A new economic assessment index for the impact of climate change on grain yield

The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional ＂yield impact of meteorological factor （YIMF）＂ or ＂yield impact of weather factor＂ to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change （YICC）, which is defined as the difference in the per unit area yields （PUAY） of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook.     

Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation

Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor rem...     

Some phenomena of the interaction between vegetation and a atmosphere on multiple scales

This article studies the response of the distribution pattern and the physiological characteristics of the ecosystem to the spontaneous precipitation and the interaction between vegetation and the atmosphere on multiple scales in arid and semi-arid zones, based on measured data of the ecological physiological parameters in the Ordas Plateau of northern China. The results show that the vegetation biomass and the energy use efficiency of photosynthesis are especially sensitive to the annual precipitation; strong and complex interactions exist between the vegetation and the atmosphere on multiple scales leading to supernormal thermal heterogeneity of the underlying surface, the strong vortex movement and turbulence. This study can facilitate understanding of the land surface processes and the influences of global climate change as well as human activities on the human environment in the arid and semi-arid zones. It also aids in improving the parameterization schemes of turbulent fluxes of a heterogeneous underlying surface for land surface processes in climate models.     

气候变化国家评估报告(Ⅱ):气候变化的影响与适应

Significant and various impacts of climate change have been observed in China, showing both positive and adverse effects, dominantly the latter, in different sectors and regions. It is very likely that future climate change would cause significant adverse impacts on the ecosystems, agriculture, water resources, and coastal zones in China. Adoption of adaptive measures to climate change can alleviate the adverse impact, therefore such measures should be incorporated into the medium-and long-term national economic and social development plans. Because China has done relatively limited research on impact assessment and our understanding of climate change is incomplete, the current impact assessment methodologies used and results obtained contain many uncertainties. To reduce the uncertainties and develop effective and practical climate change adaptive measures in China, it is necessary to emphasize regional case studies on adaptive measures, enlarge the scope of climate change research, and strengthen the assessment of the impacts resulted from extreme weather/climate events.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Assessing MODIS Land Cover Products over China with Probability of Interannual Change

Accurate and up-to-date land cover data are important for climate-change modeling. Quality assessment is becoming critical, as many satellite-based land cover products of differing scales have been released to meet the needs of scientific studies. In this study, the authors assessed the Moderate Resolution Imaging Spectroradiometer（MODIS） land cover products by analyzing the probability of interannual change from 2001 to 2012. The authors found that, cumulatively, 43.0% of MODIS land cover had changed over China from 2001 to 2012 at least once. Of this percentage, 12.1% was considered unreasonable change, 6.1% was considered reasonable change, and areas of confusion accounted for about 24.8%, giving rise to great uncertainty in the products. MODIS Collection 51 products clearly have less uncertainty than the Collection 5 products. Areas of reasonable change occurred in transition zones of ecological, biophysical, and climate gradients, while areas of unreasonable change appeared in heterogeneous landscapes. The misclassifications at three spatial scales of horizontal grids used in regional climate models occurred largely in the heterogeneous landscapes, and the areal percentage of misclassification decreased with larger horizontal grid spacing. In addition, the misclassifications in MODIS products often occurred among specific classes, which are geographically, ecologically, and spectrally similar, with low discriminative spectral-temporal signals. The effect of classification uncertainty should be made known, and further improvements are still needed for application in regional climate models. The authors＇ findings have important implications for better understanding the uncertainties of MODIS land cover products, and for improving the land surface parameterization for regional climate models.     

气候变化国家评估报告(Ⅰ):中国气候变化的历史和未来趋势

The climate change in China shows a considerable similarity to the global change, though there still exist some significant differences between them. In the context of the global warming, the annual mean surface air temperature in the country as a whole has significantly increased for the past 50 years and 100 years, with the range of temperature increase slightly greater than that in the globe. The change in precipitation trends for the last 50 and 100 years was not significant, but since 1956 it has assumed a weak increasing trend. The frequency and intensity of main extreme weather and climate events have also undergone a significant change. The researches show that the atmospheric CO2 concentration in China has continuously increased and the sum of positive radiative forcings produced by greenhouse gases is probably responsible for the country-wide climate warming for the past 100 years, especially for the past 50 years. The projections of climate change for the 21st century using global and regional climate models indicate that, in the future 20-100 years, the surface air temperature will continue to increase and the annual precipitation also has an increasing trend for most parts of the country.     

The Relative Impact of Regional Scale Land Cover Change and Increasing CO2 over China

A series of 17-yr equilibrium simulations using the NCAR CCM3 (T42 resolution) were performed to investigate the regional scale impacts of land cover change and increasing CO2 over China. Simulations with natural and current land cover at CO2 levels of 280,355, 430, and 505 ppmv were conducted. Results show statistically significant changes in major climate fields (e.g. temperature and surface wind speed) on a 15-yr average following land cover change. We also found increases in the maximum temperature and in the diurnal temperature range due to land cover change. Increases in CO2 affect both the maximum and minimum temperature so that changes in the diurnal range are small. Both land cover change and CO2 change also impact the frequency distribution of precipitation with increasing CO2 tending to lead to more intense precipitation and land cover change leading to less intense precipitation-indeed, the impact of land cover change typically had the opposite effect versus the impacts of CO2. Our results provide support for the inclusion of future land cover change scenarios in long-term transitory climate inodelling experiments of the 21st Century. Our results also support the inclusion of land surface models that can represent future land cover changes resulting from an ecological response to natural climate variability or increasing CO2. Overall, we show that land cover change can have a significant impact on the regional scale climate of China, and that regionally, this impact is of a similar magnitude to increases in CO2 of up to about 430 ppmv. This means that that the impact of land cover change must be accounted for in detection and attribution studies over China.     

Detection, Causes and Projection of Climate Change over China： An Overview of Recent Progress

This article summarizes the main results and findings of studies conducted by Chinese scientists in the past five years.It is shown that observed climate change in China bears a strong similarity with the global average.The country-averaged annual mean surface air temperature has increased by 1.1℃over the past 50 years and 0.5-0.8℃over the past 100 years,slightly higher than the global temperature increase for the same periods.Northern China and winter have experienced the greatest increases in surface air temperature.Although no significant trend has been found in country-averaged annual precipitation, interdecadal variability and obvious trends on regional scales are detectable,with northwestern China and the mid and lower Yangtze River basin having undergone an obvious increase,and North China a severe drought.Some analyses show that frequency and magnitude of extreme weather and climate events have also undergone significant changes in the past 50 years or so. Studies of the causes of regional climate change through the use of climate models and consideration of various forcings,show that the warming of the last 50 years could possibly be attributed to an increased atmospheric concentration of greenhouse gases,while the temperature change of the first half of the 20th century may be due to solar activity,volcanic eruptions and sea surface temperature change.A significant decline in sunshine duration and solar radiation at the surface in eastern China has been attributed to the increased emission of pollutants. Projections of future climate by models of the NCC(National Climate Center,China Meteorological Administration)and the IAP(Institute of Atmospheric Physics,Chinese Academy of Sciences),as well as 40 models developed overseas,indicate a potential significant warming in China in the 21st century,with the largest warming set to occur in winter months and in northern China.Under varied emission scenarios,the country-averaged annual mean temperature is projected to increase by 1.5-2.1℃by 2020,2.3-3.3℃by 2050, and by 3.9-6.0℃by 2100,in comparison to the 30-year average of 1961 1990.Most models project a 10% 12% increase in annual precipitation in China by 2100,with the trend being particularly evident in Northeast and Northwest China,but with parts of central China probably undergoing a drying trend.Large uncertainty exists in the projection of precipitation,and further studies are needed.Furthermore,anthropogenic climate change will probably lead to a weaker winter monsoon and a stronger summer monsoon in eastern Asia.     

AN IMPROVED LAND-SURFACE PROCESS MODEL AND ITS SIMULATION EXPERIMENT——PART I:LAND-SURFACE PROCESS MODEL AND ITS“OFF-LINE”TESTS AND PERFORMANCE ANALYSES

Based on the existing land-surface schemes and models,an improved Land-surface ProcessModel(LPM-ZD)has been developed.It has the following major characteristics:(1)Thecombination of physical equations and empirical analytical formulae are used to construct thegoverning equations of soil temperature and moisture.Higher resolution of model level andphysical equations are adopted for the upper soil layers,and for the lower soil layers,lowerresolution of model level is adopted and empirical analytical formulae are used.(2)In land surfacehydrological process,the sub-grid distribution of rainfall and its effects are taken into account.(3)A simple snow cover submodel has been used,which includes effects of snow cover on soilthermodynamics and hydrology,as well as albedo.By use of this model and three groups of point observation data,a series of“off-line”testshave been carried out.The simulation results indicate that land-surface process model has goodperformance and can well simulate diurnal and seasonal variation of land surface processes for manykinds of land surface covers(forest,grass,crops and desert)in different climate zone.The resultssimulated by the model are consistent with the observations.Later,by use of one group ofobservation data and the model,a series of sensitivity experiments have been done.It is shownthat the model is much sensitive to some parameters,such as initial soil moisture,vegetationphysical parameters as well as the proportion of the grid covered with rain.Therefore it is muchimportant for land-surface process model to define these parameters as accurately as possible.     

GCM STUDIES ON ANTHROPOGENIC CLIMATE CHANGE IN CHINA

Impacts of human activities on climate change as simulated by the general circulation models(GCMs)in China for the recent ten years have been summarized and reviewed in this paper.Theresearches show that it might be getting warmer over China due to the greenhouse effects.Theatmospheric circulation and precipitation also might be changed due to the greenhouse effects.Theassessments and evaluations of the models over the globe and China have also been presented in thispaper.     

An Overview of BCC Climate System Model Development and Application for Climate Change Studies

This paper reviews recent progress in the development of the Beijing Climate Center Climate System Model(BCC-CSM) and its four component models(atmosphere,land surface,ocean,and sea ice).Two recent versions are described:BCC-CSM1.1 with coarse resolution(approximately 2.8125°×2.8125°) and BCC-CSM1.1(m) with moderate resolution(approximately 1.125°×1.125°).Both versions are fully coupled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation.Both models well simulate the concentration and temporal evolution of atmospheric CO_2 during the 20th century with anthropogenic CO2 emissions prescribed.Simulations using these two versions of the BCC-CSM model have been contributed to the Coupled Model Intercomparison Project phase five(CMIP5) in support of the Intergovernmental Panel on Climate Change(IPCC) Fifth Assessment Report(AR5).These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections.Simulations of the 20th century climate using BCC-CSMl.l and BCC-CSMl.l(m) are presented and validated,with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales.Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed.Both BCC-CSMl.l and BCC-CSMl.l(m) perform well when compared with other CMIP5 models.Preliminary analyses indicate that the higher resolution in BCC-CSM1.1(m) improves the simulation of mean climate relative to BCC-CSMl.l,particularly on regional scales.     

Interannual and Decadal Changes in Tropospheric Ozone in China and the Associated Chemistry–Climate Interactions: A Review

China has been experiencing widespread air pollution due to rapid industrialization and urbanization in recent decades.The two major concerns of ambient air quality in China are particulate matter(PM) and tropospheric ozone(O_3). With the implementation of air pollution prevention and control actions in the last five years, the PM pollution in China has been substantially reduced. In contrast, under the conditions of the urban air pollution complex, the elevated O_3 levels in city clusters of eastern China, especially in warm seasons, have drawn increasing attention. Emissions of air pollutants and their precursors not only contribute to regional air quality, but also alter climate. Climate change in turn can change chemical processes, long-range transport, and local meteorology that influence air pollution. Compared to PM, less is known about O_3 pollution and its climate effects over China. Here, we present a review of the main findings from the literature over the period 2011–18 with regard to the characteristics of O_3 concentrations in China and the mechanisms that drive its interannual to decadal variations, aiming to identify robust conclusions that may guide decision-making for emissions control and to highlight critical knowledge gaps. We also review regional and global modeling studies that have investigated the impacts of tropospheric O_3 on climate, as well as the projections of future tropospheric O_3 owing to climate and/or emission changes.     

Terrestrial Near-Surface Wind Speed Variations in China: Research Progress and Prospects

Changes in terrestrial near-surface wind speed(NSWS) are indicative of the concentrated net effect of climate change and anthropogenic activities. Investigating change mechanisms of NSWS not only furthers the understanding of how the atmosphere changes and improves the climate analysis and projection, but also aids the evaluation and application of wind energy resources. Recent advances in studies of the changes and associated mechanisms of the NSWS over China are reviewed in this paper. Some new results have been achieved in understanding the behaviors of the NSWS changes. The NSWS over China has experienced a decrease in the past 40 years and a recovery in the recent decade, exhibiting large regional and seasonal differences. Understanding of the mechanisms of the NSWS changes has been improved in several aspects; for example, it is found that the reduced NSWS over China is due to the weakening of the pressure-gradient force(PGF) attributed to variations in large-scale ocean–atmosphere circulations(LOACs) as well as the increase of surface roughness due to the land use and cover change(LUCC). The main methods used to analyze the NSWS changes and corresponding mechanisms are also elucidated and discussed.However, studies are still lacking on the mechanisms for multi-timescale(seasonal, interannual, decadal, multidecadal) variations in the NSWS over China, and it remains unknown about the contributions of different forcing factors to the NSWS changes. Finally, key scientific issues regarding our understanding of the NSWS changes are proposed for future investigation.     

Evaluation of Agricultural Climatic Resource Utilization During Spring Maize Cultivation in Northeast China Under Climate Change

Agricultural climatic resources （such as light,temperature,and water） are environmental factors that affect crop productivity.Predicting the effects of climate change on agricultural climatic resource utilization can provide a theoretical basis for adapting agricultural practices and distributions of agricultural production.This study investigates these effects under the IPCC （Intergovernmental Panel on Climate Change） scenario A1B using daily data from the high-resolution RegCM3 （0.25° ×0.25°） during 1951-2100.Model outputs are adjusted using corrections derived from daily observational data taken at 101 meteorological stations in Northeast China between 1971 and 2000.Agricultural climatic suitability theory is used to assess demand for agricultural climatic resources in Northeast China during the cultivation of spring maize.Three indices,i.e.,an average resource suitability index （Isr）,an average efficacy suitability index （Ise）,and an average resource utilization index （K）,are defined to quantitatively evaluate the effects of climate change on climatic resource utilization between 1951 and 2100.These indices change significantly in both temporal and spatial dimensions in Northeast China under global warming.All three indices are projected to decrease in Liaoning Province from 1951 to 2100,with particularly sharp declines in Isr,Ise,and K after 2030,2021,and 2011,respectively.In Jilin and Heilongjiang provinces,Isr is projected to increase slightly after 2011,while Ise increases slightly and K decreases slightly after 2030.The spatial maxima of all three indices are projected to shift northeastward.Overall,warming of the climate in Northeast China is expected to negatively impact spring maize production,especially in Liaoning Province.Spring maize cultivation will likely need to shift northward and expand eastward to make efficient use of future agricultural climatic resources.     

A coupled model study on the intensification of the Asian summer monsoon in IPCC SRES Scenarios

The Asian summer monsoon is an important part of the climate system. Investigating the response of the Asian summer monsoon to changing concentrations of greenhouse gases and aerosols will be meaningful to understand and predict climate variability and climate change not only in Asia but also globally. In order to diagnose the impacts of future anthropogenic emissions on monsoon climates, a coupled general circulation model of the atmosphere and the ocean has been used at the Max-Planck-Institute for Meteorology. In addition to carbon dioxide, the major well mixed greenhouse gases such as methane, nitrous oxide, several chlorofluorocarbons, and CFC substitute gases are prescribed as a function of time. The sulfur cycle is simulated interactively, and both the direct aerosol effect and the indirect cloud albedo effect are considered. Furthermore, changes in tropospheric ozone have been pre-calculated with a chemical transport model and prescribed as a function of time and space in the climate simulations. Concentrations of greenhouse gases and anthropogenic emissions of sulfur dioxide are prescribed according to observations （1860-1990） and projected into the future （1990-2100） according to the Scenarios A2 and B2 in Special Report on Emissions Scenarios （SRES, Nakcenovic et al., 2000） developed by the Intergovernmental Panel on Climate Change （IPCC）. It is found that the Indian summer monsoon is enhanced in the scenarios in terms of both mean precipitation and interannual variability. An increase in precipitation is simulated for northern China but a decrease for the southern part. Furthermore, the simulated future increase in monsoon variability seems to be linked to enhanced ENSO variability towards the end of the scenario integrations.     

Potential impact of future climate change on crop yield in northeastern China

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China(NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5(CMIP5) under the Representative Concentration Pathway 4.5 scenario(RCP4.5), the projected maize yield changes for three future periods [2010–39(period 1), 2040–69(period 2), and 2070–99(period 3)] relative to the mean yield in the baseline period(1976–2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase(but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers.     

Detecting inhomogeneity in daily climate series using wavelet analysis

A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well- established long-term daily temperature series back to the 18th century, which have been ＂homogenized＂ with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.     

The Soil Moisture and Net Primary Production Affected by CO_2 and Climate Change Using a Coupled Model

In this paper, a coupled model was used to estimate the responses of soil moisture and net primary production of vegetation （NPP） to increasing atmospheric CO2 concentration and climate change. The analysis uses three experiments simulated by the second-generation Earth System Model （CanESM2） of the Canadian Centre for Climate Modelling and Analysis （CCCma）, which are part of the phase 5 of the Coupled Model Intercomparison Project （CMIP5）. The authors focus on the magnitude and evolution of responses in soil moisture and NPP using simulations modeled by CanESM, in which the individual effects of increasing CO2 concentration and climate change and their combined effect are separately accounted for. When considering only the single effect of climate change, the soil moisture and NPP have a linear trend of 0.03 kg m^-2 yr^-1 and-0.14 gC m^- 2 yr^-2, respec- tively. However, such a reduction in the global NPP results from the decrease of NPP at lower latitudes and in the Southern Hemisphere, although increased NPP has been shown in high northern latitudes. The largest negative trend is located in the Amazon basin at -1.79 gC m^-2 yr^-2. For the individual effect of increasing CO2 concentration, both soil moisture and NPP show increases, with an elevated linear trend of 0.02 kg m^-2 yr^-1 and 0.84 gC m^-2 yr^-2, respectively. Most regions show an increasing NPP, except Alaska. For the combined effect of increasing atmospheric CO2 and climate change, the increased soil moisture and NPP exhibit a linear trend of 0.04 kg m^2 yr^-1 and 0.83 gC m^2 yr^-2 at a global scale. In the Amazon basin, the higher reduction in soil moisture is illustrated by the model, with a linear trend of-0.39 kg m^-2 yr^-1, for the combined effect. Such a change in soil moisture is caused by a weakened Walker circulation simulated by this coupled model, compared with the single effect of increasing CO2 concentration （experiment M2）, and a consequence of the reduction in NPP is also shown in this area, with a linear trend of-     

中国气温的降尺度研究

Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.