Climate and Vegetation Drivers of Terrestrial Carbon Fluxes:A Global Data Synthesis

The terrestrial carbon(C) cycle plays an important role in global climate change, but the vegetation and environmental drivers of C fluxes are poorly understood. We established a global dataset with 1194 available data across site-years including gross primary productivity(GPP), ecosystem respiration(ER), net ecosystem productivity(NEP), and relevant environmental factors to investigate the variability in GPP, ER and NEP, as well as their covariability with climate and vegetation drivers.The results indicated that both GPP and ER increased exponentially with the increase in mean annual temperature(MAT)for all biomes. Besides MAT, annual precipitation(AP) had a strong correlation with GPP(or ER) for non-wetland biomes.Maximum leaf area index(LAI) was an important factor determining C fluxes for all biomes. The variations in both GPP and ER were also associated with variations in vegetation characteristics. The model including MAT, AP and LAI explained 53%of the annual GPP variations and 48% of the annual ER variations across all biomes. The model based on MAT and LAI explained 91% of the annual GPP variations and 92.9% of the annual ER variations for the wetland sites. The effects of LAI on GPP, ER or NEP highlighted that canopy-level measurement is critical for accurately estimating ecosystem–atmosphere exchange of carbon dioxide. The present study suggests a significance of the combined effects of climate and vegetation(e.g.,LAI) drivers on C fluxes and shows that climate and LAI might influence C flux components differently in different climate regions.     

EnKF中误差协方差优化方法及在资料同化中应用

集合卡尔曼滤波 (the Ensemble Kalman Filter,简称EnKF) 中将预报集合的统计协方差作为预报误差协方差,但该估计可能严重偏离真实的预报误差协方差,影响同化精度。基于极大似然估计理论,发展了一种优化预报误差协方差矩阵的实时膨胀方法,即MLE (the Maximum Likelihood Estimation) 方法。利用蒙古国基准站Delgertsgot (简称DGS站) 观测资料,基于EnKF方法和MLE方法,在通用陆面模式 (the Common Land Model,简称CoLM) 中同化了地表温度和10 cm土壤温度观测资料,建立了土壤温度同化系统。结果表明:MLE方法对地表温度和各层土壤温度 (尤其深层土壤温度) 的估计比EnKF方法准确。考虑到浅层和深层土壤温度的差别,在实施MLE方法时对浅层和深层土壤温度采用了不同的膨胀因子。对比膨胀因子为单一标量时的结果,多因子膨胀能缓解深层土壤温度的不合理膨胀,改善同化效果。     

基于约束性分析的数据与作物模型同化方法

同化观测数据可为作物生长模型的区域应用提供支持。该文定义了观测数据对模型参数的约束性,研究发现华北夏玉米观测数据对WOFOST模型的可约束参数主要包括初始总干物重、不同发育阶段的比叶面积、初始最大CO₂同化速率、叶片衰老系数、初始土壤有效水、最大根深日增量以及初始根深的初始土壤水分含量等。建立了基于参数约束性分析的观测数据与作物生长模型同化方法和流程, 利用优化算法进行作物生长模型所有参数和变量初值的敏感性分析,遴选出各状态变量的敏感参数;根据拟合度与优化结果之间关系进行敏感参数的约束性分析,获得不同变量的可约束参数;组合优化可约束参数得到各参数最优值,由此实现了观测数据与作物生长模型的同化。约束性体现了观测数据对模型参数或变量初值的控制能力,可约束参数作为待优化参数使数据模型同化获得了最优结果。     

Improved Analyses and Forecasts With AIRS Temperature Retrievals Using the Local Ensemble Transform Kalman Filter

In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS). Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts, which is found not only in the temperature field but also in other variables. In tropics and the Northern Hemispheric extratropics these impacts are smaller, but are still generally positive or neutral.     

Using satellite-derived Atmospheric Motion Vector (AMV) observations in the ensemble data assimilation system

The use of global Atmospheric Motion Vectors (AMV) satellite observations in the meteorological data assimilation system based on Local Ensemble Transform Kalman Filter (LETKF) algorithm is considered. The height assignment is the most crucial error source for AMV observations. To reduce its impact, the AMV height reassignment method is implemented; it is based on the consistency coefficient bet ween the observed and the background winds. The other way to improve the analysis quality is a more accurate specification of AMV observation errors. This necessitates the use of the nondiagonal observation-error covariance matrix R in the data assimilation scheme. The first results of these studies are presented. It is demonstrated that the use of AMV observations in the data assimilation system reduces the errors of forecasts computed from the initial data of this system.     

集合卡尔曼滤波同化多普勒雷达资料的观测系统模拟试验

本文将集合卡尔曼滤波同化技术应用到对流尺度系统中,实施了基于WRF模式的同化单部多普勒雷达径向风和反射率因子的观测系统模拟试验,验证了其在对流尺度中应用的可行性和有效性,并对同化系统的特性进行了探讨。试验表明:WRF-EnKF雷达资料同化系统能较准确分析模式风暴的流场、热力场、微物理量场的细致特征;几乎所有变量的预报和分析误差经过同化循环后都能显著下降,同化分析基本上能使预报场在各层上都有所改进,对预报场误差较大层次的更正更为显著;约8个同化循环后,EnKF能在雷达反射率、径向风观测与背景场间建立较可靠的相关关系,使模式各变量场能被准确分析更新,背景场误差协方差在水平方向和垂直方向都有着复杂的结构,是高度非均匀、各项异性和流依赖的;集合平均分析场做的确定性预报在短时间内能较好保持真值场风暴的细节结构,但预报误差增长较快。     

Does the integration of the dynamic nitrogen cycle in a terrestrial biosphere model improve the long-term trend of the leaf area index?

The carbon cycle strongly interacts with the nitrogen cycle. Several observations show that the effects of global change on primary production and carbon storage in plant biomass and soils are partially controlled by N availability. Nevertheless, only a small number of terrestrial biosphere models represent explicitly the nitrogen cycle, despite its importance on the carbon cycle and on climate. These models are difficult to evaluate at large spatiotemporal scales because of the scarcity of data at the global scale over a long time period. In this study, we benchmark the capacity of the O–CN global terrestrial biosphere model to reproduce temporal changes in leaf area index (LAI) at the global scale observed by NOAA_AVHRR satellites over the period 1982–2002. Using a satellite LAI product based on the normalized difference vegetation index of global inventory monitoring and modelling studies dataset, we estimate the long-term trend of LAI and we compare it with the results from the terrestrial biosphere models, either with (O–CN) or without (O–C) a dynamic nitrogen cycle coupled to the carbon–water-energy cycles. In boreal and temperate regions, including a dynamic N cycle (O–CN) improved the fit between observed and modeled temporal changes in LAI. In contrast, in the tropics, simulated LAI from the model without the dynamic N cycle (O–C) better matched observed changes in LAI over time. Despite differential regional trends, the satellite estimate suggests an increase in the global average LAI during 1982–2002 by 0.0020 m² m^?2 y^?1. Both versions of the model substantially overestimated the rate of change in LAI over time (0.0065 m² m^?2 y^?1 for O–C and 0.0057 m² m^?2 y^?1 for O–CN), suggesting that some additional limitation mechanisms are missing in the model. We also estimated the relative importance of climate, CO₂ and N deposition as potential drivers of the temporal changes in LAI. We found that recent climate change better explained temporal changes in LAI when the dynamic N cycle was included in the model (higher ranked fit for O–CN vs. O–C). Using the O–C configuration to estimate the direct effect of climate on LAI, we quantified the importance of climate-N cycle feedbacks in explaining the LAI response. We found that the warming-induced release of N from soil organic matter decomposition explains 17.5 % of the global trend in LAI over time, however, reaching up to 40.9 % explained variance in the boreal zone, which is a more important contribution than increasing anthropogenic nitrogen deposition. Our analysis supports a strong connection between warming, N cycling, and vegetation productivity. These findings underscore the importance of including N cycling in global-scale models of vegetation response to environmental change.     

1981—2019年全球陆地生态系统碳通量变化特征及其驱动因子

陆地生态系统碳汇显著降低大气CO₂浓度上升和全球变暖的速率,受人类活动和气候变化的影响,陆地生态系统碳通量具有强烈的时空变化,其估算结果仍存在较大的不确定性,不同因子的贡献尚不清晰。为此,利用遥感驱动的陆地生态系统过程模型BEPS模拟分析了1981—2019年全球陆地生态系统碳通量的时空变化特征,评价了大气CO₂浓度、叶面积指数(Leaf Area Index, LAI)、氮沉降、气候变化对全球陆地生态系统碳收支变化的贡献。1981—2019年全球陆地生态系统总初级生产力(Gross Primary Productivity, GPP)、净初级生产力(Net Primary Productivity, NPP)和净生态系统生产力(Net Ecosystem Productivity, NEP)的平均值分别为115.3、51.3和2.7 Pg·a^-1(以碳质量计,下同),上升速率分别为0.47、0.21和0.06 Pg·a^-1。全球大部分区域GPP和NPP显著增加,NEP显著上升(p<0.05)...     

西北地区陆地生态系统植被状态参数业务化遥感研究

植被指数(NDVI)和叶面积指数(LAI)是两个非常重要的陆地生态系统植被状态参数.我们首先利用最大值(MVC)合成方法使用先进遥感数据如MODIS、AVHRR3等得到旬合成植被指数(NDVI),然后利用最新的经验方法针对不同的陆地生态系统类型反演得到叶面积指数,重点研究了我国沙尘暴发生频率较高的我国西北地区植被覆被状态及其变化情况.植被指数能够反映区域,乃至全球范围植被年季状态,用于监测陆地生态系统植物光合作用活动及其变化.植被指数作为一个基础参数能够用于计算反演更高级别的陆地生态系统状态参数.叶面积指数直接影响植被的光合作用,蒸腾作用的变化和陆面过程的能量平衡状态.在沙尘暴预测研究中使用的起沙过程模型需要将叶面积指数作为一个关键输入变量,另外,绝大多数生态过程模型模拟碳、水循环时也都需要将叶面积指数作为一个非常重要的输入变量.我们总结了最新的叶面积指数经验反演方法,针对6钟不同的陆地生态系统类型应用不同经验模型计算得到了叶面积指数.     

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level(surface-sensitive)channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index(LAI) dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels(e.g., channel 3),the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.     

The Variability of Air-sea O2 Flux in CMIP6: Implications for Estimating Terrestrial and Oceanic Carbon Sinks

The measurement of atmospheric O₂ concentrations and related oxygen budget have been used to estimate terrestrial and oceanic carbon uptake. However, a discrepancy remains in assessments of O₂ exchange between ocean and atmosphere (i.e. air-sea O₂ flux), which is one of the major contributors to uncertainties in the O₂-based estimations of the carbon uptake. Here, we explore the variability of air-sea O₂ flux with the use of outputs from Coupled Model Intercomparison Project phase 6 (CMIP6). The simulated air-sea O₂ flux exhibits an obvious warming-induced upward trend (~1.49 Tmol yr^?2) since the mid-1980s, accompanied by a strong decadal variability dominated by oceanic climate modes. We subsequently revise the O₂-based carbon uptakes in response to this changing air-sea O₂ flux. Our results show that, for the 1990?2000 period, the averaged net ocean and land sinks are 2.10±0.43 and 1.14±0.52 GtC yr^?1 respectively, overall consistent with estimates derived by the Global Carbon Project (GCP). An enhanced carbon uptake is found in both land and ocean after year 2000, reflecting the modification of carbon cycle under human activities. Results derived from CMIP5 simulations also investigated in the study allow for comparisons from which we can see the vital importance of oxygen dataset on carbon uptake estimations.     

不同陆地生态系统碳通量对GEOS-Chem模型模拟全球CO2浓度的影响

大气中CO₂含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008～2010年的生物模型SiB3（Simple Biosphere version 3）与优化后的CT2016（Carbon Tracker 2016）陆地生态系统碳通量驱动GEOS-Chem大气化学传输模型模拟全球CO₂浓度。通过分析模拟CO₂浓度的空间分布与季节变化,加深对全球碳源汇分布特点的理解,探究陆地生态系统碳通量不确定性对模拟结果的影响,进而认识陆地生态系统碳通量反演精度提升的重要性。SiB3与优化后的CT2016陆地生态系统碳通量都具有明显的季节变化,但在欧洲地区碳源汇的表现相反,其全球总量与空间分布也存在极大的不确定性。模拟CO₂浓度结果表明:在人为活动较少地区,陆地生态系统碳通量对近地面CO₂浓度空间分布起主导作用,尤其在南半球和欧洲地区模拟浓度有明显差异,且两种模拟结果的季节差异依赖于陆地生态系统碳通量的季节变化。将模拟结果与9个观测站点资料进行对比,以期选用合适的陆地生态系统碳通量来提升GEOS-Chem模拟CO₂浓度的精度。实验结果表明:两种模拟结果均能较好的模拟CO₂浓度的季节变化及其峰谷值,但CT2016模拟的CO₂浓度在多数站点处更接近观测资料,模拟准确性更高。     

Development of an Operational Hybrid Data Assimilation System at KIAPS

This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.     

An Updated Coupled Model for Land-Atmosphere Interaction. Part II： Simulations of Biological Processes

In Part I, the authors succeeded in coupling the spectral atmospheric model （SAMIL_R42L9） developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences （LASG/IAP/CAS） with the land surface model, Atmosphere-Vegetation-Interaction-Model （AVIM） and analyzed the climate basic state and land surface physical fluxes simulated by R42_AVIM. In this Part Ⅱ, we further evaluate the simulated results of the biological processes, including leaf area index （LAI）, biomass and net primary productivity （NPP） etc. Results indicate that R42_AVIM can simulate the global distribution of LAI and has good consistency with the monthly mean LAI provided by Max Planck Institute for Meteorology. The simulated biomass corresponds reasonably to the vegetation classifications. In addition, the simulated annual mean NPP has a consistent distribution with the data provided by IGBP and MODIS, and compares well with the work in literature. This land-atmosphere coupled model will offer a new experiment tool for the research on the two-way interaction between climate and biosphere, and the global terrestrial ecosystem carbon cycle.     

An Updated Coupled Model for Land-Atmosphere Interaction. Part Ⅱ: Simulations of Biological Processes

In Part Ⅰ, the authors succeeded in coupling the spectral atmospheric model (SAMIL_R42L9) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP/CAS) with the land surface model, Atmosphere-Vegetation-Interaction-Model (AVIM) and analyzed the climate basic state and land surface physical fluxes simulated by R42_AVIM. In this Part Ⅱ, we further evaluate the simulated results of the biological processes, including leaf area index (LAI), biomass and net primary productivity (NPP) etc. Results indicate that R42_AVIM can simulate the global distribution of LAI and has good consistency with the monthly mean LAI provided by Max Planck Institute for Meteorology. The simulated biomass corresponds reasonably to the vegetation classifications. In addition, the simulated annual mean NPP has a consistent distribution with the data provided by IGBP and MODIS, and compares well with the work in literature. This land-atmosphere coupled model will offer a new experiment tool for the research on the two-way interaction between climate and biosphere, and the global terrestrial ecosystem carbon cycle.     

四维变分同化和集合平方根滤波联合反演土壤湿度廓线的研究

提出将集合平方根滤波（EnSRF）估计的预报误差协方差用于四维变分（4DVAR）的同化方案（文中称混合四维变分同化方法,简称混合方法）来反演土壤湿度廓线,该方法由两个同化时段构成: 第一时段为EnSRF,第二时段为4DVAR,此种组合可以充分发挥每一同化方法的优势。通过同化表层土壤湿度观测反演土壤湿度廓线这一理想试验来验证方法的可行性,并与EnSRF和4DVAR的反演结果进行比较,结果表明, 混合方法反演的分析时刻土壤湿度廓线都优于EnSRF和4DVAR的结果。与此同时,为了克服小样本在估算背景场误差协方差矩阵时出现的虚假相关对反演的干扰, 提出在原有协方差矩阵中加入具有高斯指数函数成分来降低其影响;与修正前结果相比,反演的中下层（地下34～100 cm） 土壤湿度的均方根误差从0.036 cm3/cm3降到0.016 cm3/cm3, 降幅为55.6%, 更重要的是大大降低了部分深度处反演土壤湿度的误差, 如地下90 cm处误差从0.085 cm3/cm3降到0.024 cm3/cm3, 降幅达71.8%。     

Evaluation of the Community Land Model 3.5 with carbon and nitrogen cycles (CLM3.5CN) at a Tibetan grassland site

The Tibetan plateau plays an important role in energy and carbon cycles by providing an elevated heat source and by storing a large amount of soil carbon due to low temperature. The main vegetation of the plateau is alpine grassland. This study evaluates performance of Community Land Model 3.5 with carbon and nitrogen cycles (CLM3.5CN) over a alpine grassland in the Tibetan plateau in terms of energy and carbon fluxes in conditions of reasonable phenology and initial carbon pool comparable to observations. Comparison between model and observation shows following features. The model captures the magnitude of maximum leaf area index (LAI) but underestimats leaf mass. Net ecosystem exchange (NEE) is significantly underestimated during the growing season and soil temperature is also underestimated throughout a year with higher negative bias in winter than in other seasons. In order to examine the cause of the model deficiencies, we design four sensitivity tests: seasonal mulch; shallow rooting depth; reduction of critical soil moisture to limit the decomposition rate; smaller specific leaf area (SLA). Considering seasonal mulch improves the negative bias of soil temperature during dormant season has little effect on the NEE during the growing seasson. Underestimation of NEE during the growing season is partly due to underestimated decomposition rate which results from underestimated soil temperature and deep root placement in the soil column. Underestimation of latent heat flux during summer is partly due to use of large SLA in the model. Other deficiencies are also discussed.     

Remotely sensed soil moisture integration in an ecosystem carbon flux model. The spatial implication

While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km² pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations.     

Modeling the Impacts of Nitrogen Dynamics on Regional Terrestrial Carbon and Water Cycles over China with Noah-MP-CN

As an important part of biogeochemical cycling, the nitrogen cycle modulates terrestrial ecosystem carbon storage,water consumption, and environmental quality. Modeling the complex interactions between nitrogen, carbon and water at a regional scale remains challenging. Using China as a testbed, this study presents the first application of the nitrogenaugmented community Noah land surface model with multi-parameterization options(Noah-MP-CN) at the regional scale.Noah-MP-CN parameterizes the cons...     

植被覆盖异常变化影响陆面状况的数值模拟

利用NCAR最新的公用陆面模式CLM3．0，通过数值模拟初步研究了植被叶面积指数（LAI，leafareaindex）异常变化对陆面状况的可能影响，结果表明，植被LAI的异常变化能够引起地表能量平衡、地表水循环等陆面状况的异常。（1）植被LAI的异常变化主要影响太阳辐射在植被与地表之间的分配，以及地表的感热、潜热通量。植被LAI增大，能够引起植被吸收的太阳辐射增加，而到达土壤表面的太阳辐射减小，并导致植被的蒸发、蒸腾潜热通量增加，造成地表的蒸发潜热和感热通量不同程度的减小。（2）植被LAI增大时，植被对降水的拦截和植被叶面的蒸发增大，植被的蒸腾作用也明显增强；植被LAI增加会使得热带地区各个季节的土壤表面蒸发、地表径流减小，而土壤湿度有所增加；LAI增加造成中高纬度地区土壤蒸发的减少主要出现在夏季；LAI增加还能够引起中高纬地区冬、春积雪深度不同程度的增加，造成春末、夏初地表径流的增加。（3）植被LAI增加能够使得叶面和土壤温度有所下降，但植被LAI的变化对叶面、土壤温度的影响相对较小。