Generalization of a statistical downscaling model to provide local climate change projections for Australia

Climate change information required for impact studies is of a much finer spatial scale than climate models can directly provide. Statistical downscaling models (SDMs) are commonly used to fill this scale gap. SDMs are based on the view that the regional climate is conditioned by two factors: (1) the large-scale climatic state and (2) local physiographic features. An SDM based on an analogue approach has been developed within the Australian Bureau of Meteorology and applied to six regions covering the southern half of Australia. Six surface predictands (daily minimum and maximum temperature and dew-point temperature, daily total rainfall and pan evaporation) were modelled. The skill of the SDMs is evaluated by comparing reconstructed and observed series using a range of metrics: first two moments of the series, the ability to reproduce day-to-day and inter-annual variability, and long-term trends. Once optimised, the SDMs are applied to a selection of global climate models which contributed to the Intergovernmental Panel on Climate Change 4th assessment report released in 2007. A user-friendly graphical interface has been developed to facilitate dissemination of the SDM results and provides a range of options for users to obtain tailored information. Once the projections are calculated for the places of interest, graphical outputs are displayed and can be downloaded jointly with the underlying data, allowing the user to use the data in their own application.     

Models of daily rainfall cross-correlation for the United Kingdom

Automated easy-to-use tools capable of generating spatial-temporal weather scenarios for the present day or downscaled future climate projections are highly desirable. Such tools would greatly support the analysis of hazard, risk and reliability of systems such as urban infrastructure, river catchments and water resources. However, the automatic parameterization of such models to the properties of a selected scenario requires the characterization of both point and spatial statistics. Whilst point statistics, such as the mean daily rainfall, may be described by a map, spatial properties such as cross-correlation vary according to a pair of sample points, and should ideally be available for every possible pair of locations. For such properties simple automatic representations are needed for any pair of locations.To address this need simple empirical models are developed of the lag-zero cross-correlation-distance (XCD) properties of United Kingdom daily rainfall. Following error and consistency checking, daily rainfall timeseries for the period 1961–1990 from 143 raingauges are used to calculate observed XCD properties. A three parameter double exponential expression is then fitted to appropriate data partitions assuming isotropic and piecewise-homogeneous XCD properties. Three models are developed: 1) a national aseasonal model; 2) a national model partitioned by calendar month; and 3) a regional model partitioned by nine UK climatic regions and by calendar month. These models provide estimates of lag-zero cross-correlation properties of any two locations in the UK.These cross-correlation models can facilitate the development of automated spatial rainfall modelling tools. This is demonstrated through implementation of the regional model into a spatial modelling framework and by application to two simulation domains (both ∼10,000 km²), one in north-west England and one in south-east England. The required point statistics are generally well simulated and a good match is found between simulated and observed XCD properties.The models developed here are straightforward to implement, incorporate correction of data errors, are pre-calculated for computational efficiency, provide smoothing of sample variability arising from sporadic coverage of observations and are repeatable. They may be used to parameterise spatial rainfall models in the UK and the methodology is likely to be easily adaptable to other regions of the world.     

From qualitative to quantitative environmental scenarios: Translating storylines into biophysical modeling inputs at the watershed scale

Scenarios are increasingly used for envisioning future social-ecological changes and consequences for human well-being. One approach integrates qualitative storylines and biophysical models to explore potential futures quantitatively and maximize public engagement. However, this integration process is challenging and sometimes oversimplified. Using the Yahara Watershed (Wisconsin, USA) as a case study, we present a transparent and reproducible roadmap to develop spatiotemporally explicit biophysical inputs [climate, land use/cover (LULC), and nutrients] that are consistent with scenario narratives and can be linked to a process-based biophysical modeling suite to simulate long-term dynamics of a watershed and a range of ecosystem services. Our transferrable approach produces daily weather inputs by combining climate model projections and a stochastic weather generator, annual narrative-based watershed-scale LULC distributed spatially using transition rules, and annual manure and fertilizer (nitrogen and phosphorus) inputs based on current farm and livestock data that are consistent with each scenario narrative.     

基于决策树模型的驾驶员期望车速

微观交通流仿真是智能运输系统(ITS)研究与开发的重要手段.期望车速是微观交通流仿真研究的一个重要参数,受到驾驶员特性、车辆特性、道路条件、交通干扰、天气和承运任务急缓等多种因素的影响,准确地确定期望车速是驾驶员行为研究的难点.从研究驾驶员心理-物理特性的角度出发,利用决策树能融知识表示与获取于一身的优点,将决策树用于驾驶员期望车速的研究,以实现对驾驶员行为的模拟再现.仿真结果表明,该方法用于驾驶员期望车速的研究是可行的.     

A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study

Downscaling techniques are used to obtain high-resolution climate projections for assessing the impacts of climate change at a regional scale. This study presents a statistical downscaling tool, SCADS, based on stepwise cluster analysis method. The SCADS uses a cluster tree to represent the complex relationship between large-scale atmospheric variables (namely predictors) and local surface variables (namely predictands). It can effectively deal with continuous and discrete variables, as well as nonlinear relations between predictors and predictands. By integrating ancillary functional modules of missing data detecting, correlation analysis, model calibration and graphing of cluster trees, the SCADS is capable of performing rapid development of downscaling scenarios for local weather variables under current and future climate forcing. An application of SCADS is demonstrated to obtain 10 km daily mean temperature and monthly precipitation projections for Toronto, Canada in 2070–2099. The contemporary reanalysis data derived from NARR is used for model calibration (1981–1990) and validation (1991–2000). The validated cluster trees are then applied for generating future climate projections.     

Expert elicitation of autocorrelated time series with application to e3 (energy-environment-economic) forecasting models

Explicitly representing uncertainty is recognised as a fundamental requirement of any long-term forecast. We propose and illustrate an expert elicitation protocol for constructing long-term probabilistic projections. Each projection represents a possible realization of a time series with autocorrelation properties, and thus a plausible future evolution of a quantity of interest. We illustrate the approach using two quantities – GDP growth rates and coal prices – that were elicited as part of a project producing baseline forecasts of greenhouse gas emissions in South Africa to 2050. The elicited projections can be used as inputs to deterministic structural models of the energy, economic, and environmental sectors (e3 or energy-environment-economic models), to generate similar probabilistic projections for any desired outputs of the e3 model. An R package for the generation and visualization of these probabilistic projections is provided.     

Integrated assessment of sea-level rise adaptation strategies using a Bayesian decision network approach

The exposure to sea-level rise (SLR) risks emerges as a challenging issue in the broader debate about the possible consequences of global environmental change for at least four reasons: the potentially serious impacts, the very high uncertainty regarding future projections of SLR and their effects on the environmental and socio-economic system, the multiple scales involved, and the need to take effective management decisions in terms of climate change adaptation. Unfortunately, mechanistic models generally demonstrated a limited ability to characterise in appropriate detail how complex coastal systems and their constituent parts may respond to climate change drivers and to possible adaptation initiatives. The research reported here develops an innovative methodological framework, which integrates different research areas – participatory and probabilistic modelling, and decision analysis – within a coordinated process aimed at decision support. The effectiveness of alternative adaptation measures in a lagoon in north-east Italy is assessed by means of Bayesian Decision Network (BDN) models, developed upon judgments elicited from selected experts. A concept map of the system was first developed in a group brainstorming context and was later evolved into BDN models, thus providing a simplified quantitative structure. Conditional probabilities, quantifying the causal links between the direct and indirect consequences of SLR on the area of study, are elicited from the experts. The proposed methodological framework allows the integrated assessment of factors and processes belonging to different domains of knowledge. Moreover, it activates an informed and transparent participatory process involving disciplinary experts and policy makers, where the main risk factors are considered together with the expected effects of the adaptation options, with effective treatment and communication of the uncertainty pervading the SLR issue. Finally, the framework shows potentials for being further developed and applied to consider new evidences and/or different adaptation strategies, and it results sufficiently flexible to be adopted and effectively reused in other similar case studies.     

An open framework for agent based modelling of agricultural land use change

There is growing interest in creating empirically grounded agent based models (ABMs) to simulate land use change at a variety of spatio-temporal scales. The development of land use change models is challenging, as there is a need to connect representations of human behavioural processes to simulations of the biophysical environment. This paper presents a new agent-based modelling framework (Aporia) that has the goal of reducing the complexity and difficulty of constructing high-fidelity land use models. Building on earlier conceptual developments for modelling land use change and the provision of ecosystem services, Aporia was designed to be modular, flexible and open, using a declarative, compositional approach to create complex models from subcomponents. The framework can be tightly or loosely coupled with multiple vegetation models, it can be set up to evaluate a range of ecosystem service indicators, and it can be calibrated for a range of different landscape-scale case studies and modelling styles. The framework is released under an Open Source licence, and can be freely re-used and modified to form the basis of new models. We illustrate this with two case studies implemented using Aporia, exploring different socio-economic scenarios and behavioural characteristics on the land use decisions of Swiss and Scottish farmers. We also discuss the benefits of frameworks in terms of their flexibility, expandability, verification and transparency.     

An atmospheric visual analysis and exploration system

Meteorological research involves the analysis of multi-field, multi-scale, and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. Unfortunately, traditional atmospheric visualization systems only provide tools to view a limited number of variables and small segments of the data. These tools are often restricted to two-dimensional contour or vector plots or three-dimensional isosurfaces. The meteorologist must mentally synthesize the data from multiple plots to glean the information needed to produce a coherent picture of the weather phenomenon of interest. In order to provide better tools to meteorologists and reduce system limitations, we have designed an integrated atmospheric visual analysis and exploration system for interactive analysis of weather data sets. Our system allows for the integrated visualization of 1D, 2D, and 3D atmospheric data sets in common meteorological grid structures and utilizes a variety of rendering techniques. These tools provide meteorologists with new abilities to analyze their data and answer questions on regions of interest, ranging from physics-based atmospheric rendering to illustrative rendering containing particles and glyphs. In this paper, we will discuss the use and performance of our visual analysis for two important meteorological applications. The first application is warm rain formation in small cumulus clouds. Here, our three-dimensional, interactive visualization of modeled drop trajectories within spatially correlated fields from a cloud simulation has provided researchers with new insight. Our second application is improving and validating severe storm models, specifically the Weather Research and Forecasting (WRF) model. This is done through correlative visualization of WRF model and experimental Doppler storm data.     

Evaluating four downscaling methods for assessment of climate change impact on ecological indicators

Assessments of climate change impacts on freshwater ecosystems are generally based on global climate models (GCMs) and ecologically relevant “time-averaged” hydrological indicators derived from long-term records. Although uncertainties from GCMs have been recognized, the influence of downscaling methods remains unclear. This paper evaluates the influence of applying different downscaling methods of increasing complexity (annual scaling, monthly scaling, quantile scaling, and weather generator method) on the assessment of ecological outcomes. In addition to time-averaged indicators, “sequence-dependent” metrics which involve ecological dynamics by considering the impacts of flow sequencing are also adopted. In a case study in Australia, the condition of river red gum forest was assessed. Results show that the choice of downscaling methods can be of similar importance as that of GCMs in ecological impact studies. Where sequence-dependent metrics are adopted, more sophisticated downscaling techniques should be used to better represent changes in the frequency and sequence of flow events.     

Web based visualization of large climate data sets

We have implemented the USGS National Climate Change Viewer (NCCV), which is an easy-to-use web application that displays future projections from global climate models over the United States at the state, county and watershed scales. We incorporate the NASA NEX-DCP30 statistically downscaled temperature and precipitation for 30 global climate models being used in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and hydrologic variables we simulated using a simple water-balance model. Our application summarizes very large, complex data sets at scales relevant to resource managers and citizens and makes climate-change projection information accessible to users of varying skill levels. Tens of terabytes of high-resolution climate and water-balance data are distilled to compact binary format summary files that are used in the application. To alleviate slow response times under high loads, we developed a map caching technique that reduces the time it takes to generate maps by several orders of magnitude. The reduced access time scales to >500 concurrent users. We provide code examples that demonstrate key aspects of data processing, data exporting/importing and the caching technique used in the NCCV.     

Climate policy under socio-economic scenario uncertainty

We study the role of uncertainty about the two main baseline drivers of the economy, namely population and GDP, for the determination of the optimal climate policy and the evaluation of policy costs. Firstly, we estimate the cost of baseline uncertainty from a decision maker's perspective using different metrics. Secondly, we discuss how measures of the costs of climate change induced impacts and climate policy costs can be compared under different and uncertain baseline assumptions. Given that policy costs and other measures such as impacts are typically expressed relative to GDP in a baseline, comparing those values with different baseline projections is not trivial. Finally, we compute the cost from baseline uncertainty which leads to a moderate increase of the welfare losses from climate change.     

区域MODIS水汽季节修正模型

针对MODIS水汽空间分辨率高但精度不高的不足,提出了一种利用GNSS的MODIS水汽校正模型。MODIS水汽校正模型通过GNSS水汽和线性回归方法分季节构建。通过GNSS水汽与MODIS水汽的相关性分析比较,利用线性回归方法分季节构建区域和城市MODIS水汽校正模型,通过与GNSS水汽比较验证区域和城市模型的可靠性。研究结果表明,GNSS水汽与MODIS水汽的变化趋势基本一致,存在显著正相关特性;经检验3个测站的城市和区域,冬季模型均方根误差优于1mm,春、秋季均方根误差接近,约为2mm,夏季城市模型均方根误差最大,值为6.58mm。区域模型有效提高MODIS水汽精度,可为短时天气预报提供基础。     

Phenology and carbon fixing: a satellite-based study over Continental USA

Global climate change has led to concerns about its impact on our biosphere and vegetation. Any impact of climate on vegetation can manifest in terms of changes in plant growth characteristics, its health and timing of different vegetative phenomena, such as germination, bud burst, maturity, etc. The duration and changes in the timing of plant growth stages can in turn impact the global carbon cycle. Similarly any change in plant productivity, because of changing climate will alter the carbon flux pattern by changing the overall biological flux being added or taken away from the atmosphere. We have used satellite data to study spatiotemporal changes in the plant phenology and plant productivity over the Continental USA (CONUS) to get an overall understanding of the evolution of these metrics over the past decade. Our study reveals that the prairies situated in the heartland of CONUS have become an increasingly important player in determining any changes in vegetation induced carbon source/sink patterns. The northern Great Plains has shown increased fixation of carbon in recent years, while the southern Plains has become a carbon source. This has been largely driven by changes in recent weather patterns where the northern plains have seen an increasingly cooler and wetter growing season whereas the southern plains have at the same time seen increased aridity, especially since 2011. This is also reflected in increasing growing season greenness values over the northern Plains and the opposite over the southern Plains. The gradual changing pattern of land biological fluxes over CONUS, as documented in this paper will likely be of interest to climate modellers as they seek to better understand the interaction between global carbon balance and climate change.     

Simulating wildfires backwards in time from the final fire perimeter in point-functional fire models

Fire propagation models simulate wildfires forward in time from a set of ignition locations, but are usually unable to be used backwards if only a final fire perimeter is available. This approach is useful to search fire ignition points, reconstruct past fire events, adjust fire simulators and other purposes. This study proposes three different algorithms: a short time range backwards in time simulation from the perimeter, a statistical analysis related to the likelihood of a fire ignition location over the domain, and an analysis aiming to multiple ignition locations. The methods presented are fast to be solved and may be used with any empirical fire propagation model as a core engine as long as the ROS is locally defined and the model is not coupled to the weather.     

SimiVal,a multi-criteria map comparison tool for land-change model projections

The multiple uses of land-cover models have led to validation with choice metrics or an ad hoc choice of the validation metrics available. To address this, we have identified the major dimensions of land-cover maps that ought to be evaluated and devised a Similarity Validation (SimiVal) tool. SimiVal uses a linear regression to test a modelled projection against benchmark cases of, perfect, observed and systematic-bias, calculated by rescaling the metrics from a random case relative to the observed, perfect case. The most informative regression coefficients, p-value and slope, are plot on a ternary graph of ‘similarity space’ whose extremes are the three benchmark cases. SimiVal is tested on projections of two deliberately contrasting land-cover models to show the similarity between intra- and inter-model parameterisations. We find metrics of landscape structure are important in distinguishing between different projections of the same model. Predictive and exploratory models can benefit from the tool.     

SimilarityExplorer: A Visual Inter‐Comparison Tool for Multifaceted Climate Data

Inter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.     

A multi-scale urban integrated assessment framework for climate change studies: A flooding application

In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide.     

Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis

Integrated assessment models for climate change (IAMs) couple representations of economic and natural systems to identify and evaluate strategies for managing the effects of global climate change. In this study we subject three policy scenarios from the globally-aggregated Dynamic Integrated model of Climate and the Economy IAM to a comprehensive global sensitivity analysis using Sobol' variance decomposition. We focus on cost metrics representing diversions of economic resources from global world production. Our study illustrates how the sensitivity ranking of model parameters differs for alternative cost metrics, over time, and for different emission control strategies. This study contributes a comprehensive illustration of the negative consequences associated with using a priori expert elicitations to reduce the set of parameters analyzed in IAM uncertainty analysis. The results also provide a strong argument for conducting comprehensive model diagnostics for IAMs that explicitly account for the parameter interactions between the coupled natural and economic system components.     

The methods for inferring surface fluxes from satellite data,and their use for atmosphere model validation

This paper reviews the methods developed to infer surface fluxes from satellite data, with or without the aid of other meteorological information. These methods are based on physical modelling or on statistical relations between satellite measurements and surface parameters. However,their accuracy and their possible applications differ from each other. In particular, the surface radiation budget can be obtained over sea and over particular land areas with rather good accuracy. Some of these methods could constitute the basis for future use in atmosphere models (for climate modelling or meteorological forecasting), but the quality of most of them has still to be assessed. They are therefore rarely directly used to test atmospheric models (weather prediction or climate global circulation models). A preferred approach consists of comparisons between satellite data (either direct measurements or well-established retrieved parameters) and the same parameters derived from models.