Climate Impact Analysis Research Articles

A Temporal Comparison of Drought Impacts and Responses in the New York City Metropolitan Area

Climate impact analyses seldom examine temporal changes in the impacts and responses associated with climate anomalies. Newspaper reports, quantitative agricultural and water resource data and a survey of drought sensitive segments of society are used to compare the impacts and responses of a 1995 drought in the New York City metropolitan area to those experienced during five previous droughts. Impacts related to surface water supplies dominated each of the drought periods studied. Over time, however, changes in water consumption habits and available reservoir capacity lead to an increase in impact severity for similar meteorological conditions. To account for these non-climatic influences on water storage, a method to adjust for trends in available reservoir storage and water use is developed and implemented. Once adjusted, a fairly strong (R2 = 70.9%) exponential relationship exists between the minimum reservoir level and Palmer Drought Severity Index experienced during each drought period. Unadjusted levels exhibit a considerably weaker (R2 = 49.0%) relationship. Changes in water consumption and reservoir capacity also influenced the enactment of voluntary and ultimately mandatory water conservation measures. These restrictions were responsible for business and industrial impacts that varied through time as technology evolved and societal attitudes changed. Minor time-dependent changes were also evident in agricultural and wildfire impacts.

A stochastic weather generator applied to hydrological models in climate impact analysis

A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.

CART Decision-Tree Statistical Analysis and Prediction of Summer Season Maximum Surface Ozone for the Vancouver, Montreal, and Atlantic Regions of Canada

Prediction of daily maximum surface ozone (O3) concentration was begun by Environment Canada in the spring of 1993 for the Vancouver, Montreal, and Atlantic regions in order to advise the public of expected air quality. Forecasts have been issued for southern Ontario for many years by the province of Ontario, but this is a new undertaking in other parts of the country, where air quality has become a concern in recent years. There is a need for guidance to prepare the forecasts, particularly for prediction of surface O3 concentration levels near or exceeding the Canadian 1-h maximum acceptable concentration of 82 ppb. Such occurrences are episodic and relatively rare in southern Canada. Probability of occurrence is in the range 0.00–0.08 at the sites in the regions studied here, thus, reliable prediction is difficult without guidance. Mesoscale numerical meteorological–photochemical models are not currently available for routine use in operations, but the capability exists for development and use of sophisticated multivariate statistical techniques for prediction of daily maximum O3 concentration. Most statistical ozone forecast procedures to date in Canada have been based on multiple linear regression with a limited number of predictors mainly drawn from surface meteorology and subjective classification of the synoptic meteorological flow pattern. Since the relationship between surface O3 and meteorology is nonlinear, tree-based statistical models with several predictors are appropriate for developing objective forecast guidance. Surface and upper-air meteorological predictors and other predictors were matched with several years of observed daily maximum O3 concentrations for the months of May–September at air-monitoring sites in the three regions. A recent nonparametric data-driven tree-based analysis method known as CART (classification and regression trees) was used to analyze the data at each site. The decision trees built by CART were found to fit the data reasonably well, and the rules for node splitting were found to be physically realistic. Some of the important aspects of the analyses are noted. One interesting result was that moisture content of the air plays a limiting role on the maximum surface O3 concentration that can be achieved when other factors point to occurrence of high values. The decision trees can be used to predict maximum surface O3 concentrations if the predictor variables are forecast, thus providing an inexpensive site-specific model for forecasts and climate impact analysis. An estimation of performance with independent data was conducted for the Vancouver–lower Fraser River valley and Montreal regions for each of the five years 1988–92. Verification of the ensemble of forecasts in the two regions shows the technique would have reasonably good skill in forecasting surface O3 concentrations near or exceeding acceptable 1-h limits. A computer version of the technique has been provided for use in the regional forecast offices.

The causal structure of vulnerability: Its application to climate impact analysis

The causal structure of vulnerability: Its application to climate impact analysis

The use of general circulation models in the analysis of the ecosystem impacts of climatic change

The use of general circulation models in the estimation of the impact of climatic change on the global ecosystem is seen to depend primarily on their ability to reliably depict the seasonal and geographical distribution of the changes in surface climate variables. While present GCMs generally simulate the observed distribution of surface air temperature reasonably well, they show significantly different changes in the equilibrium temperature as a result of doubled CO2, for example. These disagreements are attributed to differences in the model's resolution and parameterization of subgrid-scale processes. Such model-dependent errors notwithstanding, much more information of possible use in impact analysis can be extracted from general circulation model simulations than has generally been done so far. The completeness, consistency and experimental possibilities offered by simulated data sets permit the systematic extraction of a wide variety of statistics important to the surface ecosystem, such as the length of the growing season, the duration of rainless periods, and the surface moisture stress. Assuming further model improvements, the elements of a model-assisted methodology for climate impact analysis are seen to be: (1) the determination of the seasonal and geographical distribution of that portion of simulated climatic changes which are both statistically and physically significant; (2) the transformation of the (significant) large-scale climatic changes onto the local scale of impact (the climate ‘inversion’ problem); and (3) the design of specific statistical parameters or functions relevant to local ecosystem impacts.

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.