A team effort to develop a Community Integrated Earth System Model (CIESM) was initiated in China in 2012. The model was based on NCAR Community Earth System Model (Version 1.2.1) with several novel developments and modifications aimed to overcome some persistent systematic biases, such as the double Intertropical Convergence Zone problem and underestimated marine boundary layer clouds. Aerosols' direct and indirect effects are prescribed using the MACv2‐SP approach and data sets. The spin‐up of a 500‐year preindustrial simulation and three historical simulations are described and evaluated. Prominent improvements include alleviated double Intertropical Convergence Zone problem, increased marine boundary layer clouds, and better El Niño Southern Oscillation amplitude and periods. One deficiency of the model is the significantly underestimated Arctic and Antarctic sea ice in warm seasons. The historical warming is about 0.55 °C greater than observations toward 2014. CIESM has an equilibrium climate sensitivity of 5.67 K, mainly resulted from increased positive shortwave cloud feedback. Our efforts on porting and redesigning CIESM for the heterogeneous Sunway TaihuLight supercomputer are also introduced, including some ongoing developments toward a future version of the model.

Summary: Wood formation determines major long‐term carbon (C) accumulation in trees and therefore provides a crucial ecosystem service in mitigating climate change. Nevertheless, we lack understanding of how species with contrasting wood anatomical types differ with respect to phenology and environmental controls on wood formation. In this study, we investigated the seasonality and rates of radial growth and their relationships with climatic factors, and the seasonal variations of stem nonstructural carbohydrates (NSC) in three species with contrasting wood anatomical types (red oak: ring‐porous; red maple: diffuse‐porous; white pine: coniferous) in a temperate mixed forest during 2017–2019. We found that the high ring width variability observed in both red oak and red maple was caused more by changes in growth duration than growth rate. Seasonal radial growth patterns did not vary following transient environmental factors for all three species. Both angiosperm species showed higher concentrations and lower inter‐annual fluctuations of NSC than the coniferous species. Inter‐annual variability of ring width varied by species with contrasting wood anatomical types. Due to the high dependence of annual ring width on growth duration, our study highlights the critical importance of xylem formation phenology for understanding and modelling the dynamics of wood formation.

Wood growth constitutes the main process for long‐term atmospheric carbon sequestration in vegetation. However, our understanding of the process of wood growth and its response to environmental drivers is limited. Current dynamic global vegetation models (DGVMs) are mainly photosynthesis‐driven and thus do not explicitly include a direct environmental effect on tree growth. However, physiological evidence suggests that, to realistically model vegetation carbon allocation under increased climatic stressors, it is crucial to treat growth responses independently from photosynthesis. A plausible growth response function suitable for global simulations in DGVMs has been lacking. Here, we present the first soil water‐growth response function and parameter range for deciduous and evergreen conifers. The response curve was calibrated against European larch and Norway spruce in a dry temperate forest in the Swiss Alps. We present a new data‐driven approach based on a combination of tree ring width (TRW) records, growing season length and simulated subdaily soil hydrology to parameterize ring width increment simulations. We found that a simple linear response function, with an intercept at zero moisture stress, used in growth simulations reproduced 62.3% and 59.4% of observed TRW variability for larch and spruce respectively and, importantly, the response function slope was much steeper than literature values for soil moisture effects on photosynthesis and stomatal conductance. Specifically, we found stem growth stops at soil moisture potentials of −0.47 MPa for larch and −0.66 MPa for spruce, whereas photosynthesis in trees continues down to −1.2 MPa or lower, depending on species and measurement method. These results are strong evidence that the response functions of source and sink processes are indeed very different in trees, and need to be considered separately to correctly assess vegetation responses to environmental change. The results provide a parameterization for the explicit representation of growth responses to soil water in vegetation models.

The dryland belt (DLB) in Northern Eurasia is the largest contiguous dryland on Earth. During the last century, changes here have included land use change (e.g. expansion of croplands and cities), resource extraction (e.g. coal, ores, oil, and gas), rapid institutional shifts (e.g. collapse of the Soviet Union), climatic changes, and natural disturbances (e.g. wildfires, floods, and dust storms). These factors intertwine, overlap, and sometimes mitigate, but can sometimes feedback upon each other to exacerbate their synergistic and cumulative effects. Thus, it is important to properly document each of these external and internal factors and to characterize the structural relationships among them in order to develop better approaches to alleviating negative consequences of these regional environmental changes. This paper addresses the climatic changes observed over the DLB in recent decades and outlines possible links of these changes (both impacts and feedback) with other external and internal factors of contemporary regional environmental changes and human activities within the DLB.

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.