Abstract
Abstract. The search for a long-term benchmark for land-surface models (LSMs) has brought tree-ring data to the attention of the land-surface modelling community, as tree-ring data have recorded growth well before human-induced environmental changes became important. We propose and evaluate an improved conceptual framework of when and how tree-ring data may, despite their sampling biases, be used as century-long hindcasting targets for evaluating LSMs. Four complementary benchmarks – size-related diameter growth, diameter increment of mature trees, diameter increment of young trees, and the response of tree growth to extreme events – were simulated using the ORCHIDEE version r5698 LSM and were verified against observations from 11 sites in the independent, unbiased European biomass network datasets. The potential for big-tree selection bias in the International Tree-Ring Data Bank (ITRDB) was investigated by subsampling the 11 sites from European biomass network. We find that in about 95 % of the test cases, using ITRDB data would result in the same conclusions as using the European biomass network when the LSM is benchmarked against the annual radial growth during extreme climate years. The ITRDB data can be used with 70 % confidence when benchmarked against the annual radial growth of mature trees or the size-related trend in annual radial growth. Care should be taken when using the ITRDB data to benchmark the annual radial growth of young trees, as only 50 % of the test cases were consistent with the results from the European biomass network. The proposed maximum tree diameter and annual growth increment benchmarks may enable the use of ITRDB data for large-scale validation of the LSM-simulated response of forest ecosystems to the transition from pre-industrial to present-day environmental conditions over the past century. The results also suggest ways in which tree-ring width observations may be collected and/or reprocessed to provide long-term validation tests for land-surface models.
Highlights
Earth system models integrate numerical sub-models of atmospheric circulation, ocean dynamics and biogeochemistry, sea ice dynamics, and biophysical and biogeochemical processes at the land surface
Verification of ORCHIDEE-based treering width (TRW) simulations was applied at the 11 sites selected from the European biomass network (Sect. 3.3), via estimation of the four benchmarks (Sect. 2.4) from simulations and observations as well as their evaluation via the two skill metrics per benchmark, for a total of 88 test case comparisons (Table 1, Fig. S3)
We enrich the verification by including the effects of potentially confounding factors such as forest structure, age and size trends (Alexander et al, 2018; Nickless et al, 2011; Jiang et al, 2018), phenology (Shen et al, 2020), and sampling biases (Babst et al, 2014a), in addition to climate and environmental forcing (Klesse et al, 2018; Zuidema et al, 2020; Li et al, 2014; Rollinson et al, 2017). Targeting both size-structured and age-structured information in observations and simulations (Fig. 3), we have proposed the use of four verification benchmarks created from observations and potentially simulated by landsurface models (LSMs), with each of them defined by two complementary metrics (Fig. 2; Table S2): i
Summary
Earth system models integrate numerical sub-models of atmospheric circulation, ocean dynamics and biogeochemistry, sea ice dynamics, and biophysical and biogeochemical processes at the land surface. The credibility of projections of the future climate from any Earth system model in part relies on the ability of each of its above-mentioned four sub-models to accurately reproduce the past (McGuffie and Henderson-Sellers, 2005). Long-term changes that date back to pre-industrial conditions (Luo et al, 2012) have been documented for vegetation distribution through pollen-based reconstructions (Cao et al, 2019), land-surface models (LSMs) currently lack a long-term benchmark for forest ecosystem functioning. The absence of long-term benchmarks is thought to contribute substantially to uncertainties in simulated future global carbon stocks in soil and vegetation (Friedlingstein et al, 2006, 2014) and, as such, to climate projections (Fig. S1a)
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