Assessing the sensitivity of Canada's ecosystems to climatic change

Global warming due to increased concentrations of atmospheric CO₂ is expected to be amplified in middle and higher latitudes. Consequently, ecosystems in these latitudes will experience more pronounced climatic variations. This investigation attempts to assess the sensitivity of Canada's ecosystems to climatic change. Potential ecological impacts resulting from global warming are outlined. With this background, the effects of a doubled CO₂ climate are applied to a classification model derived from the current ecological setting. Results reveal not only major shifts in ecological boundaries but also changes in the character of these broadly distributed ecosystems.     

Assessing the impact of climatic change on food production

Attempts to assess the impact of a hypothetical climatic change on food production have relied on the use of statistical models which predict crop yields using various climatic variables. It is emphasized that the coefficients of these models are not universal constants, but rather statistical estimates subject to several sources of error. Thus, any statement regarding the estimated impact of climatic change on food production must be qualified appropriately.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Harvest dates in ancient mesopotamia as possible indicators of climatic variations

Butzer (1958; also, Lamb, 1968) puts forward observations to the effect that about 2500 B.C. a period of increased aridity set in in the general area of the Near East, including Mesopotamia, and that rainier conditions returned after about 850 B.C. Because in the Mediterranean type of climate (winter rains) of the Near East winter temperatures and seasonal rainfall are highly correlated in a negative sense, we expect that the above mentioned period of aridity was also a period of relative warmth, whereas the period after 850 B.C. up to Roman times, was a period of cool winters. This negative correlation is thought to be due to the fact that in the Near East rainfall is usually produced in the migratory cyclones, especially close and behind the associated cold fronts which bring in cold air as well. A failure of these cyclones to reach the region in numbers, manifests itself both in reduced rainfall and comparatively high winter temperatures. In order to check on these ideas on temperature conditions, a search was undertaken of references to barley harvest dates in the literature (clay tablets) of ancient Babylonia comprising two periods: 1800-1650 B.C. (Late Old Babylonian Period = L.O.B.P.) and 600-400 B.C. (Neo-Babylonian Period = N.B.P.). It turns out that in the former, the harvest began late in March or early in April, while in the latter, it began late in April or in May, amounting to a difference of about a month or slightly more. In our own era, in what was once central and northern Babylonia, harvest begins in the second half of April or so. Thus in the L.O.B.P., harvest opened 10–20 days earlier and in the N.B.P. 10–20 days later than at present. The differences between the harvest dates of the two periods seem to be too large to be explained by changes in the barley varieties cultivated. On the other hand, the above mentioned inverse relationship between rainfall and winter temperatures supports the interpretation that the L.O.B.P. was warmer, and the N.B.P. cooler than the present era, and goes some way toward explaining the inferred differences in harvest dates.     

Assessing future climatic changes of rainfall extremes at small spatio-temporal scales

Climate change is expected to influence the occurrence and magnitude of rainfall extremes and hence the flood risks in cities. Major impacts of an increased pluvial flood risk are expected to occur at hourly and sub-hourly resolutions. This makes convective storms the dominant rainfall type in relation to urban flooding. The present study focuses on high-resolution regional climate model (RCM) skill in simulating sub-daily rainfall extremes. Temporal and spatial characteristics of output from three different RCM simulations with 25 km resolution are compared to point rainfall extremes estimated from observed data. The applied RCM data sets represent two different models and two different types of forcing. Temporal changes in observed extreme point rainfall are partly reproduced by the RCM RACMO when forced by ERA40 re-analysis data. Two ECHAM forced simulations show similar increases in the occurrence of rainfall extremes of over a 150-year period, but significantly different changes in the magnitudes. The physical processes behind convective rainfall extremes generate a distinctive spatial inter-site correlation structure for extreme events. All analysed RCM rainfall extremes, however, show a clear deviation from this correlation structure for sub-daily rainfalls, partly because RCM output represents areal rainfall intensities and partly due to well-known inadequacies in the convective parameterization of RCMs. The results highlight the problem urban designers are facing when using RCM output. The paper takes the first step towards a methodology by which RCM performance and other downscaling methods can be assessed in relation to the simulation of short-duration rainfall extremes.     

Biophysical indicators and Indigenous and Local Knowledge reveal climatic and ecological shifts with implications for Arctic Char fisheries

Managing Arctic marine resources to be resilient to environmental changes requires knowledge of how climate change is affecting marine food webs and fisheries. Changes to fishery resources will have major implications for coastal Indigenous communities whose livelihoods, health, and cultures are strongly connected to fisheries. Understanding these broad social-ecological changes requires a transdisciplinary approach bringing together contrasting and complementary disciplines and ways of knowing. Here, we examine climatic proxies, ecological, and fishery indicators (stable isotopes, fish condition, and lipid content), and interviews with Inuit fishers to assess how marine ecosystem changes have influenced Arctic Char (Salvelinus alpinus) ecology and fisheries over a 30-year time period (1987–2016) in the Kitikmeot region of the Canadian Arctic. Inuit fishers reported several observations of environmental changes, including longer ice-free seasons, warmer ocean temperatures, and the arrival of new marine species. Biophysical data revealed important changes toward earlier dates of ice breakup (>12 days in some areas) and a shift in isotopic niche reflecting a changing Arctic Char diet, with increased contribution of pelagic carbon and higher trophic level prey. Fish condition was improved in years with earlier ice breakup, as observed by both Inuit fishers and biophysical indicators, while lipid content increased through time, suggesting that longer ice-free seasons may have a positive effect on Arctic Char quality as reflected by both fish condition and lipid content. Long-term impacts of continuing climate change, however, such as the northward expansion of boreal species and increasing ocean temperatures, could have negative effects on fisheries (e.g., physiological impairment in fish if temperatures exceed their thermal range). Continuous community-based monitoring that directly informs fisheries management could help communities and managers adaptively, and sustainably, manage in the face of multiple interacting changes in Arctic marine systems.     

Assessing potential impacts of climatic change on subalpine forests on the eastern Tibetan Plateau

Forest gap models have been used widely in the study of forest dynamics, including predicting long-term succession patterns and assessing the potential impacts of climate change on forest structure and composition. However, little effort is devoted to predict forest dynamics in the high elevation areas, although they have the sensitive response to global climate change. In the present study, based on a modified height-diameter function, we developed a new version (FAREAST-GFSM) of the forest patch model, FAREAST for simulating the changes of subalpine forests. The observed data from the Gongga Mt. Alpine Station were also used to test model precision. With the improved performance of FAREAST-GFSM, we explored the impact of three warming scenarios on subalpine forest on the eastern Tibetan plateau within a 100-year period. The study result indicates that the effects of climate change were evident on subalpine forests in the high elevation areas. The response of different species to the warming climate might eventually transform the subalpine Abies fabric forest into Betula utilis forest similar to that which is now widely distributed in the eastern Tibetan Plateau mountainous areas with the relatively lower elevation. Subalpine forests could move to higher and colder areas, which are currently tundra.     

A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change

To better prioritise adaptation strategies to a changing climate that are currently being developed, there is a need for quantitative regional level assessments that are systematic and comparable across multiple weather hazards. This study presents an indicator-based impact assessment framework at NUTS-2 level for the European Union that quantifies potential regional changes in weather-related hazards: heat stress in relation to human health, river flood risk, and forest fire risk. This is done by comparing the current (baseline) situation with two future time periods, 2011–2040 and 2041–2070. The indicator values for the baseline period are validated against observed impact data. For each hazard, the method integrates outcomes of a set of coherent high-resolution regional climate models from the ENSEMBLES project based on the SRES A1B emission scenario, with current and projected non-climatic drivers of risk, such as land use and socio-economic change. An index of regional adaptive capacity has been developed and compared with overall hazard impact in order to identify the potentially most vulnerable regions in Europe. The results show strongest increases in impacts for heat stress, followed by forest fire risk, while for flood risk the sign and magnitude of change vary across regions. A major difference with previous studies is that heat stress risk could increase most in central Europe, which is due to the ageing population there. An overall assessment combining the three hazards shows a clear trend towards increasing impact from climaterelated natural hazards for most parts of Europe, but hotspot regions are found in eastern and southern Europe due to their low adaptive capacities. This spatially explicit assessment can serve as a basis for discussing climate adaptation mainstreaming, and priorities for regional development in the EU.     

Assessing the influence of climate model uncertainty on EU-wide climate change impact indicators

Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional climate models (RCMs) forced by different global circulation models (GCMs). This study builds upon an indicator-based NUTS-2 level assessment that quantified potential changes for three climate-related hazards: heat stress, river flood risk, and forest fire risk, based on five GCM/RCM combinations, and non-climatic factors. First, a sensitivity analysis is performed to determine the fractional contribution of each single input factor to the spatial variance of the hazard indicators, followed by an evaluation of uncertainties in terms of spread in hazard indicator values due to inter-model climate variability, with respect to (changes in) impacts for the period 2041–70. The results show that different GCM/RCM combinations lead to substantially varying impact indicators across all three hazards. Furthermore, a strong influence of inter-model variability on the spatial patterns of uncertainties is revealed. For instance, for river flood risk, uncertainties appear to be particularly high in the Mediterranean, whereas model agreement is higher for central Europe. The findings allow for a hazard-specific identification of areas with low vs. high model agreement (and thus confidence of projected impacts) within Europe, which is of key importance for decision makers when prioritising adaptation options.     

Greenhouse gas emissions from intensive livestock systems: Their estimation and technologies for reduction

Intensive agricultural systems provide opportunities for inefficiency and leakage of materials into the wider environment. Animal production systems are potential sources of two important greenhouse gases, i.e. methane and nitrous oxide. Because of the complexity of the processes involved and of the numerous interactions there is a need to consider the potential for release of these two gases on an integrated, whole farm basis to ascertain the impact of management systems. Using information from the literature, the present study examines the potential losses of CH₄ and N₂O from components of a model intensive dairy farm based on temperate grassland. It is estimated that a typical 76 ha dairy farm in SW England could emit nearly 12 tonnes (t) CH₄-C and over 1 t N₂O-N. Gaps in existing knowledge are identified and options to reduce emissions discussed.     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

Assessing the vulnerability of food crop systems in Africa to climate change

Africa is thought to be the region most vulnerable to the impacts of climate variability and change. Agriculture plays a dominant role in supporting rural livelihoods and economic growth over most of Africa. Three aspects of the vulnerability of food crop systems to climate change in Africa are discussed: the assessment of the sensitivity of crops to variability in climate, the adaptive capacity of farmers, and the role of institutions in adapting to climate change. The magnitude of projected impacts of climate change on food crops in Africa varies widely among different studies. These differences arise from the variety of climate and crop models used, and the different techniques used to match the scale of climate model output to that needed by crop models. Most studies show a negative impact of climate change on crop productivity in Africa. Farmers have proved highly adaptable in the past to short- and long-term variations in climate and in their environment. Key to the ability of farmers to adapt to climate variability and change will be access to relevant knowledge and information. It is important that governments put in place institutional and macro-economic conditions that support and facilitate adaptation and resilience to climate change at local, national and transnational level.     

A quantitative assessment of the contributions of climatic indicators to changes in nutrients and oxygen levels in a shallow reservoir in China

Climate change has an indirect effect on water quality in freshwater ecosystems, but it is difficult to assess the contribution of climate change to the complex system. This study explored to what extent climatic indicators (air temperature, wind speed, and rainfall) influence nutrients and oxygen levels in a shallow reservoir, Yuqiao Reservoir, China. The study comprises three parts—describing the temporal trends of climatic indicators and water quality parameters during the period 1992–2011, analyzing the potential impacts of climate on water quality, and finally developing a quantitative assessment to evaluate how climatic factors govern nutrient levels in the reservoir. Our analyses showed that the reservoir experienced substantial cold periods (1992–2001) followed by a warm period (2002–2011). The results showed that increasing air temperature in spring, autumn, and winter and increasing annual wind speed decrease total phosphorus (TP) concentration in the reservoir in spring, summer, and winter. According to the quantitative assessment, the increase in air temperature in spring and winter had a larger contribution to the decrease in TP concentration (47.2 and 64.1%), compared with the influence from decreased wind speed and rainfall. The field data suggest that nutrients decline due to enhanced uptake by macrophytes in years when spring was warmer and the macrophytes started to grow earlier in the season. The increasing wind speed and air temperature in spring also significantly contribute to the increase in dissolved oxygen concentration. This study helps managers to foresee how potential future climate change might influence water quality in similar lake ecosystems.     

Potential climatic transitions with profound impact on Europe

We discuss potential transitions of six climatic subsystems with large-scale impact on Europe, sometimes denoted as tipping elements. These are the ice sheets on Greenland and West Antarctica, the Atlantic thermohaline circulation, Arctic sea ice, Alpine glaciers and northern hemisphere stratospheric ozone. Each system is represented by co-authors actively publishing in the corresponding field. For each subsystem we summarize the mechanism of a potential transition in a warmer climate along with its impact on Europe and assess the likelihood for such a transition based on published scientific literature. As a summary, the ‘tipping’ potential for each system is provided as a function of global mean temperature increase which required some subjective interpretation of scientific facts by the authors and should be considered as a snapshot of our current understanding.     

Determinants of environmental action with regard to climatic change

The study of human dimensions of global climatic change is still in the initial stage of development. Several attempts have been undertaken to define sensible research strategies in the field but until now relatively little empirical work has been undertaken and there is a lack of sound theoretical arguments. The present paper presents a theory-based empirical study of determinants influencing the probability that somebody takes climate-relevant environmental action. Important methodological differences between current models of climate dynamics and models of human reality are discussed in order to build three models of climate-related environmental action. A model focussed on the information transfer from science to the public at large is compared with a model focussed on sociodemographic characteristics and with a model focussed on socio-cultural variables like interpersonal rules and social networks. The hypothesis that the latter model is strongly superior to the former ones is tested and confirmed. Some implications for interdisciplinary cooperation and for policy making are discussed.     

Dangerous anthropogenic interference, dangerous climatic change, and harmful climatic change: non-trivial distinctions with significant policy implications

Article 2 of the United Nations Framework Convention on Climate Change (UNFCCC) calls for stabilization of greenhouse gas (GHG) concentrations at levels that prevent dangerous anthropogenic interference (DAI) in the climate system. However, some of the recent policy literature has focused on dangerous climatic change (DCC) rather than on DAI. DAI is a set of increases in GHGs concentrations that has a non-negligible possibility of provoking changes in climate that in turn have a non-negligible possibility of causing unacceptable harm, including harm to one or more of ecosystems, food production systems, and sustainable socio-economic systems, whereas DCC is a change of climate that has actually occurred or is assumed to occur and that has a non-negligible possibility of causing unacceptable harm. If the goal of climate policy is to prevent DAI, then the determination of allowable GHG concentrations requires three inputs: the probability distribution function (pdf) for climate sensitivity, the pdf for the temperature change at which significant harm occurs, and the allowed probability (“risk”) of incurring harm previously deemed to be unacceptable. If the goal of climate policy is to prevent DCC, then one must know what the correct climate sensitivity is (along with the harm pdf and risk tolerance) in order to determine allowable GHG concentrations. DAI from elevated atmospheric CO₂ also arises through its impact on ocean chemistry as the ocean absorbs CO₂. The primary chemical impact is a reduction in the degree of supersaturation of ocean water with respect to calcium carbonate, the structural building material for coral and for calcareous phytoplankton at the base of the marine food chain. Here, the probability of significant harm (in particular, impacts violating the subsidiary conditions in Article 2 of the UNFCCC) is computed as a function of the ratio of total GHG radiative forcing to the radiative forcing for a CO₂ doubling, using two alternative pdfs for climate sensitivity and three alternative pdfs for the harm temperature threshold. The allowable radiative forcing ratio depends on the probability of significant harm that is tolerated, and can be translated into allowable CO₂ concentrations given some assumption concerning the future change in total non-CO₂ GHG radiative forcing. If future non-CO₂ GHG forcing is reduced to half of the present non-CO₂ GHG forcing, then the allowable CO₂ concentration is 290–430 ppmv for a 10% risk tolerance (depending on the chosen pdfs) and 300–500 ppmv for a 25% risk tolerance (assuming a pre-industrial CO₂ concentration of 280 ppmv). For future non-CO₂ GHG forcing frozen at the present value, and for a 10% risk threshold, the allowable CO₂ concentration is 257–384 ppmv. The implications of these results are that (1) emissions of GHGs need to be reduced as quickly as possible, not in order to comply with the UNFCCC, but in order to minimize the extent and duration of non-compliance; (2) we do not have the luxury of trading off reductions in emissions of non-CO₂ GHGs against smaller reductions in CO₂ emissions, and (3) preparations should begin soon for the creation of negative CO₂ emissions through the sequestration of biomass carbon.     

A description of persistent climatic anomalies in a 1000-year climatic model simulation

 The Mark 2 version of the CSIRO coupled global climatic model has been used to generate a 1000-year simulation of natural (i.e. unforced) climatic variability representative of “present conditions”. The annual mean output from the simulation has been used to investigate the occurrence of decadal and longer trends over the globe for a number of climatic variables. Here trends are defined to be periods of years with a climatic anomaly of a given sign. The analysis reveals substantial differences between the trend characteristics of the various climatic variables. Trends longer than 12 years duration were unusual for rainfall. Such trends were fairly uniformly distributed over the globe and had an asymmetry in the rate of occurrence for wet or dry conditions. On the other hand, trends in surface wind stress, and especially the atmospheric screen temperature, were of longer duration but primarily confined to oceanic regions. The trends in the atmospheric screen temperature could be traced deep into the oceanic mixed layer, implying large changes in oceanic thermal inertia. This thermal inertia then constituted an important component of the `memory' of the climatic system. While the geographic region associated with a given trend could be identified over several adjacent grid boxes of the model, regional plots for individual years of the trend revealed a range of variations, suggesting that a consistent forcing mechanism may not be responsible for a trend at a given location. Typical return periods for 12-year rainfall trends were once in 1000 years, highlighting the rarity of such events. Using a looser definition of a trend revealed that drying trends up to 50 years duration were also possible, attributable solely to natural climatic variability. Significant (∼20% to 40%) rainfall reductions per year can be associated with a long-term drying trend, hence such events are of considerable climatic significance. It can take more than 100 years for the hydrologic losses associated with such a trend to be overcome. Overall, the simulation provides new and useful insights into climatic trends, and quantifies a number of poorly observed characteristics. The results highlight the extensive and pervasive influence of unforced natural climatic variability as an omnipresent generator of climatic trends. Received: 20 January 2000 / Accepted: 21 September 2000     

Prediction of nonstationary climatic series

Summary Most of the stochastic prediction methods are developed for stationary time series. However, many climatic series show clear evidence of non-stationarity. In such cases, methods based on the stationarity assumptions would be inappropriate. Alternative methods such as those based on stochastic approximation are preferable in these cases because they are based on adaptive learning principles. These methods have not been applied and their suitability not tested with nonstationary climatic time series.In the stochastic approximation method, the deterministic component of a nonstationary time series is estimated by first predicting the two steps ahead value of a time series. The two steps-ahead forecast may involve a term characterizing the trend in the time series. The two steps-ahead predictor is corrected to obtain the one step ahead prediction by using a gain sequence.The dynamic stochastic approximation method is used herein to predict non-stationary climatic time series. Daily minimum temperature series at West Lafayette, Indiana, U.S.A. and seasonal temperature and precipitation series at Evansville, Indiana, U.S.A. are used in the study. For data trends, an improved dynamic stochastic approximation method, called the modified dynamic stochastic approximation method gives more accurate predictions. If the method is used for seasonal data, then it can be used to track the time varying mean value.With 6 Figures