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Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Paleobotanists have long built leaf climate models based on site mean of leaf physiognomic characteristics of woody dicotyledons species (WDS) for estimating past climate. To explore the potential of the order Ericales in estimating paleoclimate, we developed two linear models for each climatic factor. One is based on WDS, and the other is based on both WDS and leaf physiognomic characters of the order Ericales (WDS-E). We found that, compared with WDS models, WDS-E models improved greatly in mean annual precipitation (MAP), growing season precipitation (GSP) and mean annual range in temperature (MART). When the minimum species number of the order Ericales is three per site, the WDS-E models improved the r2 from 0.64 to 0.78 for MART, from 0.23 to 0.61 for ln(MAP), and from 0.37 to 0.64 for ln(GSP) compared with the WDS models. For mean annual temperature (MAT), the WDS-E model (r2 = 0.86) also exhibited a moderate improvement in precision over the WDS model (r2 = 0.82). This study demonstrates that other patterns, such as those of the order Ericales, can contribute additional information towards building more precise paleoclimate models.

Abstract Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10‐fold cross‐validation. For MAT, three combinations of models ( R 2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model ( R 2 = .68). For log e (MAP), the averaging of the support vector machine (SVM) and boosting models improved the R 2 from .19 to .63 compared with that of the MLR model. For MAP, the R 2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model ( R 2 = .21). Even the bagging model, which had the lowest R 2 (.37) for log e (MAP), demonstrated better precision ( R 2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

Abstract Biomass has been promoted as a promising energy resource to mitigate global climate change. To evaluate the contribution of biomass utilization to climate change mitigation under the “Grain for Green” program in Northern Shaanxi, China, a soil carbon dynamic model and a life cycle assessment model were integrated to examine the benefits of using Caragana korshinskii Kom. as an energy crop. We found that the annual dry biomass output is maintained at 0.7 Tg during the simulation period (2020–2097). Due to the compensatory effect of biomass regrowth, the global warming potential of biomass‐derived CO 2 emissions is approximately 0.045; therefore, the total annual biogenic CO 2 emission is 57,211 ± 6,168 Mg CO 2 eq. The total annual life cycle CO 2 emissions approach 867,072 Mg CO 2 eq yr −1 . Under the scenario of no biomass removal, final carbon storage ranges from 15.7 to 19.3 TgC, and the highest carbon sequestration rate is 0.47 TgC yr −1 . In comparison with the no biomass removal scenario, the carbon sequestration rate (close to 0 MgC yr −1 ) in the biomass utilization scenario indicates a carbon loss; however, a portion of the carbon loss (31.39–62.09%) can be offset by carbon emission reductions from the substitution of fossil fuels.

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

Wetlands are an important natural source of methane (CH4), so it is important to quantify how their emissions may vary under future climate change conditions. The Qinghai–Tibet Plateau contains more than a third of China’s wetlands. Here, we simulated temporal and spatial variation in CH4 emissions from natural wetlands on the Qinghai–Tibet Plateau from 2008 to 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5. Based on the simulation results of the TRIPLEX-GHG model forced with data from 24 CMIP5 models of global climate, we predict that, assuming no change in wetland distribution on the Plateau, CH4 emissions from natural wetlands will increase by 35%, 98% and 267%, respectively, under RCP 2.6, 4.5 and 8.5. The predicted increase in atmospheric CO2 concentration will contribute 10–28% to the increased CH4 emissions from wetlands on the Plateau by 2100. Emissions are predicted to be majorly in the range of 0 to 30.5 g C m−2·a−1 across the Plateau and higher from wetlands in the southern region of the Plateau than from wetlands in central or northern regions. Under RCP8.5, the methane emissions of natural wetlands on the Qinghai–Tibet Plateau increased much more significantly than that under RCP2.6 and RCP4.5.

The spatial and temporal variation and uncertainty of precipitation and runoff in China were compared and evaluated between historical and future periods under different climate change scenarios. The precipitation pattern is derived from observed and future projected precipitation data for historical and future periods, respectively. The runoff is derived from simulation results in historical and future periods using a dynamic global vegetation model (DGVM) forced with historical observed and global climate models (GCMs) future projected climate data, respectively. One GCM (CGCM3.1) under two emission scenarios (SRES A2 and SRES B1) was used for the future period simulations. The results indicated high uncertainties and variations in climate change effects on hydrological processes in China: precipitation and runoff showed a significant increasing trend in the future period but a decreasing trend in the historical period at the national level; the temporal variation and uncertainty of projected precipitation and runoff in the future period were predicted to be higher than those in the historical period; the levels of precipitation and runoff in the future period were higher than those in the historical period. The change in trends of precipitation and runoff are highly affected by different climate change scenarios. GCM structure and emission scenarios should be the major sources of uncertainty.

Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles. , Key Points Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change A Ecosystem model (TRIPLEX‐MICROBE) offers considerable improvement over a previous version (TRIPLEX‐GHG) in simulating soil organic carbon Our work is the first step toward a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles