Methane emissions from floodplain swamps of the Ogeechee River: long-term patterns and effects of climate change

Patterns and rates of wetland methane emissions and their sensitivity to potential climate change are critical components of the global methane cycle. In this study, we use empirical simulation models to investigate these processes in floodplain swamps of the Ogeechee River in Georgia, U.S.A. We developed statistical models that relate methane emissions to monthly climate and river flow based on field observations of methane emissions from this system made during 1987–1989. Models were then applied to observed climate and hydrograph for 1937–1989 and to simulated altered climates. Altered climates were generated from the present-day climate by changing monthly temperatures by a constant amount and/or changing monthly precipitation by a constant proportion, thus altering long-term averages and preserving year-to-year variation.Under the present-day climate regime, simulated methane emissions were variable between years and responded very strongly to changes in river discharge. The long-term average was 27 g C m^-2 yr^-1, with no significant linear trend over the model period. In the altered climate simulations, methane emissions were very sensitive to changes in precipitation amounts, with a 20% decrease in rainfall resulting in 30–43% declines in methane emissions. Predicted effects of temperature changes on methane emissions were less consistent, and were strongly dependent on assumptions made about the response of evapotranspiration to elevated temperatures. In general, hydrologic impacts of changes in evapotranspiration rates (such as may occur in response to temperature shifts) were more important than direct temperature effects on methane production.     

Criteria for assessing climate change impacts on ecosystems

There is concern about the potential impacts of climate change on species and ecosystems. To address this concern, a large body of literature has developed in which these impacts are assessed. In this study, criteria for conducting reliable and useful assessments of impacts of future climate are suggested. The major decisions involve: clearly defining an emissions scenario; selecting a climate model; evaluating climate model skill and bias; quantifying General Circulation Model (GCM) between-model variability; selecting an ecosystem model and assessing uncertainty; properly considering transient versus equilibrium responses; including effects of CO(2) on plant response; evaluating implications of simplifying assumptions; and considering animal linkage with vegetation. A sample of the literature was surveyed in light of these criteria. Many of the studies used climate simulations that were >10 years old and not representative of best current models. Future effects of elevated CO(2) on plant drought resistance and productivity were generally included in growth model studies but not in niche (habitat suitability) studies, causing the latter to forecast greater future adverse impacts. Overly simplified spatial representation was frequent and caused the existence of refugia to be underestimated. Few studies compared multiple climate simulations and ecosystem models (including parametric uncertainty), leading to a false impression of precision and potentially arbitrary results due to high between-model variance. No study assessed climate model retrodictive skill or bias. Overall, most current studies fail to meet all of the proposed criteria. Suggestions for improving assessments are provided.     

Carbon–Temperature–Water change analysis for peanut production under climate change: a prototype for the AgMIP Coordinated Climate‐Crop Modeling Project (C3MP)

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate‐Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO₂]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO‐Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO₂], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain‐fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.     

Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation

Aim  To test how well species distributions and abundance can be predicted following invasion and climate change when using only species distribution and abundance data to estimate parameters.
Location  Models were developed for the species' native range in the Americas and applied to Australia.
Methods  We developed a predictive model for an invasive neotropical shrub ( Parkinsonia aculeata) using a popular ecophysiological bioclimatic modelling technique (CLIMEX) fitted against distribution and abundance data in the Americas. The effect of uncertainty in model parameter estimates on predictions in Australia was tested. Alternative data sources were used when model predictions were sensitive to uncertainty in parameter estimates. The resulting best-fit model was run under two climate change scenarios.
Results  Of the 19 parameters used, 9 could not be fitted using data from the native range. However, only parameters that lowered temperature or increased moisture requirements for growth noticeably altered the model prediction in Australia. Differences in predictions were dramatic, and reflect climates in Australia that were not represented in the Americas (novel climates). However, these poorly fitted parameters could be fitted post hoc using alternative data sources prior to predicting responses to climate change.
Conclusions  Novel climates prevented the development of a predictive model which relied only on native-range distribution and abundance data because certain parameters could not be fitted. In fact, predictions were more sensitive to parameter uncertainty than to climate change scenarios. Where uncertainty in parameter estimates affected predictions, it could be addressed through the inclusion of alternative data sources. However, this may not always be possible, for example in the absence of post-invasion data.     

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45(o)N and polewards) for the period 1900-2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.     

Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

A tool for simulating and communicating uncertainty when modelling species distributions under future climates

相似文献   

Implications of Uncertainty and Variability in the Life Cycle Assessment of Pig Production Systems(7 pp)

Goal, Scope and Background Calculating LCA outcomes implies the use of parameters, models, choices and scenarios which introduce uncertainty, as they imperfectly account for the variability of both human and environmental systems. The analysis of the uncertainty of LCA results, and its reduction by an improved estimation of key parameters and through the improvement of the models used to convert emissions into regional impacts, such as eutrophication, are major issues for LCA. Methods In a case study of pig production systems, we propose a simple quantification of the uncertainty of LCA results (intra-system variability) and we explore the inter-system variability to produce more robust LCA outcomes. The quantification of the intra-system uncertainty takes into account the variability of the technical performance (crop yield, feed efficiency) and of emission factors (for NH3, N2O and NO3) and the influence of the functional unit (FU) (kg of pig versus hectare used). For farming systems, the inter-system variability is investigated through differentiating the production mode (conventional, quality label, organic (OA)), and the farmer practices (Good Agricultural Practice (GAP) versus Over Fertilised (OF)), while for natural systems, variability due to physical and climatic characteristics of catchments expected to modify nitrate fate is explored. Results and Conclusion For the eutrophication and climate change impact categories, the uncertainty associated with field emissions contributes more to the overall uncertainty than the uncertainty associated with emissions from livestock buildings, with crop yield and with feed efficiency. For acidification, the uncertainty of emissions from livestock buildings is the single most important contributor to the overall uncertainty. The influence of the FU on eutrophication results is very important when comparing systems with different degrees of intensification such as GAP and OA. Concerning the inter-system variability, differences in farmer practices have a larger effect on eutrophication than differences between production modes. Finally, the physical characteristics of the catchment and the climate strongly affect the results for eutrophication. In conclusion, in this case study, the main sources of uncertainty are in the estimation of emission factors, due both to the variability of environmental conditions and to lack of knowledge (emissions of N2O at the field level), but also in the model used for assessing regional impacts such as eutrophication. Recommendation and Perspective Suitable deterministic simulation models integrating the main controlling variables (environmental conditions, farmer practices, technology used) should be used to predict the emissions of a given system as well as their probabilistic distribution allowing the use of stochastic modelling. Finally, our simulations on eutrophication illustrate the necessity of integrating the fate of pollutants in models of impact assessment and highlight the important margin of improvement existing for the eutrophication impact assessment model.     

Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions.     

New coupled model used inversely for reconstructing past terrestrial carbon storage from pollen data: validation of model using modern data

The knowledge of potential impacts of climate change on terrestrial vegetation is crucial to understand long-term global carbon cycle development. Discrepancy in data has long existed between past carbon storage reconstructions since the Last Glacial Maximum by way of pollen, carbon isotopes, and general circulation model (GCM) analysis. This may be due to the fact that these methods do not synthetically take into account significant differences in climate distribution between modern and past conditions, as well as the effects of atmospheric CO₂ concentrations on vegetation. In this study, a new method to estimate past biospheric carbon stocks is reported, utilizing a new integrated ecosystem model (PCM) built on a physiological process vegetation model (BIOME4) coupled with a process-based biospheric carbon model (DEMETER). The PCM was constrained to fit pollen data to obtain realistic estimates. It was estimated that the probability distribution of climatic parameters, as simulated by BIOME4 in an inverse process, was compatible with pollen data while DEMETER successfully simulated carbon storage values with corresponding outputs of BIOME4. The carbon model was validated with present-day observations of vegetation biomes and soil carbon, and the inversion scheme was tested against 1491 surface pollen spectra sample sites procured in Africa and Eurasia. Results show that this method can successfully simulate biomes and related climates at most selected pollen sites, providing a coefficient of determination ( R ) of 0.83–0.97 between the observed and reconstructed climates, while also showing a consensus with an R -value of 0.90–0.96 between the simulated biome average terrestrial carbon variables and the available observations. The results demonstrate the reliability and feasibility of the climate reconstruction method and its potential efficiency in reconstructing past terrestrial carbon storage.     

Impacts of elevated CO2 concentration on the productivity and surface energy budget of the soybean and maize agroecosystem in the Midwest USA

The physiological response of vegetation to increasing atmospheric carbon dioxide concentration ([CO₂]) modifies productivity and surface energy and water fluxes. Quantifying this response is required for assessments of future climate change. Many global climate models account for this response; however, significant uncertainty remains in model simulations of this vegetation response and its impacts. Data from in situ field experiments provide evidence that previous modeling studies may have overestimated the increase in productivity at elevated [CO₂], and the impact on large‐scale water cycling is largely unknown. We parameterized the Agro‐IBIS dynamic global vegetation model with observations from the SoyFACE experiment to simulate the response of soybean and maize to an increase in [CO₂] from 375 ppm to 550 ppm. The two key model parameters that were found to vary with [CO₂] were the maximum carboxylation rate of photosynthesis and specific leaf area. Tests of the model that used SoyFACE parameter values showed a good fit to site‐level data for all variables except latent heat flux over soybean and sensible heat flux over both crops. Simulations driven with historic climate data over the central USA showed that increased [CO₂] resulted in decreased latent heat flux and increased sensible heat flux from both crops when averaged over 30 years. Thirty‐year average soybean yield increased everywhere (ca. 10%); however, there was no increase in maize yield except during dry years. Without accounting for CO₂ effects on the maximum carboxylation rate of photosynthesis and specific leaf area, soybean simulations at 550 ppm overestimated leaf area and yield. Our results highlight important model parameter values that, if not modified in other models, could result in biases when projecting future crop–climate–water relationships.     

Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, T_max, T_min, and T_mean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.     

Impacts of climate change on paddy rice yield in a temperate climate

The crop simulation model is a suitable tool for evaluating the potential impacts of climate change on crop production and on the environment. This study investigates the effects of climate change on paddy rice production in the temperate climate regions under the East Asian monsoon system using the CERES‐Rice 4.0 crop simulation model. This model was first calibrated and validated for crop production under elevated CO₂ and various temperature conditions. Data were obtained from experiments performed using a temperature gradient field chamber (TGFC) with a CO₂ enrichment system installed at Chonnam National University in Gwangju, Korea in 2009 and 2010. Based on the empirical calibration and validation, the model was applied to deliver a simulated forecast of paddy rice production for the region, as well as for the other Japonica rice growing regions in East Asia, projecting for years 2050 and 2100. In these climate change projection simulations in Gwangju, Korea, the yield increases (+12.6 and + 22.0%) due to CO₂ elevation were adjusted according to temperature increases showing variation dependent upon the cultivars, which resulted in significant yield decreases (?22.1% and ?35.0%). The projected yields were determined to increase as latitude increases due to reduced temperature effects, showing the highest increase for any of the study locations (+24%) in Harbin, China. It appears that the potential negative impact on crop production may be mediated by appropriate cultivar selection and cultivation changes such as alteration of the planting date. Results reported in this study using the CERES‐Rice 4.0 model demonstrate the promising potential for its further application in simulating the impacts of climate change on rice production from a local to a regional scale under the monsoon climate system.     

Responses to Doubled CO2 in Medicago sativa: Studies on Ecophysiology and Simulation Modelling

A world-wide spread forage grass, Medicago sativa, was grown in two open-top chambers maintained at either normal (350 μmol · mol-1) or doubled (700 μmol · mol-1) CO2 concentration, from seedling to maturity. During the whole growth season, ecophysiological measurements and observations were conducted over different phenological stages and the main results were as follows: (1) With similar environmental factors, in terms of RH (relative humidity), irradiance, and watering, a slight shift in temperature (about 0.77℃, averaged over the whole growth season) within the chamber maintained at doubled CO2 did not affect instantaneous physiological processes at leaf level, but had some impacts on the effect of doubled CO2 over a long period. (2) Over the whole growth season, physiological variables showed differences between two chambers. The net photosynthesis of plant grown under higher CO2 increased by 18.7 %, while stomatal conductance fell slightly as compared with that of the control. So was the water use efficiency which was also 30.1% higher than the control. Based on the above results from field studies, we concluded that photosynthetically active radiation (PAR) and RH were the main factors affecting photosynthesis and stomatal behavior. Then we combined a widely accepted model of C3 leaf photosynthesis with an empirical model of stomatal conductance and made some modifications according to our experiments. This model was parameterized using field data sets of net CO2 assimilation, stomatal conductance, intercellular CO2 concentration of plants grown at both doubled and control CO2 levels. Variances of main parameters between two treatments reflected some biochemical changes in leaf cell. The maximum efficiency of light energy conversion (α) increased by 22 % and light-saturated rate of electron transport (Jmax) rose by 15 %. The maximum stomatal conductance was slightly reduced by 8 %. The increases in parameters (α and Jmax) indicate accelerated biochemical processes in leaf cell, which means that the photosynthetic capacity of M. sativa may increase at elevated CO2. These results agree well with biochemical measurements at cell level.     

Integrated approaches to climate-crop modelling: needs and challenges

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.     

Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation

The remaining carbon stocks in wet tropical forests are currently at risk because of anthropogenic deforestation, but also because of the possibility of release driven by climate change. To identify the relative roles of CO2 increase, changing temperature and rainfall, and deforestation in the future, and the magnitude of their impact on atmospheric CO2 concentrations, we have applied a dynamic global vegetation model, using multiple scenarios of tropical deforestation (extrapolated from two estimates of current rates) and multiple scenarios of changing climate (derived from four independent offline general circulation model simulations). Results show that deforestation will probably produce large losses of carbon, despite the uncertainty about the deforestation rates. Some climate models produce additional large fluxes due to increased drought stress caused by rising temperature and decreasing rainfall. One climate model, however, produces an additional carbon sink. Taken together, our estimates of additional carbon emissions during the twenty-first century, for all climate and deforestation scenarios, range from 101 to 367 Gt C, resulting in CO2 concentration increases above background values between 29 and 129 p.p.m. An evaluation of the method indicates that better estimates of tropical carbon sources and sinks require improved assessments of current and future deforestation, and more consistent precipitation scenarios from climate models. Notwithstanding the uncertainties, continued tropical deforestation will most certainly play a very large role in the build-up of future greenhouse gas concentrations.     

Multimodel ensembles of wheat growth: many models are better than one

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.     

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.     

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion.