Simulated Net Primary Production Research Articles

Assessing net primary production in the northwestern Barents Sea using in situ, remote sensing and modelling approaches

The northwestern Barents Sea (NW-BS) is a highly productive region within the transitional zones of an Atlantic to Arctic-dominated marine ecosystem. The steep latitudinal gradients in sea ice concentration, Atlantic and Arctic Water, offer an opportunity to test hypotheses on physical drivers of spatial and temporal variability of net primary production (NPP). However, quantifying NPP in such a large ocean region can be challenged by the lack of in situ measurements with high spatial and temporal resolution, and gaps in remote sensing estimates due to the presence of clouds and sea ice, and assumptions regarding the depth distribution of alga biomass. Without reliable data to evaluate models, filling these gaps with numerical models is limited by the model representation of the physical environment and its assumptions about the relationships between NPP and its main limiting factors. Hence, within the framework of the Nansen Legacy Project, we combined in situ measurements, remote sensing, and model simulations to constrain the estimates of phytoplankton NPP in the NW-BS. The region was subdivided into Atlantic, Subarctic, and Arctic subregions on the basis of different phytoplankton phenology. In 2004 there was a significant regime change in the Atlantic subregion that resulted in a step-increase in NPP in tandem with a step-decrease in sea ice concentration. Contrary to results from other Arctic seas, this study does not find any long term trends in NPP despite changes in the physical environment. Mixing was the main driver of simulated annual NPP in the Atlantic subregion, while light and nutrients drove annual NPP in the Subarctic and Arctic subregions. The multi-source estimate of annual NPP ranged 79–118gCm−2yr−1 in the Atlantic, 74–82gCm−2yr−1 in the Subarctic, and 19–47gCm−2yr−1 in the Arctic. The total NPP in the NW-BS region was estimated between 15 and 48TgCyr−1, which is 15–50% of the total NPP needed to sustain three of the most harvested fish species north of 62°N (roughly 90TgCyr−1). This research shows the importance of continuing to strive for better regional estimates of NPP.

Across-model spread and shrinking in predicting peatland carbon dynamics under global change.

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2 ) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Simulating the potential forest ranges in an old land: the case for Turkey’s forests

The central aim of this study is to estimate the current potential distribution of Turkey’s forests and their composition in absence of anthropogenic landcover change, and in this manner to contribute to past studies on the distribution of European forest taxa by filling an important spatial gap. For our simulations, we used high resolution climate data from ERA5-Land and a process based dynamic regional to global vegetation model (LPJ-GUESS), for which we modified the original European tree species list to match the climatic sensitivities that the species in turn exhibit throughout their natural ranges in Turkey, alternating between subspecies where necessary. This is the first application of LPJ-GUESS for the entire country using high resolution climate data. We defined the climatic tolerances for the model of three conifer species with wide distribution in Turkey: Pinus nigra, Pinus brutia, and Cedrus libani. We first identified relative bioclimatic tolerances of species for Turkey by way of comparing their current recorded distributions to ERA5-Land reanalysis data using observation data from the General Directorate of Forestry of Turkey, and later through ensemble runs with LPJ-GUESS we finetuned their parameters. Finally, we compared simulated net primary production to MODIS remote sensing observation estimates. In general, LPJ-GUESS captured the natural distribution ranges of different forest taxa, especially after the introduction of three additional species native to Turkey, highlighting competition for resources. Due to probable regional acclimatization of species, our final set of parameters for the different plant functional types offers a suitable alternative for future studies.

Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060

BackgroundForest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5).ResultsThe runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060.ConclusionsThe obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.

Regional contributions to interannual variability of net primary production and climatic attributions

Climate change profoundly affects the interannual variability (IAV) of net primary production (NPP) of terrestrial ecosystem from multiple aspects. However, the IAV of the nationwide annual NPP in China and the impacts of distinct climatic drivers are not well addressed. In this study, we investigated regional contributions to the IAV of the nationwide NPP and quantified the contributions of nine climatic drivers in different regions using the NPP estimated by the Carnegie-Ames-Stanford (CASA) model from 1982 to 2018. Our results showed that the simulated NPP exhibited an increasing trend of 15.2 Tg C yr-1 at the national scale. The nationwide NPP also showed large IAV ranging from ‍-0.29 to 0.22 Pg C with the mean absolute NPP IAV showing a descending gradient from southeastern to northwestern China. Our estimates and thirteen terrestrial biosphere models verified that humid region accounted for the largest contribution (62%) to this large IAV. Attribution analyses indicated that normal and high precipitation amount (nP, HP), as well as high temperature days (HT) and daily temperature range (DTR) exerted the largest contributions to the national NPP IAV. Regional analyses indicated that DTR and HP were the major climatic drivers to NPP IAV in humid region, whereas NPP IAV in water-limited regions (i.e., semi-humid, semi-arid, and arid regions) were tightly associated with nP, HP, and HT. DTR and nP exerted the largest contributions to NPP IAV in the Tibetan Plateau. However, more attention should be paid to the negative impacts of low temperature events and potential drought on NPP IAV in humid region and that of HT in water-limited regions. This study emphasized the dominant role of humid region in controlling the national NPP IAV and the different ecosystem responses to diverse climatic drivers, and therefore can be valuable for adaptive management of ecosystems when facing climate change.

Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau

AbstractThere are still considerable uncertainties related to the numerical simulation and prediction of net primary production (NPP) as an important part of terrestrial carbon sources and sinks over the Tibetan Plateau (TP). To reduce the uncertainty of numerical simulations and improve the ability of predictions, the key physical processes related to the uncertainty of simulated NPP are identified at nine observational stations over the TP. A sensitivity analysis of parameter combinations based on the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP‐P) approach, which can be used to assess the sensitivity of a parameter subset, is conducted for 28 target physical parameters in the Lund‐Potsdam‐Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ‐WHyMe v1.3.1). Firstly, the numerical results show that the uncertainties of physical parameters do lead to a large error in the simulated NPP over the TP, and the range of error varies from 72.4 (MS 3478) to 150.5 g C m−2 year−1 (Ngari station). Secondly, in areas of moderate precipitation over the TP, the photosynthesis is the main factor leading to high uncertainty in NPP modeling. In areas of low and high precipitation over the TP, the combined influences of hydrological processes and photosynthesis play a key role. Finally, eliminating the errors associated with the most sensitive and important parameter combinations led to the maximum benefit in terms of reducing the uncertainty of simulated NPP, when compared to that obtained with the traditional method. This study suggests that we should prioritize reducing the uncertainty of relatively sensitive parameter combinations among all physical parameters to improve the prediction or simulation ability of NPP over the TP.

How Simulations of the Land Carbon Sink Are Biased by Ignoring Fluvial Carbon Transfers: A Case Study for the Amazon Basin

Summary Land-surface models are important tools for simulation of the past, present, and future capacity of terrestrial ecosystems to absorb anthropogenic CO2 emissions. However, fluvial carbon (C) transfers are presently neglected in these models. Using the Amazon basin as a case study, we show that this negligence leads to significant underestimation of the net uptake of atmospheric C while terrestrial C storage changes are overestimated. These biases arise from the fact that C—in reality, leached from soils and exported through the river network—is instead represented as partly being respired and partly being stored in soils. Moreover, these biases scale mainly to the fluvial C export to the coast, despite aquatic CO2 emission to the atmosphere being the major pathway of riverine C exports. We further show that fluvial C transfers may change significantly in response to changes in either hydrology or in atmospheric C uptake by vegetation.

Spatial-temporal patterns of net primary production in Anji (China) between 1984 and 2014

Abstract Long-term and persistent human disturbances, such as the changes in land use types, have degraded the carbon fixation capacity of forest ecosystems. In Anji, China, the changes in land use types, such as the urbanization and increase of Moso bamboo forest, had changed the distribution of the broadleaf forest, needle leaf forest, and Moso bamboo forest, which in turn affected the net primary production (NPP) at the regional scale. This paper used the Carnegie-Ames-Stanford approach (CASA) model to estimate NPP in Anji during the period 1984–2014. The referenced NPP measured by eddy covariance technique was used to validate the simulated NPP, and the results show that the simulated NPP was consistent with the referenced NPP (R2 = 0.90, p

Future supply and demand of net primary production in the Sahel

Abstract. In the 21st century, climate change in combination with increasing demand, mainly from population growth, will exert greater pressure on the ecosystems of the Sahel to supply food and feed resources. The balance between supply and demand, defined as the annual biomass required for human consumption, serves as a key metric for quantifying basic resource shortfalls over broad regions.Here we apply an exploratory modelling framework to analyse the variations in the timing and geography of different NPP (net primary production) supply–demand scenarios, with distinct assumptions determining supply and demand, for the 21st century Sahel. We achieve this by coupling a simple NPP supply model forced with projections from four representative concentration pathways with a global, reduced-complexity demand model driven by socio-economic data and assumptions derived from five shared socio-economic pathways.For the scenario that deviates least from current socio-economic and climate trends, we find that per capita NPP begins to outstrip supply in the 2040s, while by 2050 half the countries in the Sahel experience NPP shortfalls. We also find that despite variations in the timing of the onset of NPP shortfalls, demand cannot consistently be met across the majority of scenarios. Moreover, large between-country variations are shown across the scenarios, in which by the year 2050 some countries consistently experience shortage or surplus, while others shift from surplus to shortage. At the local level (i.e. grid cell), hotspots of total NPP shortfall consistently occur in the same locations across all scenarios but vary in size and magnitude. These hotspots are linked to population density and high demand. For all scenarios, total simulated NPP supply doubles by 2050 but is outpaced by increasing demand due to a combination of population growth and the adoption of diets rich in animal products. Finally, variations in the timing of the onset and end of supply shortfalls stem from the assumptions that underpin the shared socio-economic pathways rather than the representative concentration pathways.Our results suggest that the UN sustainable development goals for eradicating hunger are at high risk for failure. This emphasizes the importance of policy interventions such as the implementation of sustainable and healthy diets, family planning, reducing yield gaps, and encouraging the transfer of resources to impoverished areas via trade relations.

The response of the net primary production of Moso bamboo forest to the On and Off-year management: A case study in Anji County, Zhejiang, China

Abstract The On and Off-year management affects the carbon sequestration capacity and carbon allocation of Moso bamboo forest. However, to date, the impact of the On and Off-year management on stand-scale NPP has not been estimated. This paper used the Triplex-Flux model to estimate the 30-min averaged net primary production (NPP) and carbon use efficiency (CUE) of the Moso bamboo stand in Anji during the period 2011–2014. Comparison of the simulated NEP and NPP with the observed NEP from the eddy covariance measurements and the NPP from the previous studies in Moso bamboo forest showed that the Triplex-Flux model was suitable for NPP and CUE estimation in the Moso bamboo stand in Anji County. The annual averaged NPP and CUE were 835.58 g C m−2 year−1 and 0.53 year−1, which were higher than those of the other forest types in subtropical China. In Anji County, the On and Off-year management affects the NPP and CUE of Moso bamboo forest. Overall, the NPP and CUE in the On-year were higher than those in the Off-year. The selective bamboo cutting and culm sheaths hooking that lasts from September of the On-year to February of the Off-year reduced the NPP and CUE of Moso bamboo forest by 25.81% and 14.93%. The fresh bamboo shoots harvest in the Off-year significantly reduced the NPP and CUE of Moso bamboo forest by 40.17% and 16.36%.

Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feedbacks of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of aboveground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about 1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly-sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to-year variation in plant growth.

Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models

The net primary production (NPP) is a key indicator for assessing the terrestrial carbon pools and fluxes from the atmosphere to biosphere in any given ecosystem. Adequate measurement of the sensitivity and uncertainty of regional and global carbon pools and fluxes in different climatic and anthropogenic regimes is needed to properly investigate the terrestrial carbon balance. Remote sensing light use efficiency (LUE) approaches can be used to quantify the terrestrial NPP accurately. Using LUE models, NPP was calculated for last two decades of an estuarine ecosystem, the Sundarban Biosphere region, India. Results from seven LUE models: Carnegie-Ames-Stanford Approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), Eddy Covariance-Light Use Efficiency (EC-LUE), MODerate resolution Imaging Spectroradiometer (MOD17), Temperature and Greenness (TG), Greenness and Radiation (GR) models were compared to ascertain model consistency for NPP estimation during the study period 2000–2013. To optimize structural biases in the model accurate parameterization and systematic multi-method assessment were employed. The influence of the input drivers (biophysical, bioclimatic and environmental stress) on model performances was evaluated. Best model performances were observed over cropland, followed by mixed forest and mangrove ecoregion, respectively. Amongst all model, EC-LUE simulated higher NPP at mangrove ecoregion, while the MOD17 model simulated lower NPP in most of the evaluated biomes. In addition, TG and GR models exhibited larger unexplained variances and found statistically significant at mixed forest site. This error was attributed to the absence of environmental stress factors used to drive these model. GLO-PEM and VPM corroborate with NPP prediction among all LUE models. All seven LUE models predict the statistically significant NPP across the biomes and, the poor model performance is attributed to the different parameterization scheme executed for defining the biophysical and stress variables. Biophysical drivers mostly controlled the model performances, followed by environmental stress and bioclimatic drivers, respectively. This analysis is suggesting that the TG and GR model (driven only by the biophysical factors) could be useful in NPP predictions in the regions with no meteorological inputs and real-time eddy covariance flux tower measurement.

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

The analyses of the net primary production due to regional and seasonal temperature differences in eastern China using the LPJ model

A kind of temperature change scenario is supplied by the approach of conditional nonlinear optimal perturbation related to parameter (CNOP-P) to estimate the variation of the net primary production (NPP) in North–South transect of eastern China within a state-of-the-art Lund-Potsdam-Jena dynamical global vegetation model (LPJ DGVM). There are two traits for the kind of temperature change scenario. Firstly, the kind of temperature change scenario considers the regional and seasonal differences in North–South transect of eastern China. The character of the temperature change is similar to the observation data due to the observational constraint. Secondly, the kind of temperature change scenario causes the maximal possible impact on the simulated NPP to discuss the maximal uncertainty in the simulated NPP to the temperature change in North–South transect of eastern China. Other two kinds of temperature change scenarios are also applied to explain the above two traits and to analyze variations due to different kinds of temperature change scenarios.It is shown that the kind of temperature change scenario resulted of the CNOP-P approach, which is called as the CNOP-P-type temperature change scenario, exhibits the regional and seasonal temperature differences in North–South transect of eastern China. The NPP decreases by 1.84% in northern China, and respectively increases by 4.09% and 18.99% in northeastern and southern China as the results of the CNOP-P-type temperature change scenario, though the NPP increases in small part of northern China and decreases in part of northeastern China. The variations in the NPP caused by the CNOP-P-type temperature change scenario are different to those by the other two types of temperature change scenarios in northern, northeastern China and southern China. The impact of the CNOP-P-type temperature change scenario on the NPP is intenser than that of the other two types of temperature change scenarios. The seasonal analyses demonstrate that the differences among the variations in the NPP due to three types of temperature change scenarios principally stem from the variations in spring, summer and autumn. The above results imply that the regional and seasonal temperature change play a key role in estimating the uncertainty of the NPP, and the CNOP-P approach could afford a possible temperature change scenario to reflect the regional and seasonal temperature change.

Tree species composition affects productivity and carbon dynamics of different site types in boreal forests

AbstractThe objective was to analyse how differences in the initial proportions of tree species and site fertility affect carbon sequestration in living biomass and soil. We used the individual-based simulation model EFIMOD, which is able to simulate spatially explicit competition between trees for light and nutrients. Simulations were carried out for three site types with distinct initial stocks of soil nutrients. For each site, the 100-years undisturbed dynamics of monocultures and mixtures of three tree species (Betula pendulaRoth,Pinus sylvestrisL. andPicea abies(L.) H. Karst.) was predicted. Changes in the proportions of competing tree species were dependent on the fertility of the site: on poor sites, pine was the most competent species, while on rich sites, spruce increased its proportion during stand succession. Net primary production (NPP) and soil respiration were the highest in stands of two coniferous species and in stands with a high initial proportion of pine. Mixed stands were more productive than monocultures; the highest overyielding was observed with mixtures of two coniferous species. Simulated NPP and carbon stocks in all pools increased from poor to rich sites. The highest carbon stocks in standing biomass were observed for mixtures of conifer species and three-species mixtures; the greatest accumulation of forest floor occurred in stands with high proportions of pine.

An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains.

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine (Pinus ponderosa). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities.

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.

Global-scale carbon and energy flows through the marine planktonic food web: An analysis with a coupled physical–biological model

Global-scale planktonic ecosystem models exhibit large differences in simulated net primary production (NPP) and assessment of planktonic food web fluxes beyond primary producers has been limited, diminishing confidence in carbon flux estimates from these models. In this study, a global ocean-ice-ecosystem model was assessed against a suite of observation-based planktonic food web flux estimates, many of which were not considered in previous modeling studies. The simulation successfully captured cross-biome differences and similarities in these fluxes after calibration of a limited number of highly uncertain yet influential parameters. The resulting comprehensive carbon budgets suggested that shortened food webs, elevated growth efficiencies, and tight consumer-resource coupling enable oceanic upwelling systems to support 45% of pelagic mesozooplankton production despite accounting for only 22% of ocean area and 34% of NPP. In seasonally stratified regions (42% of ocean area and 40% of NPP), weakened consumer-resource coupling tempers mesozooplankton production to 41% and enhances export below 100m to 48% of the global total. In oligotrophic systems (36% of ocean area and 26% of NPP), the dominance of small phytoplankton and low consumer growth efficiencies supported only 14% of mesozooplankton production and 17% of export globally. Bacterial production, in contrast, was maintained in nearly constant proportion to primary production across biomes through the compensating effects of increased partitioning of NPP to the microbial food web in oligotrophic ecosystems and increased bacterial growth efficiencies in more productive areas. Cross-biome differences in mesozooplankton trophic level were muted relative to those invoked by previous work such that significant differences in consumer growth efficiencies and the strength of consumer-resource coupling were needed to explain sharp cross-biome differences in mesozooplankton production. Lastly, simultaneous consideration of multiple flux constraints supports a highly distributed view of respiration across the planktonic food web rather than one dominated by heterotrophic bacteria. The solution herein is unlikely unique in its ability to explain observed cross-biome energy flow patterns and notable misfits remain. Resolution of existing uncertainties in observed biome-scale productivity and increasingly mechanistic physical and biological model components should yield significant refinements to estimates herein.

Climatic trends of the equatorial undercurrent: A backup mechanism for sustaining the equatorial Pacific production

The Equatorial Undercurrent (EUC) is the major source of iron to the equatorial Pacific and it is sensitive to climatic changes as other components of the tropical Pacific. This work proposes a methodology based on a Lagrangian approach aimed at understanding the changes in the transport of iron rich waters to the EUC in a future climate change scenario, using climate model data from an Earth system model. A selected set of regions from the northern and southern extra-equatorial Pacific has been chosen. These regions are characterized by the presence of iron sources from continental shelf processes like the Papua New Guinea region and atmospheric deposition like the northern subtropical gyre. The trajectories that reach the EUC during the 20th and the 21st century departing from these areas have been analyzed using a set of statistics designed to determine variations in the amount of transport and in the travel times of the water masses. The transport of waters to the EUC from the north Pacific subtropical gyre and from the Bismarck Sea is projected to increase during the 21st century. The increase is particularly significant for water masses from the northern subtropical gyre, with travel times lower than 10years in the second half of the 21st century. This increased interaction between the extra-tropics and the EUC may bring additional iron-rich waters in the high-nutrient low-chlorophyll region of the equatorial Pacific compatibly with the significant increase of the simulated net primary production found in the biogeochemical model, thus partly offsetting the anticipated decrease of production implied by the surface warming.

Impacts of increased variability in precipitation and air temperature on net primary productivity of the Tibetan Plateau: a modeling analysis

We analyzed interannual variability (IAV) of precipitation and air temperature over a 40-year period (1969–2008) for 11 sites along a precipitation gradient on the Tibetan Plateau. The observed IAV for both precipitation and air temperature decreases with increasing mean annual precipitation. Using Biome-BGC, a process-based ecosystem model, we simulated net primary production (NPP) along this gradient and find that the IAV of NPP is positively correlated to the IAV of precipitation and temperature. Following projected climate change scenarios for the Tibetan Plateau, our simulations suggest that with increasing IAV of precipitation and temperature, the IAV of NPP will also increase and that climate thresholds exist that, if surpassed, lead to ecosystem die-off. The impacts of these changes on ecosystem processes and climate-vegetation feedbacks on the rapidly warming Tibetan Plateau are potentially quite significant.