Guodong Sun

Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model

The approach of conditional nonlinear optimal perturbation related to parameter (CNOP-P) is employed to provide a possible climate scenario and to study the impact of climate change on the simulated net primary production (NPP) in China within a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). The CNOP-P, as a type of climate perturbation to bring variation in climatology and climate variability of the reference climate condition, causes the maximal impact on the simulated NPP in China. A linear climate perturbation that induces variation in climatology, as another possible climate scenario, is also applied to explore the role of variation in climate variability in the simulated NPP. It is shown that NPP decreases in northern China and increases in northeastern and southern China when the temperature changes as a result of a CNOP-P-type temperature change scenario. A similar magnitude of change in the spatial pattern variations of NPP is caused by the CNOP-P-type and the linear temperature change scenarios in northern and northeastern China, but not in southern China. The impact of the CNOP-P-type temperature change scenario on magnitude of change of NPP is more intense than that of the linear temperature change scenario. The numerical results also show that in southern China, the change in NPP caused by the CNOP-P-type temperature change scenario compared with the reference simulated NPP is sensitive. However, this sensitivity is not observed under the linear temperature change scenario. The seasonal simulations indicate that the differences between the variations in NPP due to the two types of temperature change scenarios principally stem from the variations in summer and autumn in southern China under the LPJ model. These numerical results imply that NPP is sensitive to the variation in temperature variability. The results influenced by the CNOP-P-type precipitation change scenario are similar to those under the linear precipitation change scenario, which cause the increasing NPP in arid and semi-arid regions of the northern China. The above findings indicate that the CNOP-P approach is a useful tool for exploring the nonlinear response of NPP to climate variability.

Responses of soil carbon variation to climate variability in China using the LPJ model

In this study, we explored the maximal response of soil carbon in a part of China to climate change, including variations in climatology and climate variability, under the condition of global warming. A conditional nonlinear optimal perturbation (CNOP) approach was employed to discuss the above issue using the Lund–Potsdam–Jena (LPJ) model. The variation in the soil carbon was compared with those caused by a linear temperature or precipitation perturbation. The key difference between the CNOP-type and the linear perturbations depended on whether the perturbations brought the variation in the temperature or the precipitation variability in comparison with the reference temperature or the precipitation variability. The model results demonstrated that the variations in the soil carbon resulted from the CNOP-type and linear temperature perturbations in south of the study region, which was corresponding to part of South China, had different variations. We examined three components of the soil carbon in the LPJ model: fast-decomposing soil carbon, slow-decomposing soil carbon, and litter below the ground. The variations of these components derived by the two types of temperature perturbations were different in the chosen region. The reduction in the litter below the ground may be the main cause of decreased soil carbon in arid and semi-arid regions as a result of the two types of temperature perturbations. The different impacts of the two types of temperature perturbations in the south of the study region may be mainly caused by the variations in the fast-decomposing soil carbon. The variations in the soil carbon caused by the two types of precipitation perturbations were similar. In the arid and semi-arid regions, the soil carbon increased due to the two types of precipitation perturbations. This research implies that the variation in temperature variability plays a crucial role in the variations of the soil carbon and its components in the study region.

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund–Potsdam–Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semi-arid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply “target observations” to reduce the uncertainties in model parameters.

Projections of soil carbon using the combination of the CNOP-P method and GCMs from CMIP5 under RCP4.5 in north-south transect of eastern China

Background and aimsSoil plays a key role in land-atmosphere carbon exchange as the largest carbon pool in terrestrial ecosystems. Because of the uncertainty in predictions of soil carbon storage, understanding the magnitude and spatial and temporal patterns of terrestrial carbon sinks and sources is difficult.MethodsIn this study, the response of soil carbon to future climate change scenarios, which were provided by 10 general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 (CMIP5) under the Representative Concentration Pathway (RCP) 4.5 scenario, was explored with the Lund-Potsdam-Jena (LPJ) model for a North-South Transect of Eastern China (NSTEC). Additionally, the conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was used to provide two scenarios to evaluate the possible maximal uncertainties of soil carbon response to future climate change.ResultsBased on the 10 GCMs from 2011 to 2100, the mean soil carbon was from 75.6 Gt C to 86.7 Gt C. As a result of the two climate change scenarios using the CNOP-P approach, soil carbon stocks were respectively 93.1 Gt C and 84.1 Gt C, which were larger than those using the 10 GCMs. The primary difference was determined by the difference in middle and high latitudes (30o N-35o N; 40o N-45o N) of the NSTEC region according to zonal analysis. Soil carbon associated with different plant functional types was also analyzed. The primary contributors to the augmentation of soil carbon under the CNOP-P-type scenario were the increases in soil carbon for temperate broad-leaved summer-green trees and temperate grasslands.ConclusionsAs these numerical results indicated, uncertainty was found in the predictions of soil carbon, and the future soil carbon will increase in NSTEC region compared to 1961–1990. This implied that the soil may play role of carbon sink. And, the CNOP-P approach might offer a possible future upper limit for the evaluation of soil carbon with the LPJ model.

Variations in soil moisture over the ‘Huang-Huai-Hai Plain’ in China due to temperature change using the CNOP-P method and outputs from CMIP5

In this study, the variations in surface soil liquid water (SSLW) due to future climate change are explored in the ‘Huang-Huai-Hai Plain’ (‘3H’) region in China with the Common Land Model (CoLM). To evaluate the possible maximum response of SSLW to climate change, the combination of the conditional nonlinear optimal perturbation related to the parameter (CNOP-P) approach and projections from 10 general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 (CMIP5) are used. The CNOP-P-type temperature change scenario, a new type of temperature change scenario, is determined by using the CNOP-P method and constrained by the temperature change projections from the 10 GCMs under a high-emission scenario (the Representative Concentration Pathway 8.5 scenario). Numerical results have shown that the response of SSLW to the CNOP-P-type temperature scenario is stronger than those to the 11 temperature scenarios derived from the 10 GCMs and from their ensemble average in the entire ‘3H’ region. In the northern region, SSLW under the CNOP-P-type scenario increases to 0.1773 m3 m‒3; however, SSLW in the scenarios from the GCMs fluctuates from 0.1671 to 0.1748 m3 m‒3. In the southern region, SSLW decreases, and its variation (–0.0070 m3 m‒3) due to the CNOP-P-type scenario is higher than each of the variations (–0.0051 to –0.0026 m3 m‒3) due to the scenarios from the GCMs.

A study of parameter uncertainties causing uncertainties in modeling a grassland ecosystem using the conditional nonlinear optimal perturbation method

In this paper, we apply the approach of conditional nonlinear optimal perturbation related to the parameter (CNOP-P) to study parameter uncertainties that lead to the stability (maintenance or degradation) of a grassland ecosystem. The maintenance of the grassland ecosystem refers to the unchanged or increased quantity of living biomass and wilted biomass in the ecosystem, and the degradation of the grassland ecosystem refers to the reduction in the quantity of living biomass and wilted biomass or its transformation into a desert ecosystem. Based on a theoretical five-variable grassland ecosystem model, 32 physical model parameters are selected for numerical experiments. Two types of parameter uncertainties could be obtained. The first type of parameter uncertainty is the linear combination of each parameter uncertainty that is computed using the CNOP-P method. The second type is the parameter uncertainty from multi-parameter optimization using the CNOP-P method. The results show that for the 32 model parameters, at a given optimization time and with greater parameter uncertainty, the patterns of the two types of parameter uncertainties are different. The different patterns represent physical processes of soil wetness. This implies that the variations in soil wetness (surface layer and root zone) are the primary reasons for uncertainty in the maintenance or degradation of grassland ecosystems, especially for the soil moisture of the surface layer. The above results show that the CNOP-P method is a useful tool for discussing the abovementioned problems.

A new climate scenario for assessing the climate change impacts on soil moisture over the Huang–Huai–Hai Plain region of China

Abstract To assess the impacts of temperature and precipitation changes on surface soil moisture (SSM) in the Huang–Huai–Hai Plain (3H) region of China, the approach of conditional nonlinear optimal perturbation related to parameters (CNOP-P) and the Common Land Model are employed. Based on the CNOP-P method and climate change projections derived from 22 global climate models from CMIP5 under a moderate emissions scenario (RCP4.5), a new climate change scenario that leads to the maximal change magnitudes of SSM is acquired, referred to as the CNOP-P type temperature or precipitation change scenario. Different from the hypothesized climate change scenario, the CNOP-P-type scenario considers the variation of the temperature or precipitation variability. Under the CNOP-P-type temperature change, the SSM changes in the last year of the study period mainly fluctuate in the range from −0.014 to +0.012 m3 m−3 (−5.0% to +10.0%), and from +0.005 to +0.018 m3 m−3 (+1.5% to +9.6%) under the CNOP-P-type precipitation change scenario. By analyzing the difference of the SSM changes between different types of climate change scenarios, it is found that this difference associated with SSM is obvious only when precipitation changes are considered. Besides, the greater difference mainly occurs in north of 35°N, where the semi-arid zone is mainly situated. It demonstrates that, in the semi-arid region, SSM is more sensitive to the precipitation variability. Compared with precipitation variability, temperature variability seems to play little role in the variations of SSM.

Using the Lund-Potsdam-Jena model to understand the different responses of three woody plants to land use in China

In this study, the approach of conditional nonlinear optimal perturbation related to initial perturbation (CNOP-I) was employed to investigate the maximum variations in plant amount for three main woody plants (a temperate broadleaved evergreen, a temperate broadleaved summergreen, and a boreal needleleaved evergreen) in China. The investigation was conducted within a certain range of land use intensity using a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). CNOP-I represents a class of deforestation and can be considered a type of land use with respect to the initial perturbation. When deforestation denoted by the CNOP-I has the same intensity for all three plants, the variation in plant amount of the boreal needleleaved evergreen in northern China is greater than the variation in plant amount of both the temperate broadleaved evergreen and temperate broadleaved summergreen in southern China. As deforestation intensity increases, the plant amount variation in the three woody plant functional types carbon changes, in a nonlinear fashion. The impact of land use on plant functional types is minor because the interaction between climate condition and land use is not considered in the LPJ model. Finally, the different impacts of deforestation on net primary production of the three plant functional types were analyzed by modeling gross primary production and autotrophic respiration. Our results suggest that the CNOP-I approach is a useful tool for exploring the nonlinear and different responses of terrestrial ecosystems to land use.