Using second-order approximation to incorporate GCM uncertainty in climate change impact assessments

This study presents a method to incorporate uncertainty of climate variables in climate change impact assessments, where the uncertainty being considered refers to the divergence of general circulation model (GCM) projections. The framework assesses how much bias occurs when the uncertainties of climate variables are ignored. The proposed method is based on the second-order expansion of Taylor series, called second-order approximation (SOA). SOA addresses the bias which occurs by assuming the expected value of a function is equal to the function of the expected value of the predictors. This assumption is not valid for nonlinear systems, such as in the case of the relationship of climate variables to streamflow. To investigate the value of SOA in the climate change context, statistical downscaling models for monthly streamflow were set up for six hydrologic reference stations in Australia which cover contrasting hydro-climate regions. It is shown that in all locations SOA makes the largest difference for low flows and changes the overall mean flow by 1–3%. Another advantage of the SOA approach is that the individual contribution of each climate variable to the total difference can be estimated. It is found that geopotential height and specific humidity cause more bias than wind speeds in the downscaling models considered here.     

Fine-resolution regional climate model simulations of the impact of climate change on tropical cyclones near Australia

Fine-resolution regional climate simulations of tropical cyclones (TCs) are performed over the eastern Australian region. The horizontal resolution (30 km) is fine enough that a good climatological simulation of observed tropical cyclone formation is obtained using the observed tropical cyclone lower wind speed threshold (17 m s^–1). This simulation is performed without the insertion of artificial vortices (bogussing). The simulated occurrence of cyclones, measured in numbers of days of cyclone activity, is slightly greater than observed. While the model-simulated distribution of central pressures resembles that observed, simulated wind speeds are generally rather lower, due to weaker than observed pressure gradients close to the centres of the simulated storms. Simulations of the effect of climate change are performed. Under enhanced greenhouse conditions, simulated numbers of TCs do not change very much compared with those simulated for the current climate, nor do regions of occurrence. There is a 56% increase in the number of simulated storms with maximum winds greater than 30 m s^–1 (alternatively, a 26% increase in the number of storms with central pressures less than 970 hPa). In addition, there is an increase in the number of intense storms simulated south of 30°S. This increase in simulated maximum storm intensity is consistent with previous studies of the impact of climate change on tropical cyclone wind speeds.     

Assessing climate change impacts on the Iberian power system using a coupled water-power model

Climate change is expected to have a negative impact on the power system of the Iberian Peninsula; changes in river runoff are expected to reduce hydropower generation, while higher temperatures are expected to increase summer electricity demand, when water resources are already limited. However, these impacts have not yet been evaluated at the peninsular level. We coupled a hydrological model with a power market model to study three impacts of climate change on the current Iberian power system: changes in hydropower production caused by changes in precipitation and temperature, changes in temporal patterns of electricity demand caused by temperature changes, and changes in irrigation water use caused by temperature and precipitation changes. A stochastic dynamic programming approach was used to develop operating rules for the integrated system given hydrological uncertainty. We found that changes in precipitation will reduce runoff, decrease hydropower production (with accompanying increases in thermal generation), and increase irrigation water use, while higher temperatures will shift power demand from winter to summer months. The combined impact of these effects will generally make it more challenging to balance agricultural, power, and environmental objectives in the operation of Iberian reservoirs, though some impacts could be mitigated by better alignment between temporal patterns of irrigation and power demands.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: baseline climate

Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

Assessing the socioeconomic consequences of climate change on water resources

When dealing with water resources, traditionally three types of information were needed: the future availability of water, the future demand for water, and the consequences that both of these have on the environment. Introducing the possibility of anthropogenic climate change expands the information requirement to include the effects of climate change on the availability of water. The paper reviews the current state of knowledge about these effects and concludes that, given the large uncertainties involved in the availability of water and the even larger uncertainties involved in forecasting socioeconomic demands for water coupled with the relatively short time horizons involved in economically efficient water investments, we cannot predict significant problems for U.S. water resources over the next few decades that are likely to be attributable to the effects of climate change.Presented at Workshop on Integrated Assessments of the Impacts of Climate Change on Natural Resources, San Diego, February 28–March 4, 1993.     

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.     

The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model

Assessments of the impacts of uncertainties in parameters on mean climate and climate change in complex climate models have, to date, largely focussed on perturbations to parameters in the atmosphere component of the model. Here we expand on a previously published study which found the global impacts of perturbed ocean parameters on the rate of transient climate change to be small compared to perturbed atmosphere parameters. By separating the climate-change-induced ocean vertical heat transport in each perturbed member into components associated with the resolved flow and each parameterisation scheme, we show that variations in global mean heat uptake in different perturbed versions are an order of magnitude smaller than the average heat uptake. The lack of impact of the perturbations is attributed to (1) the relatively small impact of the perturbation on the direct vertical heat transport associated with the perturbed process and (2) a compensation between those direct changes and indirect changes in heat transport from other processes. Interactions between processes and changes appear to combine in complex ways to limit ensemble spread and uncertainty in the rate of warming. We also investigate regional impacts of the perturbations that may be important for climate change predictions. We find variations across the ensemble that are significant when measured against natural variability. In terms of the experimental set-up used here (models without flux adjustments) we conclude that perturbed physics ensembles with ocean parameter perturbations are an important component of any probabilistic estimate of future climate change, despite the low spread in global mean quantities. Hence, careful consideration should be given to assessing uncertainty in ocean processes in future probabilistic assessments of regional climate change.     

Assessing future climate changes and extreme indicators in east and south Asia using the RegCM4 regional climate model

This paper assesses future climate changes over East and South Asia using a regional climate model (RegCM4) with a 50?km spatial resolution. To evaluate the model performance, RegCM4 is driven with ??perfect boundary forcing?? from the reanalysis data during 1970?C1999 to simulate the present day climate. The model performs well in reproducing not only the mean climate and seasonality but also most of the chosen indicators of climate extremes. Future climate changes are evaluated based on two experiments driven with boundary forcing from the European-Hamburg general climate model (ECHAM5), one for the present (1970?C1999) and one for the SRES A1B future scenario (2070?C2099). The model predicts an annual temperature increase of about 3°?C5° (smaller over the ocean and larger over the land), and an increase of annual precipitation over most of China north of 30°N and a decrease or little change in the rest of China, India and Indochina. For temperature-related extreme indicators in the future, the model predicts a generally longer growing season, more hot days in summer, and less frost days in winter. For precipitation-related extremes, the number of days with more than 10?mm of rainfall is predicted to increase north of 30°N and decrease in the south, and the maximum five-day rainfall amount and daily intensity will increase across the whole model domain. In addition, the maximum number of consecutive dry days is predicted to increase over most of the model domain, south of 40°N. Most of the Yangtze River Basin in China stands out as ??hotspots?? of extreme precipitation changes, with the strongest increases of daily rain intensity, maximum five-day rain amount, and the number of consecutive dry days, suggesting increased risks of both floods and droughts.     

Towards quantifying uncertainty in transient climate change

Ensembles of coupled atmosphere–ocean global circulation model simulations are required to make probabilistic predictions of future climate change. “Perturbed physics” ensembles provide a new approach in which modelling uncertainties are sampled systematically by perturbing uncertain parameters. The aim is to provide a basis for probabilistic predictions in which the impact of prior assumptions and observational constraints can be clearly distinguished. Here we report on the first perturbed physics coupled atmosphere–ocean model ensemble in which poorly constrained atmosphere, land and sea-ice component parameters are varied in the third version of the Hadley Centre model (the variation of ocean parameters will be the subject of future study). Flux adjustments are employed, both to reduce regional sea surface temperature (SST) and salinity biases and also to admit the use of combinations of model parameter values which give non-zero values for the global radiation balance. This improves the extent to which the ensemble provides a credible basis for the quantification of uncertainties in climate change, especially at a regional level. However, this particular implementation of flux-adjustments leads to a weakening of the Atlantic overturning circulation, resulting in the development of biases in SST and sea ice in the North Atlantic and Arctic Oceans. Nevertheless, model versions are produced which are of similar quality to the unperturbed and un-flux-adjusted version. The ensemble is used to simulate pre-industrial conditions and a simple scenario of a 1% per year compounded increase in CO₂. The range of transient climate response (the 20 year averaged global warming at the time of CO₂ doubling) is 1.5–2.6°C, similar to that found in multi-model studies. Measures of global and large scale climate change from the coupled models show simple relationships with associated measures computed from atmosphere-mixed-layer-ocean climate change experiments, suggesting that recent advances in computing the probability density function of climate change under equilibrium conditions using the perturbed physics approach may be extended to the transient case.     

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.     

Assessing climate change impacts in the European north

Global climate change and its regional manifestation will result in significant impacts in the European North. However, in order to determine the consequences of such impacts, a holistic, integrated assessment is needed. This paper sets the stage for the remainder of this volume by describing an attempt to derive such an assessment for the Barents Sea Region through the EU-funded BALANCE project. The paper explains some of the major methodologies employed in the study. It also provides insight into major results obtained and attempts to answer a number of overarching questions. It will be shown that climate change does present a significant threat to environmental and societal integrity in the study region. However, it will also be shown that stakeholders regard other drivers of future changes (economical, political developments) at least as equally important for their personal lives.     

Considering attitudinal uncertainty in the climate change skepticism continuum

In this paper we present an empirically driven language to discuss climate change skepticism. We conceptualize skeptic/skepticism as an umbrella term that includes those who actively reject climate science and those who are uncertain about climate change. We propose four categories for better empirical analysis of climate skepticism: epistemic deniers, epistemic doubters (borrowing from Capstick and Pidgeon 2014), attribution deniers, and attribution doubters (borrowing from Rahmstorf 2004). Using a unique dataset of surveys (n = 1000) and interviews (n = 33) with residents of the U.S. Pacific Northwest who are skeptical about climate change, we compare those four groups across several predictors and demographic variables (age, race, gender, political ideology, religiosity, income, education, and level of trust in science) and outcome variables (environmental concern, policy support, and conspiracy ideation (adherence to the belief that climate change is a “hoax”). We demonstrate the importance of considering attitudinal uncertainty in the analysis of climate skepticism by providing evidence for the presence of a continuum of thought wherein epistemic deniers and attribution doubters make up the two ends of a continuum with more complicated distinctions between epistemic doubters and attribution deniers.     

Assessing the impact of weather and climate in Australia

Weather and climate have a significant impact on the Australian society and environment. While the impacts of major changes in the Australian climate likely to occur from the enhanced greenhouse effect have been reported in the literature fewer studies have addressed the impact of the climate's natural variability. Ways in which weather and climate and its variability impact on the decision making process are described and methods available to assess the sensitivity of the economy and the natural environment are also presented. Methods which are being developed to determine the value of weather and climate information to decision makers are briefly described. The El Niño Southern Oscillation is a strong contributor to the variability of the Australian climate. It is used to illustrate the impact of climate variability on both the economy and natural environment of Australia. Finally, the extent to which there is political awareness of the impact of climate variability on the Australian society and environment is briefly examined.     

Indexes of leading climate indicators for impact assessment

Could users of climate information for impact assessment be overlooking an important source of information in climate indicators? We argue that indexes of leading climate indicators of impacts may be usable knowledge for consumers and may provide guidance to the global climate observing community concerning the types of data and information that users need. Five classes of indexes are suggested: Climate Extremes Index (CEI) and Greenhouse Climate Response Index (GCRI) - such are already available from scientists at the U.S. National Climatic Data Center -plus proposed indexes of Hazard Warning, Ecosystem Health, and Energy Demand and Renewable Natural Resources. We conclude that the CEI and GCRI possess several necessary attributes to become usable knowledge; the other indexes have the potential to become usable knowledge, but remain to be implemented with climate data and fully evaluated.