EMPIRICAL ANALYSIS OF CLIMATE CHANGE IMPACT ON LOBLOLLY PINE PLANTATIONS IN THE SOUTHERN UNITED STATES

Abstract Few studies have empirically examined climate change impacts on managed forests in the southern United States. In this paper, we use the U.S. Forest Service's Forest Inventory and Analysis Database to fit two growth models across the South and apply the four Hadley III climate scenarios developed for the Intergovernmental Panel on Climate Change Fourth Assessment Report to project future growth and site productivity on loblolly pine plantations. The static growth model provides a direct test of whether a significant climate influence on forest growth can be statistically derived, while the dynamic growth model estimates climate effects through site productivity. Results indicate considerable spatial variation in potential future growth and productivity change on loblolly pine plantations due to climate change in the southern United States, while overall regional effects are projected to be marginal. The pattern of climate change impacts is consistent across the growth models and climate scenarios. These findings have several implications for climate change adaptation policies.     

Detecting change in UK extreme precipitation using results from the climate<Emphasis Type="Italic">prediction</Emphasis>.net BBC climate change experiment

We investigate a question posed by policy makers, namely, “when will changes in extreme precipitation due to climate change be detectable?” To answer this question we use climateprediction.net (CPDN) model simulations from the BBC Climate Change Experiment (CCE) over the UK. These provide us with the unique opportunity to compare 1-day extreme precipitation generated from climate altered by observed forcings (time period 1920–2000) and the SRES A1B emissions scenario (time period 2000–2080) (the Scenario) to extreme precipitation generated by a constant climate for year 1920 (the Control) for the HadCM3L General Circulation Model (GCM). We fit non-stationary Generalized Extreme Value (GEV) models to the Scenario output and compare these to stationary GEV models fit to the parallel Control. We define the time of detectable change as the time at which we would reject a hypothesis at the α = 0.05 significance level that the 20-year return level of the two runs is equal. We find that the time of detectable change depends on the season, with most model runs indicating that change to winter extreme precipitation may be detectable by the year 2010, and that change to summer extreme precipitation is not detectable by 2080. We also investigate which climate model parameters affect the weight of the tail of the precipitation distribution and which affect the time of detectable change for the winter season. We find that two climate model parameters have an important effect on the tail weight, and two others seem to affect the time of detection. Importantly, we find that climate model simulated extreme precipitation has a fundamentally different behavior to observations, perhaps due to the negative estimate of the GEV shape parameter, unlike observations which produce a slightly positive (～0.0–0.2) estimate.     

How Crucial is Cooperation in Mitigating World Climate? Analysis with World-MARKAL

In order to study the conditions for a World self-enforcing agreement on climate change, we model cooperative and non-cooperative World climate strategies with an integrated version of the 15-region techno-economic MARKAL model in which abatement costs and climate related damages are both included. We first explain why the use of a technology oriented model may add value to the analysis of global GHG strategies. Based on the empirical finding of linear cumulative climate damages, the computation of Nash equilibrium can be reduced to solving a series of 15 independent linear programs, one per region. Moreover, assuming interregional transfers to share the global surplus of cooperation, our work adopts the point of view of dynamic partial equilibrium computation coupled with cooperative game-theoretic principles. The results illustrate how the non-cooperative strategy is closer to the base case than to the cooperative strategy, and the amount of side-payments sufficient to guarantee the stability of the cooperative strategy are calculated with four different rules. The internal (in)stability of farsighted coalitions without transfers (non-cooperation) is also analyzed. The current project appears to be the first one of the sort using a large and detailed technology explicit model such as MARKAL.     

Hopf bifurcation in a conceptual climate model with ice–albedo and precipitation–temperature feedbacks

In this paper we analyse a dynamical system based on the so-called KCG (Källén, Crafoord, Ghil) conceptual climate model. This model describes an evolution of the globally averaged temperature and the average extent of the ice sheets. In the nondimensional form the equations can be simplified facilitating the subsequent analysis. We consider the limiting case of a stationary snow line for which the phase plane can be completely analysed and the type of each critical point can be determined. One of them can exhibit the Hopf bifurcation and we find sufficient conditions for its existence. Those, in turn, have a straightforward physical meaning and indicate that the model predicts internal oscillations of the climate. Using the typical real-world values of appearing parameters we conclude that the obtained results are in the same ballpark as the conditions on our planet during the quaternary ice ages. Our analysis is a rigorous justification of a generalization of some previous results by KCG and other authors.     

A Spatial Markov Model for Climate Extremes

Spatial climate data are often presented as summaries of areal regions such as grid cells, either because they are the output of numerical climate models or to facilitate comparison with numerical climate model output. Extreme value analysis can benefit greatly from spatial methods that borrow information across regions. For Gaussian outcomes, a host of methods that respect the areal nature of the data are available, including conditional and simultaneous autoregressive models. However, to our knowledge, there is no such method in the spatial extreme value analysis literature. In this article, we propose a new method for areal extremes that accounts for spatial dependence using latent clustering of neighboring regions. We show that the proposed model has desirable asymptotic dependence properties and leads to relatively simple computation. Applying the proposed method to North American climate data reveals several local and continental-scale changes in the distribution of precipitation and temperature extremes over time. Supplementary material for this article is available online.     

Sources of uncertainty in the extreme value statistics of climate data

We investigate three sources of uncertainty in the calculation of extreme value statistics for observed and modeled climate data. Inter-model differences in formulation, unforced internal variability and choice of statistical model all contribute to uncertainty. Using fits to the GEV distribution to obtain 20 year return values, we quantify these uncertainties for the annual maximum daily mean surface air temperatures of pre-industrial control runs from 15 climate models in the CMIP3 dataset.     

On the Extremum Control Problem with Pointwise Observation for a Parabolic Equation

Doklady Mathematics - In this paper we consider a control problem with pointwise observation for a one-dimensional parabolic equation which arises in a mathematical model of climate control in...     

Sticks and carrots for the design of international climate agreements with renegotiations

This paper examines renegotiations of international climate agreements for carbon abatement. We explore coalition stability under ‘optimal transfers’ that have been suggested to stabilise international environmental agreements (e.g. McGinty in Oxford Economic Papers 59, 45–62, 2007). Such transfer schemes need to be refined when agreements are renegotiated. We determine the requirements that transfers between signatories of an international climate agreement must satisfy in order to stabilise the sequence of agreements that performs best in terms of provision of the public good ‘carbon abatement’. If these requirements are met, no country wants to change its membership status at any stage. In order to demonstrate the applicability of our result we use the STACO model, a 12-regions global model, to assess the impact of well-designed transfer rules on the stability of an international climate agreement. Although there are strong free-rider incentives, we find a stable grand coalition in the first commitment period in a game with one round of renegotiations if renegotations take place sufficiently early.     

Energy and Climate Policy Analysis with the Hybrid Bottom-Up Computable General Equilibrium Model SCREEN: The Case of the Swiss CO2 Act

This paper shows how bottom-up activity analyses within a dynamic computable general equilibrium framework can be undertaken for the longer-term analysis of energy and climate policies using the model SCREEN [25]. In particular we demonstrate for the case of Switzerland how the impact of policy measures to reduce the carbon intensity of the energy sector can be assessed with such a model for various socio-economic and environmental dimensions (e.g., C0₂ emissions, GDP, employment, foreign exchange rate). The results can provide valuable insights for the appropriate design of energy or climate policies that allow for the targeted fostering of a more sustainable energy development.     

Stochastic processes adapted by neural networks with application to climate, energy, and finance

Local climate parameters may naturally effect the price of many commodities and their derivatives. Therefore we propose a joint framework for stochastic modeling of climate and commodity prices. In our setting, a stable Levy process is drift augmented to a generalized SDE. The related nonlinear function on the state space typically exhibits deterministic chaos. Additionally, a neural network adapts the parameters of the stable process such that the latter produces increasingly optimal differences between simulated output and observed data. Thus we propose a novel method of “intelligent” calibration of the stochastic process, using learning neural networks in order to dynamically adapt the parameters of the stochastic model.     

Bayesian policy support for adaptive strategies using computer models for complex physical systems

In this paper, we discuss combining expert knowledge and computer simulators in order to provide decision support for policy makers managing complex physical systems. We allow future states of the complex system to be viewed after initial policy is made, and for those states to influence revision of policy. The potential for future observations and intervention impacts heavily on optimal policy for today and this is handled within our approach. We show how deriving policy dependent system uncertainty using computer models leads to an intractable backwards induction problem for the resulting decision tree. We introduce an algorithm for emulating an upper bound on our expected loss surface for all possible policies and discuss how this might be used in policy support. To illustrate our methodology, we look at choosing an optimal CO₂ abatement strategy, combining an intermediate complexity climate model and an economic utility model with climate data.     

Lyapunov type functions for classes of autonomous parabolic feedback control problems and applications

In this note we provide sufficient conditions for the existence of a Lyapunov function for a class of parabolic feedback control problems. The results are applied to the long-time behavior of state functions for the following problems: (i) a model of combustion in porous media; (ii) a model of conduction of electrical impulses in nerve axons; and (iii) a climate energy balance model.     

THE EFFECT OF CLIMATE CHANGE ON WETLANDS AND WATERFOWL IN WESTERN CANADA: INCORPORATING CROPPING DECISIONS INTO A BIOECONOMIC MODEL

Abstract We extend an earlier bioeconomic model of optimal duck harvest and wetland retention in the Prairie Pothole Region of Western Canada to include cropping decisions. Instead of a single state equation, the model has two state equations representing the population dynamics of ducks and the amount of wetlands. We use the model to estimate the impact of climate change on wetlands and waterfowl, including direct climate effects as well as land use change due to biofuel policies aimed at mitigating climate change. The model predicts that climate change will reduce wetlands by 37–56% from historic levels. Land use change due to biofuel policies is expected to reduce wetlands by between 35% and 45% from historic levels, whereas direct climate effects will range from a reduction of 2–11%, depending on the future climate scenario. This result indicates that models that neglect the effect of land use changes underestimate the effect of climate change on wetlands. Further, wetlands loss is geographically heterogeneous, with losses being the largest in Saskatchewan.     

气候对安徽中药材产业经济影响分析——基于经济气候模型

通过考虑气候和经济社会因素,将气候因素与函数综合考虑并进行分析,构建“经济气候”模型,分析并得出气候因素对安徽省中药材产业影响程度,利用所构建BP神经网络模型,分析数据,对气象因素对中药材产业的影响结果进行预测。通过分析安徽省气候变化的特点与方向,确定气候因素对中药材产业的影响程度,为中药材产业发展提供一定的数据分析支撑,为有针对性的管理中药材产业提供方法与数据基础,同时对降低严重气象灾害对中药材生产带来的负面作用具有一定的理论与实践意义。     

A class of large solution of 2D tropical climate model without thermal diffusion

In this paper, we consider the Cauchy problem of 2D tropical climate model without thermal diffusion and construct global smooth solutions by choosing a class of special initial data whose L^∞ norm can be arbitrarily large.     

Approximate inertial manifolds for the 2d model of atmosphere

In order to understand the mechanism of long term weather prediction and climate, we construct explicitly in this paper an infinite number of approximate inertial manifolds M _j,m for the 2D model of large scale atmosphere to approximate the global attractor of the model. Associated with each manifold, there exists a thin neighborhood of it, into which the orbits of the model enter with a finite time. These neighborhoods contain and localize the global attractor. The thickness of these neighborhoods decreases with increasing m for a fixed j. Moreover we also obtain the time analyticity of the solutions of the model and the behavior of the small structures.     

淡水湖泊藻类模型分析

利用非经典生物操纵法建立了鱼直接捕食藻类的模型.气候变化导致的温室效应越来越突出,鱼及藻类的生长对磷的消耗率等因素都与湖水温度有关,模型中考虑了温度的影响.分析了边界平衡点、正平衡点的存在性以及稳定性.得到了藻类和鱼灭绝的阈值,讨论了生态学意义.     

THE VALUE OF SHORT‐RUN CLIMATE FORECASTS IN MANAGING THE COASTAL COHO SALMON (ONCORHYNCHUS KISUTCH) FISHERY IN WASHINGTON STATE

ABSTRACT. . In recent years our understanding of the intricate connections between climate variability, marine and freshwater environmental conditions and the responses of fish stocks has improved considerably. With predictable relationships between the environment and stock abundance, fishery managers should be able to forecast variation in stock survival and recruitment. Such forecasts present an opportunity for increasing the economic value of fisheries and for achieving other management objectives, such as stock conservation and maintenance of population diversity. After describing a 4‐step framework for addressing the question ‘What is a forecast worth?’ in a fishery decision‐making context, we introduce the management system for Washington's coastal coho salmon (Oncorhynchus kisutch) fishery. Then we apply the 4‐step framework to estimate the value of improved run size forecasts in the annual harvest management of coho salmon in Washington State. Our principal analytical tool is a stochastic simulation model that incorporates the main characteristics of the fishery. The paper concludes with a discussion of opportunities and constraints to the use of climate‐based forecasts in fishery management on various spatial and temporal scales, and we consider the challenges associated with forecasting variations in fish stock size caused by shifts in climate and related ocean conditions.     

纳入政策预期的国际气候博弈

本研究首先对Baumol和Oates构建的公共外部性模型的假设条件进行修正,从而构建起更符合实际的国际气候治理的数理模型;求解该数理模型,本研究推导出同时实现全球帕累托最优和自身财政收支平衡下,国际环境协议必须遵循的唯一政策规则;最后,以此为基础,本研究进一步构建起纳入政策预期的国际气候博弈模型,并通过数理分析论证,揭示了:如果世界各国都只考虑自身利益最大化,纳入政策预期下的气候博弈的均衡结果,将无法实现全球气候治理的帕累托最优。     

海-气耦合气候系统非线性扰动模式的周期正解

与ENSO相关的热带大尺度海-气相互作用是影响全球气候年际变化的主要过程之一.该文从一个海-气相互作用方程组出发,推广了一个具有一般形式的海 气耦合气候系统非线性扰动模式.运用拓扑度理论,从数学上严格证明了一定条件下该模式存在周期正解的结果,并分析了所得结果潜在的应用价值.海-气相互作用研究,有助于理解气候变化过程,为气候模拟和预报提供理论基础.