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Abstract A method to assess firn compaction using data collected with the Airborne SAR (Synthetic Aperture Radar)/Interferometric Radar Altimeter System (ASIRAS) is developed. For this, we develop a dynamical firn-compaction model that includes meltwater retention. Based on the ASIRAS data, which show internal layers as annual horizons in the uppermost firn, the method relies on inferring the age/ depth (internal layers) information from the radar data using a Monte Carlo inversion technique to tune in parallel both the firn model and the atmospheric forcing parameters (temperature and accumulation). The model is validated against two firn cores, and it is shown that applying both firn densities and age/ depth information for the inversion gives the most accurate understanding of model biases. The method is then applied to a 67 km section of the EGIG line forced by atmospheric output from a regional climate model using only age/depth information in the inversion step. The layers traced by the ASIRAS data are modeled with a root-mean-square error of 9 cm, which is within the estimated error of the layer tracing. This gives us confidence in applying observed annual layering from firn radar data to assess firn compaction; however, the study also indicates that our firn-model-tuning parameters are site-dependent and cannot be parameterized by temperature and accumulation alone.

Abstract To retain the sequence of events of a regional climate model (RCM) simulation driven by a reanalysis, a method that has not been widely adopted uses an RCM with frequent reinitializations toward its driving field. In this regard, this study highlights the benefits of an RCM simulation with frequent (daily) reinitializations compared to a standard continuous RCM simulation. Both simulations are carried out with the RCM HIRHAM5, driven with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data, over the 12-km-resolution European Coordinated Regional Climate Downscaling Experiment (CORDEX) domain covering the period 1989–2009. The analysis of daily precipitation shows improvements in the sequence of events and the maintenance of the added value from the standard continuous RCM simulation. The validation of the two RCM simulations with observations reveals that the simulation with reinitializations indeed improves the temporal correlation. Furthermore, the RCM simulation with reinitializations has lower systematic errors compared to the continuous simulation, which has a tendency to be too wet. A comparison of the distribution of wet day precipitation intensities shows similar added value in the continuous and reinitialized simulations with higher variability and extremes compared to the driving field ERA-Interim. Overall, the results suggest that the finescale climate dataset of the RCM simulation with reinitializations better suits the needs of impact studies by providing a sequence of events matching closely the observations, while limiting systematic errors and generating reliable added value. Downsides of the method with reinitializations are increased computational costs and the introduction of temporal discontinuities that are similar to those of a reanalysis.

Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land-atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e. model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.

Abstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.

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 Recent studies have suggested that tropical forests may not be resilient against climate change in the long term, primarily owing to predicted reductions in rainfall and forest productivity, increased tree mortality, and declining forest biomass carbon sinks. These changes will be caused by drought‐induced water stress and ecosystem disturbances. Several recent studies have reported that climate change has increased tree mortality in temperate and boreal forests, or both mortality and recruitment rates in tropical forests. However, no study has yet examined these changes in the subtropical forests that account for the majority of China's forested land. In this study, we describe how the monsoon evergreen broad‐leaved forest has responded to global warming and drought stress using 32 years of data from forest observation plots. Due to an imbalance in mortality and recruitment, and changes in diameter growth rates between larger and smaller trees and among different functional groups, the average DBH of trees and forest biomass have decreased. Sap flow measurements also showed that larger trees were more stressed than smaller trees by the warming and drying environment. As a result, the monsoon evergreen broad‐leaved forest community is undergoing a transition from a forest dominated by a cohort of fewer and larger individuals to a forest dominated by a cohort of more and smaller individuals, with a different species composition, suggesting that subtropical forests are threatened by their lack of resilience against long‐term climate change.

In recent years, plastic greenhouse vegetable cultivation (PGVC) has expanded worldwide, particularly in China, where it accounts for more than 90% of all global PGVC operations. As compared with conventional agricultural methods, PGVC has doubled crop yields by extending growing seasons and intensifying agriculture. PGVC also offers more ecosystem services relative to conventional approaches, including greater soil carbon sequestration, lower water consumption, and improved soil protection at regional scales. The economic benefits of this easily implemented agricultural method are attractive to small‐holder farmers. However, greater environmental impacts (eg greenhouse‐gas emissions, generation of large amounts of plastic waste) are associated with PGVC than with conventional approaches. Here, we review what is currently known about PGVC and identify future research priorities that will comprehensively assess the ecosystem services offered by this method of cultivation, as well as its environmental impacts and socioeconomic benefits.