Response of Growing Season Gross Primary Production to El Nino in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically credible.This paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.

Ocean-atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China

Since the 1950s,the terrestrial carbon uptake has been characterized by interannual variations,which are mainly determined by interannual variations in gross primary production(GPP).Using an ensemble of seven-member TRENDY(Trends in Net Land-Atmosphere Carbon Exchanges)simulations during 1951-2010,the relationships of the interannual variability of seasonal GPP in China with the sea surface temperature(SST)and atmospheric circulations were investigated.The GPP signals that mostly relate to the climate forcing in terms of Residual Principal Component analysis(hereafter,R-PC)were identified by separating out the significant impact from the linear trend and the GPP memory.Results showed that the seasonal GPP over China associated with the first R-PC1(the second R-PC2)during spring to autumn show a monopole(dipole or tripole)spatial structure,with a clear seasonal evolution for their maximum centers from springtime to summertime.The dominant two GPP R-PC are significantly related to Sea Surface Temperature(SST)variability in the eastern tropical Pacific Ocean and the North Pacific Ocean during spring to autumn,implying influences from the El Niño-Southern Oscillation(ENSO)and the Pacific Decadal Oscillation(PDO).The identified SST and circulation factors explain 13%,23%and 19%of the total variance for seasonal GPP in spring,summer and autumn,respectively.A clearer understanding of the relationships of China’s GPP with ocean-atmosphere teleconnections over the Pacific and Atlantic Ocean should provide scientific support for achieving carbon neutrality targets.

Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes.Remote sensing-based models,which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics,improve spatially extended estimates of vegetation productivity with high accuracy.In this study,the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM),which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types.The field data were collected by coordinating observations at nine stations in 2008.The results indicate that in the region during the growing season GPP was highest in cropland sites,second highest in woodland sites,and lowest in grassland sites.VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas.Further,Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas,while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation.This study demonstrates the potential of satellite-driven models for scaling-up GPP,which is a key component for studying the carbon cycle at regional and global scales.

Responses in gross primary production of Stipa krylovii and Allium polyrhizum to a temporal rainfall in a temperate grassland of Inner Mongolia, China

In the arid and semi-arid areas of China, rainfall and drought affect the growth and photosynthetic activities of plants.Gross primary productivity(GPP) is one of the most important indices that measure the photosynthetic ability of plants.This paper focused on the GPP of two representative grassland species(Stipa krylovii Roshev.and Allium polyrhizum Turcz.ex Regel) to demonstrate the effect of a temporal rainfall on the two species.Our research was conducted in a temperate grassland in New Barag Right Banner, Hulun Buir City, Inner Mongolia Autonomous Region of China, in a dry year 2015.We measured net ecosystem productivity(NEP) and ecosystem respiration flux(ER) using a transparent chamber system and monitored the photosynthetically active radiation(PAR), air and soil temperature and humidity simultaneously.Based on the measured values of NEP and ER, we calculated the GPP of the two species before and after the rainfall.The saturated GPP per aboveground biomass(GPPAGB) of A.polyrhizum remarkably increased from 0.033(±0.018) to 0.185(±0.055) μmol CO2/(gdw·s) by 5.6-fold and that of S.krylovii decreased from 0.068(±0.021) to 0.034(±0.011) μmol CO2/(gdw·s) by 0.5-fold on the 1st and 2nd d after a 9.1 mm rainfall event compared to the values before the rainfall at low temperatures below 35℃.However, on the 1st and 2nd d after the rainfall, both of the saturated GPPAGB values of S.krylovii and A.polyrhizum were significantly lower at high temperatures above 35℃(0.018(±0.007) and 0.110(±0.061) μmol CO2/(gdw·s), respectively) than at low temperatures below 35℃(0.034(±0.011) and 0.185(±0.055) μmol CO2/(gdw·s), respectively).The results showed that the GPP responses to the temporal rainfall differed between S.krylovii and A.polyrhizum and strongly negative influenced by temperature.The temporal rainfall seems to be more effective on the GPP of A.polyrhizum than S.krylovii.These differences might be related to the different physiological and structural features, the coexistence of the species and their species-specific survival strategies.

How the enhanced East Asian summer monsoon regulates total gross primary production in eastern China

Recognizing the relationship between gross primary production(GPP)and precipitation in eastern China,the East Asian Summer Monsoon(EASM)plays a crucial role in shaping GPP.Despite confirmation of the strong link between EASM and GPP,there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China.Here we used simulations from Trends in Net Land-Atmosphere Carbon Exchanges(TRENDY)models from 1951 to 2010 and divided eastern China into five subregions for the study.We also used the New East Asian Summer Monsoon Index(NEWI)as a quantitative metric to distinguish between periods of strong and weak EASM.Building on this,this study aims to investigate the response of GPP in different subregions of eastern China.Regionally,under strengthened EASM years(1954,1957,1965,1969,1977,1980,1983,1987,1993 and 1998),East China experienced the most pronounced increase in GPP at 12±21(mean±1 sigma)gC m^(-2) mon^(-1) compared to the weak EASM years(1958,1961,1972,1973,1978,1981,1985,1994,1997 and 2004).In contrast,Southwest China showed a decline in GPP at-4±10 gC m^(-2) mon^(-1).Moreover,GPP also increased in Northeast and North China when EASM strengthened,while South China showed a decline in GPP.This indicated that GPP changed with monsoon intensity.According to the mechanism analysis,during strong EASM,there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation,which further contributed to the increase in GPP.Downward solar radiation in Southwest China decreased with EASM enhancement,which suppressed GPP and hindered vegetation growth.Overall,the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

Gross primary production(GPP)is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively,yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations.This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model(BCC_AVIM2.0)in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6(CMIP6)experiments.Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008.Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset,increasing from cold(tundra)and dry(desert)biomes to warm(temperate)and humid(tropical rainforest)biomes.BCC_AVIM2.0 overestimates GPP in most parts of the globe,especially in boreal forest regions and Southeast China,while underestimating GPP in subhumid regions in eastern South America and tropical Africa.The four datasets broadly agree on the GPP seasonal cycle,but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations.The observation-based datasets exhibit slight interannual variability(IAV)and weak GPP linear trends,while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia.Regarding the possible relationships between annual means of GPP and climate factors,BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation,especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa,while the observed GPP in the above regions is temperature-or radiation-dominant.The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth,while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.

Satellite-based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau:A Multi-model Comparison

Alpine swamp meadows on the Tibetan Plateau,with the highest soil organic carbon content across the globe,are extremely vulnerable to climate change.To accurately and continually quantify the gross primary production（GPP） is critical for understanding the dynamics of carbon cycles from site-scale to global scale.Eddy covariance technique（EC） provides the best approach to measure the site-specific carbon flux,while satellite-based models can estimate GPP from local,small scale sites to regional and global scales.However,the suitability of most satellite-based models for alpine swamp meadow is unknown.Here we tested the performance of four widely-used models,the MOD17 algorithm（MOD）,the vegetation photosynthesis model（VPM）,the photosynthetic capacity model（PCM）,and the alpine vegetation model（AVM）,in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived GPP.Our results indicated that all these models provided good descriptions of the intra-annual GPP patterns（R〉20.89,P〈0.0001）,but hardly agreed with the inter-annual GPP trends.VPM strongly underestimated the GPP of alpine swamp meadow,only accounting for 54.0% of GPP_EC.However,the other three satellite-based GPP models could serve as alternative tools for tower-based GPP observation.GPP estimated from AVM captured 94.5% of daily GPP_EC with the lowest average RMSE of 1.47 g C m^（-2）.PCM slightly overestimated GPP by 12.0% while MODR slightly underestimated by 8.1% GPP compared to the daily GPP_EC.Our results suggested that GPP estimations for this alpine swamp meadow using AVM were superior to GPP estimations using the other relatively complex models.

Responses of gross primary productivity to different sizes of precipitation events in a temperate grassland ecosystem in Inner Mongolia, China

Changes in the sizes of precipitation events in the context of global climate change may have profound impacts on ecosystem productivity in arid and semiarid grasslands. However, we still have little knowledge about to what extent grassland productivity will respond to an individual precipitation event. In this study, we quantified the duration, the maximum, and the time-integrated amount of the response of daily gross primary productivity (GPP) to an individual precipitation event and their variations with different sizes of precipitation events in a typical temperate steppe in Inner Mongolia, China. Results showed that the duration of GPP-response (τR) and the maximum absolute GPP-response (GPPmax) increased linearly with the sizes of precipitation events (Pes), driving a corresponding increase in time-integrated amount of the GPP-response (GPPtotal) because variations of GPPtotal were largely explained by τR and GPPmax. The relative contributions of these two parameters to GPPtotal were strongly Pes-dependent. The GPPmax contributed more to the variations of GPPtotal when Pes was relatively small (<20 mm), whereas τR was the main driver to the variations of GPPtotal when Pes was relatively large. In addition, a threshold size of at least 5 mm of precipitation was required to induce a GPP-response for the temperate steppe in this study. Our work has important implications for the modeling community to obtain an advanced understanding of productivity-response of grassland ecosystems to altered precipitation regimes.

Contributions of climate change, land use change and CO2 to changes in the gross primary productivity of the Tibetan Plateau

The ecosystems on the Tibetan Plateau(TP) are highly vulnerable to climate change, rising CO2 concentration, and land-use and land-cover change(LULCC), but their contributions to changes in the gross primary productivity(GPP) of the TP are not clearly understood. In this study, the role of these three factors on the interannual variations(IAVs) and trends of the TP’s GPP were investigated using 12 terrestrial biosphere models. The ensemble simulations showed that climate change can explain most of the changes in the GPP, while the direct effect of LULCC and rising CO2(mainly fertilization effect) contributed 10% and-14% to the mean GPP values, 37% and -20% to the IAV, and 52% and -24% to the GPP’s trend, respectively. The LULCC showed higher contributions to the significant positive trend in the annual GPP of the TP. However, the results from different model simulations showed that considerable uncertainties were associated with the effects of LULCC on the GPP of the TP.

Sensitivity of Near Real-time MODIS Gross Primary Productivity in Terrestrial Forests Based on Eddy Covariance Measurements

As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

Clipping Alters the Response of Biomass Production to Experimental Warming: A Case Study in an Alpine Meadow on the Tibetan Plateau, China

Predicting how human activity will influence the response of alpine grasslands to future warming has many uncertainties.In this study, a field experiment with controlled warming and clipping was conducted in an alpine meadow at three elevations（4313 m, 4513 m and 4693 m） in Northern Tibet to test the hypothesis that clipping would alter warming effect on biomass production.Open top chambers（OTCs） were used to increase temperature since July,2008 and the OTCs increased air temperature by approximately 0.9o C ~ 1.8o C during the growing in2012.Clipping was conducted three times one year during growing season and the aboveground parts of all live plants were clipped to approximately 0.01 m in height using scissors since 2009.Gross primary production（GPP） was calculated from the Moderate-Resolution Imaging Spectroradiometer GPP algorithm and aboveground plant production was estimated using the surface-measured normalized difference vegetation index in 2012.Warming decreased the GPP, aboveground biomass（AGB） and aboveground net primary production（ANPP） at all three elevations when clipping was not applied.In contrast, warming increased AGB at all three elevations, GPP at the two lower elevations and ANPP at the two higher elevations when clipping was applied.These findings show that clipping reduced the negative effect of warming on GPP, AGB and ANPP, suggesting that clipping may reduce the effect of climate warming on GPP, AGB and ANPP in alpine meadows on the Tibetan Plateau, and therefore, may be a viable strategy for mitigating the effects of climate change on grazing and animal husbandry on the Tibetan Plateau.

Analysis of the Spatiotemporal Variation Characteristics and Driving Factors of Land Vegetation GPP in a Certain Region of Asia

Gross primary productivity (GPP) of vegetation is a critical indicator of ecosystem growth and carbon sequestration. The spatiotemporal variation characteristics of land vegetation GPP trends in a specific region of Asia from 2001 to 2020 were analyzed by Sen and MK trend analysis methods in this study .Moreover , a GPP change attribution model was established to explore the driving influences of factors such as Leaf Area Index (LAI), Land Surface Temperature (LST), Vapor Pressure Deficit (VPD), Soil Moisture, Solar Radiation and Wind Speed on GPP. The results indicate that summer GPP values are significantly higher than those in other months, accounting for 60.8% of the annual total GPP;spring and autumn contribute 18.91% and 13.04%, respectively. In winter, due to vegetation being nearly dormant, the contribution is minimal at 7.19%. Spatially, GPP shows a decreasing trend from southeast to northwest. LAI primarily drives the spatial and seasonal variations of regional GPP, while VPD, surface temperature, solar radiation, and soil moisture have varying impacts on GPP across different dimensions. Additionally, wind speed exhibits a minor contribution to GPP across different dimensions.

Using triple oxygen isotopes and oxygen-argon ratio to quantify ecosystem production in the mixed layer of northern South China Sea slope region

Quantifying the gross and net production is an essential component of carbon cycling and marine ecosystem studies.Triple oxygen isotope measurements and the O_(2)/Ar ratio are powerful indices in quantifying the gross primary production and net community production of the mixed layer zone,respectively.Although there is a substantial advantage in refining the gas exchange term and water column vertical mixing calibration,application of mixed layer depth history to the gas exchange term and its contribution to reducing indices error are unclear.Therefore,two cruises were conducted in the slope regions of the northern South China Sea in October 2014(autumn)and June 2015(spring).Discrete water samples at Station L07 in the upper 150 m depth were collected for the determination ofδ^(17)0,δ^(18)O,and the O_(2)/Ar ratio of dissolved gases.Gross oxygen production(GOP)was estimated using the triple oxygen isotopes of the dissolved O_(2),and net oxygen production(NOP)was calculated using O_(2)/Ar ratio and O_(2)concentration.The vertical mixing effect in NOP was calibrated via a N_(2)O based approach.GOP for autumn and spring was(169±23)mmol/(m^(2)·d)(by O_(2))and(189±26)mmol/(m^(2)·d)(by O_(2)),respectively.While NOP was 1.5 mmol/(m^(2)·d)(by O_(2))in autumn and 8.2 mmol/(m^(2)·d)(by O_(2))in spring.Application of mixed layer depth history in the gas flux parametrization reduced up to 9.5%error in the GOP and NOP estimations.A comparison with an independent O_(2)budget calculation in the diel observation indicated a26%overestimation in the current GOP,likely due to the vertical mixing effect.Both GOP and NOP in June were higher than those in October.Potential explanations for this include the occurrence of an eddy process in June,which may have exerted a submesoscale upwelling at the sampling station,and also the markedly higher terrestrial impact in June.

Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia

Variations in temperature and precipitation affect local ecosystems. Considerable spatial and temporal heterogeneity exists in arid ecosystems such as desert steppes. In this study, we analyzed the spatiotemporal dy- namics of climate and vegetation phenology in the desert steppe of Inner Mongolia, China using meteorological data （1961-2010） from 11 stations and phenology data （2004-2012） from 6 ecological observation stations. We also estimated the gross primary production for the period of 1982-2009 and found that the annual mean tem- perature increased at a rate of 0.47~C/decade during 1961-2010, with the last 10 years being consistently warmer than the 50 years as an average. The most significant warming occurred in winters. Annual precipitation slightly decreased during the 50-year period, with summer precipitation experiencing the highest drop in the last 10 years, and spring precipitation, a rise. Spatially, annual precipitation increased significantly in the northeastern and eastern central areas next to the typical steppe. From 2004 to 2012, vegetation green-up and senescence date advanced in the study area, shortening the growing season. Consequently, the primary productivity of the desert steppe de- creased along the precipitation gradient from southeast to northwest. Temporally, productivity increased during the period of 1982-1999 and significantly decreased after 2000. Overall, the Last decade witnessed the most dramatic climatic changes that were likely to negatively affect the desert steppe ecosystem. The decreased primary produc- tivity, in particular, decreases ecosystem resilience and impairs the livelihood of local farmers and herdsmen.

Improving GPP estimates by partitioning green APAR from total APAR in two deciduous forest sites

Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.

Changes in Water Use Efficiency Caused by Climate Change,CO_(2) Fertilization,and Land Use Changes on the Tibetan Plateau

Terrestrial ecosystem water use efficiency(WUE)is an important indicator for coupling plant photosynthesis and transpiration,and is also a key factor linking the carbon and water cycles between the land and atmosphere.However,under the combination of climate change and human intervention,the change in WUE is still unclear,especially on the Tibetan Plateau(TP).Therefore,satellite remote sensing data and process-based terrestrial biosphere models(TBMs)are used in this study to investigate the spatiotemporal variations of WUE over the TP from 2001 to 2010.Then,the effects of land use and land cover change(LULCC)and CO_(2) fertilization on WUE from 1981-2010 are assessed using TBMs.Results show that climate change is the leading contributor to the change in WUE on the TP,and temperature is the most important factor.LULCC makes a negative contribution to WUE(-20.63%),which is greater than the positive contribution of CO_(2) fertilization(11.65%).In addition,CO_(2) fertilization can effectively improve ecosystem resilience on the TP.On the northwest plateau,the effects of LULCC and CO_(2) fertilization on WUE are more pronounced during the driest years than the annual average.These findings can help researchers understand the response of WUE to climate change and human activity and the coupling of the carbon and water cycles over the TP.

Seasonal and Interannual Variations of Carbon Exchange over a Rice–Wheat Rotation System on the North China Plain

Rice-wheat （R-W） rotation systems are ubiquitous in South and East Asia, and play an important role in modulating the carbon cycle and climate. Long-term, continuous flux measurements help in better understanding the seasonal and interannual variation of the carbon budget over R-W rotation systems. In this study, measurements of CO2 fluxes and meteorological variables over an R-W rotation system on the North China Plain from 2007 to 2010 were analyzed. To analyze the abiotic factors regulating Net Ecosystem Exchange （NEE）, NEE was partitioned into gross primary production （GPP） and ecosystem respiration. Nighttime NEE or ecosystem respiration was controlled primarily by soil temperature, while daytime NEE was mainly determined by photosythetically active radiation （PAR）. The responses of nighttime NEE to soil temperature and daytime NEE to light were closely associated with crop development and photosynthetic activity, respectively. Moreover, the interannual variation in GPP and NEE mainly depended on precipitation and PAR. Overall, NEE was negative on the annual scale and the rotation system behaved as a carbon sink of 982 g C m 2 per year over the three years. The winter wheat field took up more CO2 than the rice paddy during the longer growing season, while the daily NEE for wheat and rice were -2.35 and -3.96 g C m-2, respectively. After the grain harvest was subtracted from the NEE, the winter wheat field became a moderately strong carbon sink of 251-334 g C m-2 per season, whereas the rice paddy switched to a weak carbon sink of 107-132 per season.