Fast versus slow response in climate change: implications for the global hydrological cycle

Recent studies have shown that changes in global mean precipitation are larger for solar forcing than for CO2 forcing of similar magnitude.In this paper, we use an atmospheric general circulation model to show that the differences originate from differing fast responses of the climate system. We estimate the adjusted radiative forcing and fast response using Hansen's ``fixed-SST forcing'' method.Total climate system response is calculated using mixed layer simulations using the same model. Our analysis shows that the fast response is almost 40% of the total response for few key variables like precipitation and evaporation. We further demonstrate that the hydrologic sensitivity, defined as the change in global mean precipitation per unit warming, is the same for the two forcings when the fast responses are excluded from the definition of hydrologic sensitivity, suggesting that the slow response (feedback) of the hydrological cycle is independent of the forcing mechanism. Based on our results, we recommend that the fast and slow response be compared separately in multi-model intercomparisons to discover and understand robust responses in hydrologic cycle. The significance of this study to geoengineering is discussed.

Professional Driving : An Emphasis on Organizational Safety Climate

According to the literature and statistical figures, professional drivers constitute a high-risk group in traffic and should be investigated in connection with the factors related to safe driving. However, safety-related behaviours and outcomes among professional drivers have attracted very little attention from safety researchers. In addition, comparing different professional and non-professional driver groups in terms of critical on-the-road characteristics and outcomes has been indicated in the literature as being necessary for a more comprehensive understanding of driver groups and the nature of driving itself. The aim of the present study was to investigate professional driving from a safety climate stand point in relation to predominant driving-related factors and by considering the differences between driver groups. Hence, four Sub-studies were conducted according to a framework emphasizing the relationships between safety climate, driver groups, driver stress, human factors (i.e., driver behaviour and performance) and accidents. Demographic information, as well as data for driver behaviour, performance, and driver stress was collected by questionnaire. The data was analysed using factor analysis, analysis of covariance as well as hierarchical and logistic regression analysis. The results revealed multi-dimensional factor structures for the safety climate measures. Considering the relationships between variables, differences were evidenced regarding on-the-road stress reactions, risky driver behaviours and penalties, between the various professional and non-professional driver groups. Driver stress was found to be related to accidents. The results also indicated that the safety climate has positive relationships with both driver behaviour and performance, and as well as involvement in accidents. The present study has a number of critical implications resulting from the fact that the way in which the effects of safety climate on professional driving were investigated, as well as the differences between professional and non-professional driver groups, was unique. Additionally, for the first time, a safety climate scale was developed specifically for professional drivers. According to the results of the study and to previous literature, a tentative model was proposed representing a possible route for the relationships between safety climate, human factors, driver stress, driver groups and accidents, by emphasizing the effects of safety climate.

The decline of malaria in Finland – the impact of the vector and social variables

Background: Malaria was prevalent in Finland in the 18th century. It declined slowly without deliberate counter-measures and the last indigenous case was reported in 1954. In the present analysis of indigenous malaria in Finland, an effort was made to construct a data set on annual malaria cases of maximum temporal length to be able to evaluate the significance of different factors assumed to affect malaria trends. Methods: To analyse the long-term trend malaria statistics were collected from 1750–2008. During that time, malaria frequency decreased from about 20,000 – 50,000 per 1,000,000 people to less than 1 per 1,000,000 people. To assess the cause of the decline, a correlation analysis was performed between malaria frequency per million people and temperature data, animal husbandry, consolidation of land by redistribution and household size. Results: Anopheles messeae and Anopheles beklemishevi exist only as larvae in June and most of July. The females seek an overwintering place in August. Those that overwinter together with humans may act as vectors. They have to stay in their overwintering place from September to May because of the cold climate. The temperatures between June and July determine the number of malaria cases during the following transmission season. This did not, however, have an impact on the longterm trend of malaria. The change in animal husbandry and reclamation of wetlands may also be excluded as a possible cause for the decline of malaria. The long-term social changes, such as land consolidation and decreasing household size, showed a strong correlation with the decline of Plasmodium. Conclusion: The indigenous malaria in Finland faded out evenly in the whole country during 200 years with limited or no counter-measures or medication. It appears that malaria in Finland was basically a social disease and that malaria trends were strongly linked to changes in human behaviour. Decreasing household size caused fewer interactions between families and accordingly decreasing recolonization possibilities for Plasmodium. The permanent drop of the household size was the precondition for a permanent eradication of malaria.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Regional impacts of climate change on irrigation water demands

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U-2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U-2. Consequently, the reference evapotranspiration, modeled by the Penman-Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U-2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright (c) 2012 John Wiley & Sons, Ltd.

Regional hydrological impacts of climate change: implications for water management in India

Climate change is most likely to introduce an additional stress to already stressed water systems in developing countries. Climate change is inherently linked with the hydrological cycle and is expected to cause significant alterations in regional water resources systems necessitating measures for adaptation and mitigation. Increasing temperatures, for example, are likely to change precipitation patterns resulting in alterations of regional water availability, evapotranspirative water demand of crops and vegetation, extremes of floods and droughts, and water quality. A comprehensive assessment of regional hydrological impacts of climate change is thus necessary. Global climate model simulations provide future projections of the climate system taking into consideration changes in external forcings, such as atmospheric carbon-dioxide and aerosols, especially those resulting from anthropogenic emissions. However, such simulations are typically run at a coarse scale, and are not equipped to reproduce regional hydrological processes. This paper summarizes recent research on the assessment of climate change impacts on regional hydrology, addressing the scale and physical processes mismatch issues. Particular attention is given to changes in water availability, irrigation demands and water quality. This paper also includes description of the methodologies developed to address uncertainties in the projections resulting from incomplete knowledge about future evolution of the human-induced emissions and from using multiple climate models. Approaches for investigating possible causes of historically observed changes in regional hydrological variables are also discussed. Illustrations of all the above-mentioned methods are provided for Indian regions with a view to specifically aiding water management in India.

The Health Effects of Climate Change: A Survey of Recent Quantitative Research

44 p.

New climate scenario framework implementation in the GCAM integrated assessment model

48 p.

1976 step in the Pacific climate: forty environmental changes between 1968-1975 and 1977-1984

Examination of 40 time series of multidisciplinary environmental variables from the Pacific Ocean and the Americas, collected in 1968 to 1984, demonstrated the remarkable consistency of a major climate-related, step-like change in 1976. To combine the 40 variables (e.g., air and water temperatures, Southern Oscillation, chlorophyll, geese, salmon, crabs, glaciers, atmospheric dust, coral, carbon dioxide, winds, ice cover, Bering Strait transport) into a single time series, standard variants of individual annual values (subtracting the mean and dividing by a standard deviation) were averaged. Analysis of the resulting time series showed that the single step in 1976, separating the 1968-1975 period from the 1977-1984 period, accounted for 89% of variance within the composite time series. Apparently, one of the Earth's large ecosystems occasionally undergoes large abrupt shifts.

Associations among chlorophyll concentration and climatic variables for Suisun Bay and the Sacramento-San Joaquin Delta [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): The recent changes in phytoplankton production and community composition within the Suisun Bay and Sacramento-San Joaquin Delta may be related to climate. Chlorophyll a concentration, decreased by 42% (spring-summer) and 29% (fall) between 1972 through 1976 and 1977 through 1981. The decrease in biomass was characterized by a shift in phytoplankton community dominance from Skeletonema spp., Cyclotella spp. and Coscinodiscus spp. to Melosira granulata. The possible influence of climate on phytoplankton abundance was suggested by multivariate statistical analyses that demonstrated an association between changes in phytoplankton community composition and abundance between 1975 and 1982 and the climate related variables wind velocity, precipitation, river flow and water temperature.

In search of climate effects on Atlantic Croaker (Micropogonias undulatus) stock off the U.S. Atlantic coast with Bayesian state-space biomass dynamic models

Atlantic Croaker (Micropogonias undulatus) production dynamics along the U.S. Atlantic coast are regulated by fishing and winter water temperature. Stakeholders for this resource have recommended investigating the effects of climate covariates in assessment models. This study used state-space biomass dynamic models without (model 1) and with (model 2) the minimum winter estuarine temperature (MWET) to examine MWET effects on Atlantic Croaker population dynamics during 1972–2008. In model 2, MWET was introduced into the intrinsic rate of population increase (r). For both models, a prior probability distribution (prior) was constructed for r or a scaling parameter (r0); imputs were the fishery removals, and fall biomass indices developed by using data from the Multispecies Bottom Trawl Survey of the Northeast Fisheries Science Center, National Marine Fisheries Service, and the Coastal Trawl Survey of the Southeast Area Monitoring and Assessment Program. Model sensitivity runs incorporated a uniform (0.01,1.5) prior for r or r0 and bycatch data from the shrimp-trawl fishery. All model variants produced similar results and therefore supported the conclusion of low risk of overfishing for the Atlantic Croaker stock in the 2000s. However, the data statistically supported only model 1 and its configuration that included the shrimp-trawl fishery bycatch. The process errors of these models showed slightly positive and significant correlations with MWET, indicating that warmer winters would enhance Atlantic Croaker biomass production. Inconclusive, somewhat conflicting results indicate that biomass dynamic models should not integrate MWET, pending, perhaps, accumulation of longer time series of the variables controlling the production dynamics of Atlantic Croaker, preferably including winter-induced estimates of Atlantic Croaker kills.

Time series analyses of biological and environmental variables for Suisun Bay and the Sacramento and San Joaquin rivers

EXTRACT (SEE PDF FOR FULL ABSTRACT): Arima analysis was used to compute cross-correlations between principal component axes that described environmental variables, chlorophyll concentration and zooplankton density for the Sacramento and San Joaquin rivers and Suisun Bay. ... Cross-correlations among the time series may provide information about links between environmental and biological variables within the estuary and the possible influence of climate.

Interactions between climatic variables controlling subalpine tree growth: implications for climatic history of the Sierra Nevada, California

EXTRACT (SEE PDF FOR FULL ABSTRACT): Tree-ring records from foxtail pine (Pinus balfouriana) and western juniper (Juniperus occidentalis) growing near tree line in the eastern Sierra Nevada, California, show strong correlations with summer temperature and winter precipitation. Response surfaces portraying tree growth as a function of summer temperature and winter precipitation indicate a strong interaction between these variables in controlling growth. ... Above average growth for both foxtail pine and western juniper from AD 1480 to 1570 can be interpreted as indicating an extended period of warm, moist conditions unequalled during the 20th century.

Influence of climate on environmental factors associated with long-term changes in chlorophyll production for the Sacramento-San Joaquin delta and Suisun Bay, California

Long-term changes in chlorophyll production were predicted from environmental variables for the Sacramento and San Joaquin rivers and Suisun Bay using Box-Jenkins transfer function models. Data used for the analyses were collected semimonthly or monthly between 1971 and 1987. Transfer function models developed to describe changes in chlorophyll production over time as a function of environmental variables were characterized by lagged responses and described between 39 and 51 percent of the data variation. Significant correlations between environmental variables and the California climate index (CA SLP) were used to develop a conceptual model of the link between regional climate and estuarine production.