福建山区耕地主要熟制的气候评估模型

福建山区耕地以水稻为主茬作物的熟制主要有双三熟制和单二熟制。该研究建立了这些熟制的3个气候评估模型,即①生长季适应性评估。建立了主要作物生育期钟模型模式,反映作物生长发育随气候的动态变化,并将生育期与气候生长季进行对比分析,实现生长季适应性评估;②产量表现评估。建立主要作物产量-气候经验模式,评估作物在各地的产量表现;③其它约束条件的评估。是对上述两条评估未考虑到的其它必要气候条件的评估。主要熟制气候评估模型的建立,为传统的定性评估向定量评估的转变提供了一条有效的途径。

中国亚热带地区柑桔气候风险评估

综合考虑柑桔气候适宜性水平及其变率变化,构建柑桔的风险度模型,运用滑动建模技术对中国亚热带地区柑桔的气候风险性进行动态分析与评估。根据风险分布强度将中国亚热带地区柑桔温度、降水、日照和气候风险大致分为三类型:低风险型、中风险型和高风险型。温度风险度大致呈纬度地带性分布,除西部高山区外,由低纬向高纬风险度依次增高;降水风险度呈现亚热带中部低,北部和西部高;与降水风险度相反,日照风险度在亚热带中部高,北部和西部低;气候风险度受温度变化的主导,也大致呈现纬度地带性,呈现高纬和西部高海拔区高,低纬和东部沿海区低。柑桔气候风险在时间和空间变化上都存在着差异,近46年来,中国亚热带地区柑桔气候风险度有逐渐增加的趋势,尤其以20世纪80年代初以来增加的最快;由于全球气候变暖的影响,亚热带东部和南部风险较低的区域分布有逐渐减少的趋势,而北部和西部风险高的区域分布有进一步增大并向东部和南部扩展的可能。从中国亚热带地区柑桔减产率大于10%、20%、30%的气候风险度分布区域变动过程来看,柑桔各减产率的气候风险度分布具有很明显的区域性和连续性,大体上由东南向西北呈增高趋势。

SIMULATION OF PRESENT CLIMATE OVER EAST ASIA BY A REGIONAL CLIMATE MODEL

A 15-year simulation of climate over East Asia is conducted with the latest version of a regional climate model RegCM3 nested in one-way mode to the ERA40 Re-analysis data. The performance of the model in simulating present climate over East Asia and China is investigated. Results show that RegCM3 can reproduce well the atmospheric circulation over East Asia. The simulation of the main distribution patterns of surface air temperature and precipitation over China and their seasonal cycle/evolution, are basically agree with that of the observation. Meanwhile a general cold bias is found in the simulation. As for the precipitation, the model tends to overestimate the precipitation in northern China while underestimate it in southern China, particularly in winter. In general, the model has better performance in simulating temperature than precipitation.

Evaluation of a regional climate model for atmospheric simulation over Arctic river basins

Evaluation on a regional climate model was made with five-month atmospheric simulations over the Arctic river basins. The simulations were performed with a modified mesoscale model, Polar MM5 coupled to the NCAR Land Surface Model （LSM） to illustrate the skill of the coupled model （Polar MM5＋LSM） in simulating atmospheric circulation over the Arctic river basins. Near-surface and upper-air observations were used to verify the simulations. Sensitivity studies between the Polar MM5 and Polar MM5＋LSM simulations revealed that the coupled model could improve the forecast skill for surface variables at some sites. In addition, the extended evaluations of the coupled model simulations on the North American Arctic domain during December 15, 2002 to May 15, 2003 were carried out. The time series plots and statistics of the observations and Polar MM5＋LSM simulations at six stations for near-surface and vertical profiles at 850 hPa and 500 hPa were analyzed. The model was found capable of reproducing the observed atmospheric behavior in both magnitude and variability, especially for temperature and near-surface wind direction.

An Alternative Means of Assessing Climate Models

The authors compared two different sets of assessment of the abilities of contemporary climate models. One group is made of experts, and their results are provided in two expert reports, while the other is the subjective assessment made by ＂physical climate scientists＂ in general, sampled in a series of three survey questionnaires. The expert group is considerably more optimistic than the general group; the former suggesting progress, while the perception of the latter group is more or less stationary.

Climate Precipitation Prediction by Neural Network

In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.

Minimum Regret Climate Policy with Act-Then-Learn Decision-A New Model Framework under Long-Term Uncertainties

The technology investment strategy under uncertainty is the key subject. However, the expected utility maximization often employed as the decision process fails to consider the high risk with low probability cases. On the other hand, the existing min-max regret strategy tends to be dominated by the ＂worst assumption＂ regardless of its probability. This research proposes a new framework by formulating the regret by the Minkowski＇s generalized distance. The authors then apply the formulation to the IAM （integrated assessment model） MARIA. This study focuses on the uncertainties of CCS （carbon capture and storage） costs and the global warming damages. This formulation is then extended to the multi-stage decision frame, known as ATL （act-then-learn） method. The simulation results suggest that the substantial changes in CCS and nuclear deployment strategies depending on the future uncertainty scenarios. The results also suggest that the minimum regret strategy favors the capital accumulation in the early stage.

CIECIA:A new climate change integrated assessment model and its assessments of global carbon abatement schemes

From the perspective of global economic general equilibrium, this study developed a new climate change IAM named CIECIA. The economic core of this IAM is a multi-country-sector general equilibrium model. The endogenous technology progress mode is introduced into CIECIA. Based on this model, three assessment principles of the global cooperating abatement scheme are proposed, including effectiveness, feasibility, and fairness. This study simulated and analyzed six types of primary global cooperating abatement schemes. The simulated results indicate that all of the selected schemes can satisfy the climate mitigation targets by 2100. Thus, they are all effective schemes. However, the schemes have quite different feasibilities and fairness. The Stern Scheme benefits the developed countries, but is unfair to the developing countries. The Nordhaus Scheme promotes the developments of the developing countries. However, it leads to negative impacts on the interests of the developed countries. The principle of convergence on accumulated carbon emissions per capita and the principle of convergence on carbon emissions per capita benefit the economic developments of the middle and low developing countries most. However, these two types of schemes cause tremendous losses to the main economic entities in the world including China. The Pareto Improvement Scheme, which was developed from the Global Economic Growth Scheme, balances the fairness and feasibility in the carbon abatement process and realizes the Pareto improvement of accumulated utilities in all the participating countries. Thus, the Pareto Improvement Scheme is the most reasonable global cooperating carbon abatement scheme.