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.     

草原生态系统模式中参数不确定性导致的模拟结果不确定性研究

基于五变量草原生态系统理论模式,应用与参数有关的条件非线性最优扰动(CNOP-P)方法,探讨了由参数不确定性导致的草原生态系统模式模拟结果的不确定性问题。参数的不确定性可能来源于观测和（或）对物理过程描述等的不确定性。选取了五变量草原生态系统模式中具有物理意义的32个模式参数进行数值试验。试验结果表明,对所考察的32个模式参数,在一定的不确定性和给定的优化时刻范围内,单独优化每个参数所得CNOP-Ps的联合模态与同时优化32个参数所得CNOP-P的模态并不相同。比较了上述两类参数误差以及随机参数误差对草原生态系统模拟的差异。随机参数误差与上述优化方法所得参数误差的不确定性范围大小相同。数值结果表明,同时优化32个参数所得 CNOP-P 类型参数误差使得草原生态系统模拟的不确定性程度最大。这种影响表现在使得草原生态系统转变为沙漠生态系统,或者使得草原生态系统转变为具有更多生草量的草原生态系统。上述数值结果不依赖于优化时间和参数不确定性程度的大小。这些数值结果建议我们应当考虑多参数的非线性相互作用来研究草原生态系统模式模拟的不确定性问题,并且揭示出CNOP-P方法是讨论上述问题的一个有用的工具。     

Applications of conditional nonlinear optimal perturbation in predictability study and sensitivity analysis of weather and climate

Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP, rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry. Third, in the studies of the sensitivity and stability of ocean’s thermohaline circulation (THC), the nonlinear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP. Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis.     

数值天气预报和气候预测可预报性研究的若干动力学方法

简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。     

Inducing Unstable Grassland Equilibrium States Due to Nonlinear Optimal Patterns of Initial and Parameter Perturbations: Theoretical Models

Due to uncertainties in initial conditions and parameters,the stability and uncertainty of grassland ecosystem simulations using ecosystem models are issues of concern.Our objective is to determine the types and patterns of initial and parameter perturbations that yield the greatest instability and uncertainty in simulated grassland ecosystems using theoretical models.We used a nonlinear optimization approach,i.e.,a conditional nonlinear optimal perturbation related to initial and parameter perturbations (CNOP) approach,in our work.Numerical results indicated that the CNOP showed a special and nonlinear optimal pattern when the initial state variables and multiple parameters were considered simultaneously.A visibly different complex optimal pattern characterizing the CNOPs was obtained by choosing different combinations of initial state variables and multiple parameters in different physical processes.We propose that the grassland modeled ecosystem caused by the CNOP-type perturbation is unstable and exhibits two aspects:abrupt change and the time needed for the abrupt change from a grassland equilibrium state to a desert equilibrium state when the initial state variables and multiple parameters are considered simultaneously.We compared these findings with results affected by the CNOPs obtained by considering only uncertainties in initial state variables and in a single parameter.The numerical results imply that the nonlinear optimal pattern of initial perturbations and parameter perturbations,especially for more parameters or when special parameters are involved,plays a key role in determining stabilities and uncertainties associated with a simulated or predicted grassland ecosystem.     

内蒙古典型草原区近40年气候变化及其对土壤水分的影响

分析气候变化对草原区土壤水分的影响对了解草原退化原因、恢复草原生态环境有重要的指导意义。根据近40年气象资料和近20年的土壤水分观测资料,利用线性趋势等数理统计方法,分析了内蒙古典型草原区气候变化趋势和对土壤水分变化的影响,得出内蒙古典型草原区近40年气候变化趋势与全球气候变化规律相似;影响土壤湿度的气象因子主要是降水和蒸发,温度通过影响蒸发而间接影响土壤湿度,蒸降差是分析气候变化对土壤水分影响的直观指标。气候变暖导致蒸发加剧,在降水增加不明显的条件下,加速了土壤干旱化程度。     

Nonlinear feature of the abrupt transitions between multiple equilibria states of an ecosystem model

Based on a five-variable theoretical ecosystem model, the stability of equilibrium state and the nonlinear feature of the transition between a grassland state and a desert state are investigated. The approach of the conditional nonlinear optimal perturbations (CNOPs), which are the nonlinear generalization of the linear singular vectors (LSVs), is adopted. The numerical results indicate that the linearly stable grassland and desert states are nonlinearly unstable to large enough initial perturbations on the condition that the moisture index $\mu$ satisfies 0.3126<μ<0.3504. The perturbations represent some kind of anthropogenic influence and natural factors. The results obtained by CNOPs, LSVs and Lyapunov vectors (LVs) are compared to analyze the nonlinear feature of the transition between the grassland state and the desert state. Besides this, it is shown that the five-variable model is superior to the three-variable model in providing more visible signals when the transitions occur.     

A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application

Simulations and predictions using numerical models show considerable uncertainties, and parameter uncertainty is one of the most important sources. It is impractical to improve the simulation and prediction abilities by reducing the uncertainties of all parameters. Therefore, identifying the sensitive parameters or parameter combinations is crucial. This study proposes a novel approach: conditional nonlinear optimal perturbations sensitivity analysis(CNOPSA) method. The CNOPSA method fully consi...     

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.     

天山北坡典型草地净初级生产力对氮沉降及气候变化的响应阈值研究

生态阈值现象普遍存在于自然系统中.气候变化幅度过大,超出了生态系统本身的调节和修复能力,生态系统的结构功能就会遭到破坏.新疆干旱区气候波动明显,该区草地生态系统对大气氮沉降和气候变化的响应是否存在阈值,有待深入研究.本文以天山北坡沿海拔梯度分布的四种草地类型（高山草甸（AM）、中山森林草地（MMFM）、低山干草原（LMDG）和平原荒漠草原（PDG））为研究对象,基于DNDC模型,揭示氮沉降及气候变化对天山北坡草地生态系统净初级生产力的影响.研究结果表明：1）草地净初级生产力 （NPP）对氮沉降增加的响应存在阈值,PDG、LMDG、MMFM和AM的响应阈值分别为20±5.77、60±26.46、50±15.28和30±11.55 kg·hm^-2.2）四种草地类型的NPP从大到小依次为MMFM、LMDG、PDG和AM,水热条件是决定NPP的主要因素.3）PDG草地NPP对温度升高的响应存在阈值,而对于其他类型的草地,在目前的研究中尚未得出确切结论.4）PDG和LMDG草地NPP与降水有明显的正相关关系,而AM草地NPP的变化与降水变化呈负相关.不同草地类型对降水变化的敏感程度也有较大差异,PDG最大,其次是LMDG,之后是AM和MMFM.     

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.     

内蒙古草原气候特点与草原生态类型区域划分

为了合理开发利用气候资源，给草原畜牧业生产的分区管理提供科学依据，文章对内蒙古草原气候特点及气候对牧草生长、畜种分布和土壤环境的影响进行了分析，发现内蒙古气候湿润度的某些等值线与土壤带的分界线几乎完全重合，表明土壤带的形成与气候条件密切相关。而气候和土壤环境是草场类型及其生态系统的主要影响因素，因此以气候湿润度为主要依据，结合内蒙古土壤带的水平分布特征，进行草原生态类型区域的划分不仅具有合理性，而且具有稳定性。指出近年来的气候增暖以及由此引起的其他气候变化，虽使草原生产力有一定的提高，但并未使内蒙古草原的生态类型有所改变。     

Climate change alters growing season flux dynamics in mesic grasslands

Changing climate could affect the functioning of grassland ecosystems through variation in climate forcings and by altering the interactions of forcings with ecological processes. Both the short and long-term effects of changing forcings and ecosystem interactions are a critical part of future impacts to ecosystem ecology and hydrology. To explore these interactions and identify possible characteristics of climate change impacts to mesic grasslands, we employ a low-dimensional modeling framework to assess the IPCC A1B scenario projections for the Central Plains of the United States; forcings include increased precipitation variability, increased potential evaporation, and earlier growing season onset. These forcings are also evaluated by simulations of vegetation photosynthetic capacity to explore the seasonal characteristics of the vegetation carbon assimilation response for species at the Konza Prairie in North Central Kansas, USA. The climate change simulations show decreases in mean annual soil moisture and and carbon assimilation and increased variation in water and carbon fluxes during the growing season. Simulations of the vegetation response show increased variation at the species-level instead of at a larger class scale, with important heterogeneity in how individual species respond to climate forcings. Understanding the drivers and relationships behind these ecosystem responses is important for understanding the likely scale of climate change impacts and for exploring the mechanisms shaping growing season dynamics in grassland ecosystems.     

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.     

Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau

The Tibetan Plateau is a region sensitive to climate change, due to its high altitude and large terrain. This sensitivity can be measured through the response of vegetation patterns to climate variability in this region. Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery and correlation analyses are effective tools to study land cover changes and their response to climatic variations. This is especially important for regions like the Tibetan Plateau, which has a complex ecosystem but lacks a lot of detailed in-situ observation data due to its remoteness, vastness and the severity of its climatic conditions. In this research a time series of 315 SPOT VEGETATION scenes, covering the period between 1998 and 2006, has been processed with the Harmonic ANalysis of Time Series (HANTS) algorithm in order to reveal the governing spatiotemporal pattern of variability. Results show that the spatial distribution of NDVI values is in agreement with the general climate pattern in the Tibetan Plateau. The seasonal variation is greatly influenced by the Asian monsoon. Interannual analysis shows that vegetation density (recorded here by the NDVI values) in the entire Tibetan Plateau has generally increased. Using a 1 km resolution land cover map from GLC2000, seven meteorological stations, presenting monthly data on near surface air temperature and precipitation, were selected for correlation analysis between NDVI and climate conditions in this research. A time lag response has also been found between NDVI and climate variables. Except in desert grassland (Shiquanhe station), the NDVI of all selected sites showed strong correlation with air temperature and precipitation, with variations in correlation according to the different land cover types at different locations. The strongest relationship was found in alpine and subalpine plain grass, the weakest in desert grassland.     

中天山北坡草地生物量估产模型研究

分别对草甸、草原和荒漠3种草地类型进行了生物量的实测工作。根据同步的MODIS与AVHRR资料,应用ENVI软件提取出这两种传感器在3种草地类型上的植被指数：NDVI和RVI。经过统计分析得到两种传感器在3种草地类型上的生物量模型,从而建立起MODIS植被指数与草地生物量的相关关系,并对MODIS和AVHRR植被指数估产模型进行了评价与分析。     

Numerical experiments of the effects of initial desert moisture on the climate change

A numerical model with the p-sigma incorporated coordinate system and primitive equations is used to simulate the effect of initial soil moisture in desert areas on the climate change. The results show that the present deserts have a tendency to expand. When the initial soil moisture in the desert regions increases, the desert areas will shrink but can not disappear. The small deserts may not remain any longer when there are sources of water vapour around. Both the land-sea contrast and the topography are the background conditions of the present desert distribution through the mechanism of the downdrafts and the rare precipitation over the desert regions. The increase of the initial desert soil moisture will weaken the summer monsoon circulation and, consequently, the monsoonal precipitation.     

Extended application of the conditional nonlinear optimal parameter perturbation method in the common land model

An extension of the conditional nonlinear optimal parameter perturbation (CNOP-P) method is applied to the parameter optimization of the Common Land Model (CoLM) for the North China Plain with the differential evolution (DE) method. Using National Meteorological Center (NMC) Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project II (AMIP-II) 6-hourly Reanalysis Gaussian Grid data, two experiments (I and II) were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture. A third experiment (III) was designed to study the shallow soil moisture and latent heat flux simultaneously. In all the three experiments, after the optimization stage, the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month. The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture, with the simulation results of CoLM after the double-parameter optimal experiment being better than the single-parameter optimal experiment in the optimization slot. Furthermore, the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage. In addition, whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization, and the more accurate the data are, the more significant the results of optimization may be.     

Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considered:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates.     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.