Ying-Ping Wang

A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I:: Model description and comparison with a multi-layered model

An one-layered, two-leaf canopy model which calculates the fluxes of sensible heat, latent heat and CO2 separately for sunlit and shaded leaves is presented. The two-leaf model includes: (1) a simple but robust radiation model, (2) an improved leaf model accounting for the interaction of conductance and photosynthesis and the response of stomata to water vapour pressure deficit and available soil water and (3) a new parameterisation of radiative conductance which simplifies solution of the leaf energy balance equation. Comparisons with a multi-layered model show that predicted fluxes of CO2, latent and sensible heat fluxes usually agree within 5% over a range leaf area index typical of a wheat crop grown in a temperate climate. The two-leaf model is computationally 10 times more efficient than the multi-layered model and is suitable for the incorporation into regional and global climate models. For a hypothetical canopy with a leaf area index of 5 under very dry (vapour pressure deficit of air of 2 kPa) and sunny conditions, the net canopy photosynthesis and latent heat fluxes calculated by the two-leaf model agree with those by the multi-layered model within 10% for the whole range of soil water conditions (from very dry to wet) and the sensible heat fluxes of the canopy calculated by the two-leaf model agree with those by the multi-layered model within 25 W m ˇ2 (or usually within 15%). For a canopy with leaf area index less than 2, the differences in the modelled fluxes of canopy CO2, latent or sensible heat are less than 5% between the multi-layered model and two-leaf model. Our results show that the two-leaf model can predict net photosynthesis, latent and sensible heat fluxes of a canopy quite accurately under a wide range of soil water availability and meteorological conditions, as compared with the multi-layered model. # 1998 Elsevier Science B.V. All rights reserved.

A two-big-leaf Model for canopy temperature, photosynthesis, and stomatal conductance

The energy exchange, evapotranspiration, and carbon exchange by plant canopies depend on leaf stomatal control. The treatment of this control has been required by land components of climate and carbon models. Physiological models can be used to simulate the responses of stomatal conductance to changes in atmospheric and soil environments. Big-leaf models that treat a canopy as a single leaf tend to overestimate fluxes of CO 2 and water vapor. Models that differentiate between sunlit and shaded leaves largely overcome these problems. A one-layered, two-big-leaf submodel for photosynthesis, stomatal conductance, leaf temperature, and energy fluxes is presented in this paper. It includes 1) an improved two stream approximation model of radiation transfer of the canopy, with attention to singularities in its solution and with separate integrations of radiation absorption by sunlit and shaded fractions of canopy; 2) a photosynthesis‐stomatal conductance model for sunlit and shaded leaves separately, and for the simultaneous transfers of CO 2 and water vapor into and out of the leaf—leaf physiological properties (i.e., leaf nitrogen concentration, maximum potential electron transport rate, and hence photosynthetic capacity) vary throughout the plant canopy in response to the radiation‐weight time-mean profile of photosynthetically active radiation (PAR), and the soil water limitation is applied to both maximum rates of leaf carbon uptake by Rubisco and electron transport, and the model scales up from leaf to canopy separately for all sunlit and shaded leaves; 3) a well-built quasi-Newton‐Raphson method for simultaneous solution of temperatures of the sunlit and shaded leaves. The model was incorporated into the Common Land Model (CLM) and is denoted CLM 2L. It was driven with observational atmospheric forcing from two forest sites [Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) and Boreal Ecosystem‐Atmosphere Study (BOREAS)] for 2 yr of simulation. The simulated fluxes by CLM 2L were compared with the observations, and with the results by the CLM with a single bigleaf scheme (CLM 1L) and by the CLM with the assimilation‐stomatal conductance scheme of NCAR Land Surface Model (LSM). The results showed that CLM 2L was an improvement compared to the CLM 1L and the CLM for the test cases of tropical evergreen broadleaf land cover and coniferous boreal forest.

Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies

We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2] (eCO2) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-Air CO2 Enrichment (FACE) experiments) to test alternative representations of carbon (C)–nitrogen (N) cycle processes. We decomposed the model responses into component processes affecting the response to eCO2 and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above-ground–below-ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO2 effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C–N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO2, given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.

Detection and attribution of vegetation greening trend in China over the last 30 years

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of Chinas afforestation program in explaining the spatial patterns of trend in vegetation growth.

Multi-decadal trends in global terrestrial evapotranspiration and its components

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy. II. Comparison with measurements

A new two-leaf canopy model for predicting fluxes of net radiation, sensible heat, latent heat and CO2 between plant canopies and the atmosphere was tested against 228 half-hourly micrometeorological flux measurements spanning over two months during the vegetative growth of two wheat crops. During that period green area index ranged from 1.8 to 4.5 for the fertilised crop and from 1.0 to 2.7 for the control crop. Excellent agreement was obtained between simulations and measurements for fluxes of net radiation, latent heat and CO2, although sensible heat fluxes were less satisfactory. Uncertainties in estimates for fluxes of water vapour and CO2 from the underlying soil contributed to discrepancies between measurements and simulations. Modelled canopy fluxes of both CO2 and latent heat are highly sensitive to the quantum yield of photosynthesis (mmol CO2 mol ˇ1 quanta). Fluxes of latent heat are more sensitive than CO2 to parameters describing stomatal function, while CO2 flux was more sensitive than transpiration to maximum carboxylation rates. The two-leaf model requires only a few parameters that may vary with plant species. It is computationally 10 times more efficient than an earlier described multilayered model and is suited for incorporation into regional- or global-scale climate models. # 1998 Published by Elsevier Science B.V. All rights reserved.

Limitations of nitrogen and phosphorous on the terrestrial carbon uptake in the 20th century

[1] A climate model, coupled to a sophisticated land model, is used to explore the impact of nitrogen and phosphorous limitations on carbon uptake under increasing atmospheric carbon dioxide concentration, or [CO2], from 1870 to 2009. Adding nitrogen limitation strongly reduces the capacity of land CO2 uptake under increasing [CO2]. The further limitation by phosphorous has a smaller impact on the global uptake of CO2. However, phosphorous limitation has a strong impact on regional carbon uptake: increasing CO2 sinks over North America and Eurasia and decreasing sinks over China and Australia. Thus, while the global carbon balance can be resolved with just nitrogen limitation, simulations of continental‐scale carbon sinks will need to include the additional limitation of phosphorous through the 20th century. Citation: Zhang, Q., Y. P. Wang, A. J. Pitman, and Y. J. Dai (2011), Limitations of nitrogen and phosphorous on the terrestrial carbon uptake in the 20th century, Geophys. Res. Lett., 38, L22701, doi:10.1029/2011GL049244.

Explicitly representing soil microbial processes in Earth system models

©2015. American Geophysical Union. All Rights Reserved. Microbes influence soil organic matter decomposition and the long-term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro-scale process-level understanding and measurements to macro-scale models used to make decadal- to century-long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial-explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial-explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model-data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well-curated repositories; and (3) the application of scaling methods to integrate microbial-explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.

Systematic Bias in Land Surface Models

A neural network–based flux correction technique is applied to three land surface models. It is then used to show that the nature of systematic model error in simulations of latent heat, sensible heat, and the net ecosystem exchange of CO2 is shared between different vegetation types and indeed different models .B y manipulating the relationship between the dataset used to train the correction technique and that used to test it, it is shown that as much as 45% of per-time-step model root-mean-square error in these flux outputs is due to systematic problems in those model processes insensitive to changes in vegetation parameters. This is shown in the three land surface models using flux tower measurements from 13 sites spanning 2 vegetation types. These results suggest that efforts to improve the representation of fundamental processes in land surface models, rather than parameter optimization, are the key to the development of land surface model ability.