the timescale of interest. At 2100, the GCM uncertainty is largest, but by 2200 both the ISM and parameterisation uncertainties are larger. We also perform a perturbed parameter ensemble with one ISM to estimate the shape of the projected sea level probability distribution; our results indicate that the probability density is slightly skewed towards higher sea level contributions.