Land surface models significantly underestimate the impact of land-use changes on global evapotranspiration

Despite numerous assessments of the impact of land-use change (LUC) on terrestrial evapotranspiration (ET) that have been conducted using land surface models (LSMs), no attempts have been made to evaluate their performance in this regard globally. Errors in simulating LUC impacts on ET largely stem from LUC data interpretation (LI, i.e. mapping of gridded LUC data into annual plant function types) and model structure (MS, i.e. parameterization of land-surface processes). The objective of this study was to benchmark ET estimates from four LSMs using the Zhang-curve, a prototype of the Budyko framework that has been validated against global hydrological observations and used widely to quantify the impacts of LUC on ET. A framework was further proposed to quantify and attribute errors in estimated ET changes induced by LI or MS. Results showed that all LSMs underestimated ET changes by about 55%–78%, and 37%–48% of the error was attributable to LI, but only 11%–32% of the error was attributable to MS across the four LSMs. From a hydrological perspective, our analysis provided insights about the errors in estimated impacts of LUC on ET by LSMs. The results demonstrated that LUC data interpretation accounted for a larger fraction of errors than LSM structure. Therefore, there is an urgent need for the defining and development of consistent protocols for interpreting global LUC data for future assessments.

The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe.

Land-atmosphere interactions in sub-polar and alpine climates in the CORDEX FPS LUCAS models: Part II. The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect, and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snow melt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. Greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass-snow and forest-snow interactions during the snowmelt season. Pathways to accomplishing this include 1) a more sophisticated representation of forest structure, 2) kilometer scale simulations, and 3) more observational studies on vegetation-snow interactions in Northern Europe.

Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for global land-surface models

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.

Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.