气候变化影响中国粮食安全研究的新思路北大核心CSCD

鉴于气候变化影响粮食安全问题的特殊性和复杂性,本文试图从自然科学和社会科学的交叉研究入手,提出一种新的研究的思路和方法,即:运用计量经济学模型对气候变化数据进行统计分析,使用计量经济学方法来评估气候这一外部驱动因素引发的社会经济系统变化与观测到的气候变化引发的社会经济系统变化之间的关系;在厘清“气候变化影响量”对粮食产量的影响的基础上,预估我国未来30年特别是经济社会发展两个关键节点2035年和2050年的粮食生产的气候变化风险,文章给出了一种新的研究视角,构建了研究内容和研究方法,力争实现定性研究与定量研究相结合,以科学预测为政策指导提供有力支撑。     

The use of general circulation models in climate impact analysis — A preliminary study of the impacts of a CO2- induced climatic change on West European agriculture

An investigation is made of the possible impacts of a climatic change (induced by a doubling of atmospheric carbon dioxide concentration) on the European agricultural sector. Two general circulation models have been used to develop climatic change scenarios for the European study area. From the scenarios, information was obtained concerning the possible behavior of temperature, precipitation, solar radiation, and relative humidity in the altered climatic state. This meteorological information was then employed in two separate crop-weather models - an empirical/statistical model (for winter wheat) and a simple simulation model (for biomass potential). This type of approach represents a considerable departure from that employed by previous large-scale climate impact studies. Both the seasonal and regional components of a possible climatic change are incorporated directly in the two crop-weather models. The results of this investigation demonstrate that a simple crop-weather simulation model may be more suitable for the purposes of agricultural impact analysis than the linear regression models frequently used in such studies. In order for such an impact analysis to be accepted as a valid scientific experiment, a full presentation of the underlying assumptions and uncertainties is essential.     

Sensitivity of agricultural production to climatic change

Although the range of cultivated species is relatively restricted, domestic plants and animals exhibit considerable resilience to stochastic shocks, and the study of their ecological adaptability and critical physiological and phenological requirements is a valuable first step in determining their possible response to climatic change. Methods of assessing agroclimatic suitability and their limitations are discussed, and suggestions are made for simulating the probable impact of shifts in the main climatic parameters on the productivity and spatial distribution of key crops and livestock. Some regions and crops are climatically more vulnerable than others: some regions (in particular North America) are strategically more critical to the stability of world food supplies, while in others resources for agricultural production are under more severe pressure.As well as attempts to forecast long-term climatic trends and their effects on agriculture, combating climatic variability merits high priority. This is an ever-present source of instability in production and could be enhanced in association with changing climate. Its magnitude differs widely among crops and geographical regions, but its impact from year to year is often greater than that predicted from climatic change even in extreme scenarios. The paper indicates a number of potentially desirable areas for action and suggests that several of these would be beneficial both as a buffer against short-term effects of variability and as a means of combating climatic change.     

Climate change and the world predicament: A case study for interdisciplinary research

The relationship between climatic change and issues of population, food, resources, environment and the human condition i.e., the world predicament, are explored. It is concluded that society is dangerously vulnerable to natural climatic variability at times of depleted food reserves (such as now) and that massive use of technologies (especially energy) to improve the human condition could well cause significant climatic change as early as the year 2000. Therefore, these problems cannot be addressed in the sole context of disciplinary research, and the obstacles and opportunities for interdisciplinary research at academic institutions are explored. Criteria for interdisciplinary research quality review are suggested, and contrasted to traditional peer review processes.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

气候变化对越南北方水稻生产的影响

利用越南北方各省19个代表站近50a的气候和水稻资料,采用EOF等数理统计方法,分析了气候变化对越南北方水稻生产的影响。同时讨论了水稻趋势产量和气象产量变化的特征,气温、降水等要素与水稻产量之间的关系以及1959年以来气象灾害对越南北方水稻产量形成的影响,并通过积分回归分析探讨了不同因子在不同时段对水稻生产的作用,进而提出在未来气候变暖背景下越南北方应采取的相应对策。     

An estimate of future climate change for western France using a statistical downscaling technique

A statistical downscaling procedure based on an analogue technique is used to determine projections for future climate change in western France. Three ocean and atmosphere coupled models are used as the starting point of the regionalization technique. Models' climatology and day to day variability are found to reproduce the broad main characteristics seen in the reanalyses. The response of the coupled models to a similar CO₂ increase scenario exhibit marked differences for mean sea-level pressure; precipitable water and temperature show arguably less spread. Using the reanalysis fields as predictors, the statistical model parameters are set for daily extreme temperatures and rain occurrences for seventeen stations in western France. The technique shows some amount of skill for all three predictands and across all seasons but failed to give reliable estimates of rainfall amounts. The quality of both local observations and large-scale predictors has an impact on the statistical model skill. The technique is partially able to reproduce the observed climatic trends and inter annual variability, showing the sensitivity of the analogue approach to changed climatic conditions albeit an incomplete explained variance by the statistical technique. The model is applied to the coupled model control simulations and the gain compared with direct model grid-average outputs is shown to be substantial at station level. The method is then applied to altered climate conditions; the impact of large-scale model uncertain responses and model sensitivities are quantified using the three coupled models. The warming in the downscaled projections are reduced compared with their global model counterparts.     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

Environmental change in grasslands: Assessment using models

Modeling studies and observed data suggest that plant production, species distribution, disturbance regimes, grassland biome boundaries and secondary production (i.e., animal productivity) could be affected by potential changes in climate and by changes in land use practices. There are many studies in which computer models have been used to assess the impact of climate changes on grassland ecosystems. A global assessment of climate change impacts suggest that some grassland ecosystems will have higher plant production (humid temperate grasslands) while the production of extreme continental steppes (e.g., more arid regions of the temperate grasslands of North America and Eurasia) could be reduced substantially. All of the grassland systems studied are projected to lose soil carbon, with the greatest losses in the extreme continental grassland systems. There are large differences in the projected changes in plant production for some regions, while alterations in soil C are relatively similar over a range of climate change projections drawn from various General Circulation Models (GCM's). The potential impact of climatic change on cattle weight gains is unclear. The results of modeling studies also suggest that the direct impact of increased atmospheric CO₂ on photosynthesis and water use in grasslands must be considered since these direct impacts could be as large as those due to climatic changes. In addition to its direct effects on photosynthesis and water use, elevated CO₂ concentrations lower N content and reduce digestibility of the forage.     

Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.     

Assessing Possible Impacts of Climate Change on Species Important for Forestry in Vietnam

The likely effects on two tree species of a range of scenarios of climatic and atmospheric change expected by the year 2050 are investigated using a climatic mapping program, a simple simulation model and a process-based simulation model. Styrax tonkinensis is a native species for which relatively little information is available. Acacia mangium is an introduced species, which is important for pulp production in several other countries, and for which there is considerable information for growth and utilization. A climatic mapping program is used to show areas which may be suitable for these species under present and predicted conditions. Two simulation models are used to investigate likely effects on productivity of the two species for a range of climatic change scenarios for Hanoi and Ho Chi Minh City. The estimated changes in production are predicted to be relatively small, though uncertainities associated with the simulations are quite high. However, the models highlight areas where more data are needed and also suggest some key regions in Vietnam which would be worth monitoring to detect early signs of the effects of climatic and atmospheric change.     

Impact of Climate Change and Variability on Irrigation Requirements: A Global Perspective

Anthropogenic climate change does not only affect water resources but also water demand. Future water and food security will depend, among other factors, on the impact of climate change on water demand for irrigation. Using a recently developed global irrigation model, with a spatial resolution of 0.5° by 0.5°, we present the first global analysis of the impact of climate change and climate variability on irrigation water requirements. We compute how long-term average irrigation requirements might change under the climatic conditions of the 2020s and the 2070s, as provided by two climate models, and relate these changes to the variations in irrigation requirements caused by long-term and interannual climate variability in the 20th century. Two-thirds of the global area equipped for irrigation in 1995 will possibly suffer from increased water requirements, and on up to half of the total area (depending on the measure of variability), the negative impact of climate change is more significant than that of climate variability.     

Impact of Climate Change on Indian Agriculture: A Review

During the recent decade, with the growing recognition of the possibility of climate change and clear evidence of observed changes in climate during 20^th century, an increasing emphasis on food security and its regional impacts has come to forefront of the scientific community. In recent times, the crop simulation models have been used extensively to study the impact of climate change on agricultural production and food security. The output provided by the simulation models can be used to make appropriate crop management decisions and to provide farmers and others with alternative options for their farming system. It is expected that in the coming decades with the increased use of computers, the use of simulation models by farmers and professionals as well as policy and decision makers will increase. In India, substantial work has been done in last decade aimed at understanding the nature and magnitude of change in yield of different crops due to projected climate change. This paper presents an overview of the state of the knowledge of possible effect of the climate variability and change on food grain production in India. An erratum to this article can be found at     

Downscaling GCMs Using the Smooth Support Vector Machine Method to Predict Daily Precipitation in the Hanjiang Basin

General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the sta...     

The adaptation and mitigation potential of traditional agriculture in a changing climate

The threat of global climate change has caused concern among scientists because crop production could be severely affected by changes in key climatic variables that could compromise food security both globally and locally. Although it is true that extreme climatic events can severely impact small farmers, available data is just a gross approximation at understanding the heterogeneity of small scale agriculture ignoring the myriad of strategies that thousands of traditional farmers have used and still use to deal with climatic variability. Scientists have now realized that many small farmers cope with and even prepare for climate change, minimizing crop failure through a series of agroecological practices. Observations of agricultural performance after extreme climatic events in the last two decades have revealed that resiliency to climate disasters is closely linked to the high level of on-farm biodiversity, a typical feature of traditional farming systems.Based on this evidence, various experts have suggested that rescuing traditional management systems combined with the use of agroecologically based management strategies may represent the only viable and robust path to increase the productivity, sustainability and resilience of peasant-based agricultural production under predicted climate scenarios. In this paper we explore a number of ways in which three key traditional agroecological strategies (biodiversification, soil management and water harvesting) can be implemented in the design and management of agroecosystems allowing farmers to adopt a strategy that both increases resilience and provides economic benefits, including mitigation of global warming.     

How will climate change spatially affect agriculture production in Ethiopia? Case studies of important cereal crops

Nearly all of Ethiopia’s agriculture is dependent on rainfall, particularly the amount and seasonal occurrence. Future climate change predictions agree that changes in rainfall, temperature, and seasonality will impact Ethiopia with dramatic consequences. When, where, and how these changes will transpire has not been adequately addressed. The objective of our study was to model how projected climate change scenarios will spatially and temporally impact cereal production, a dietary staple for millions of Ethiopians. We used Maxent software fit with crop data collected from household surveys and bioclimatic variables from the WorldClim database to develop spatially explicit models of crop production in Ethiopia. Our results were extrapolated to three climate change projections (i.e., Canadian Centre for Climate Modeling and Analysis, Hadley Centre Coupled Model v3, and Commonwealth Scientific and Industrial Research Organization), each having two emission scenarios. Model evaluations indicated that our results had strong predictability for all four cereal crops with area under the curve values of 0.79, 0.81, 0.79, and 0.83 for teff, maize, sorghum, and barley, respectively. As expected, bioclimatic variables related to rainfall were the greatest predictors for all four cereal crops. All models showed similar decreasing spatial trends in cereal production. In addition, there were geographic shifts in land suitability which need to be accounted for when assessing overall vulnerability to climate change. The ability to adapt to climate change will be critical for Ethiopia’s agricultural system and food security. Spatially explicit models will be vital for developing early warning systems, adaptive strategies, and policy to minimize the negative impacts of climate change on food production.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment

Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.     

气象条件对作物品质和产量影响的试验研究

利用人工气候室试验研究了高温、高CO2浓度和水分胁迫等气象条件变化对农作物籽粒品质以及粮食安全供给的影响.结果表明:土壤水分胁迫有利于提高农作物籽粒的品质,而大气中CO2浓度升高并伴随高温出现不仅不利于农作物籽粒品质的提高,而且对作物在干旱条件下提高作物籽粒品质的能力有抑制作用,并将在大多数气候变化情景下对中国的粮食安全供给产生不利影响.     

Application of stochastic simulation to climatic-change studies

Stochastic modelling provides a tool for exploring the full implications of the statistical behavior of historical records and can be used to predict the changing probabilities that events of various magnitudes will occur for different climatic change scenarios. Two simulation models are presented, one for daily air temperature, and the other for daily precipitation. The simulation procedures are: (1) extract salient parameter values from historical records; (2) simulate long sequences of data using the stochastic models, with or without a climatic change scenario as provided by a general circulation model; and (3) using the simulated data as inputs, derive the probability distributions of other variables based on known deterministic or probabilistic relationships between the input and the predicted variables.Given a doubling of carbon dioxide concentration in the atmosphere, the climatic models produce varying degrees of temperature and precipitation changes. Examples of application, including the derivation of snowfall and riverice data using simulated temperature and precipitation, illustrate that stochastic modelling offers a suitable approach to quantify the possible hydrologic impacts of climatic change.     

Estimating the consequences of CO2-induced climatic change on north american grain agriculture using general circulation model information

A procedure to estimate the potential climatic effects of a doubling of atmospheric carbon dioxide concentration on agricultural production is illustrated. The method combines use of atmospheric general circulation models (GCMs) and process-oriented crop models. Wheat and corn (maize) yields in three important North American grain cropping regions are treated. Combined use of these two types of models can provide insights into the impacts of climate changes at the level of plant physiology, and potential means by which agricultural production practices may adapt to these changes.Specific agronomic predictions are found to depend critically on the details of the projected climate change. Uncertainties in the specification of the doubled-CO₂ climate by the GCM, particularly with respect to precipitation, dictate that agricultural predictions derived from them at this time must be regarded only as illustrative of the impact assessment method.