Choice Of Emission Scenario Research Articles

Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Quantifying the uncertainty in future groundwater recharge simulations from regional climate models

AbstractThis study aims to show how future groundwater recharge (GR) simulations in arid areas respond to uncertainty in climatic parameters—a question, if explored, that bridges a gap in water resources management plans. To this aim, eight regional climate models (RCMs) under two representative concentration pathways (RCP4.5 and RCP8.5) projected four climatic parameters [surface air temperature, precipitation, wind speed, and potential evapotranspiration (PET)] over Qatar during the period of 2071–2100. Using topographic and groundwater data, a physically based water balance model was built to simulate future GR under these 16 scenarios. Results show high uncertainty in climatic parameters. Relative to the reference period (1976–2005), values varied under RCP4.5 (RCP8.5) from +1.8 to +3.4 (+3.8 to +5.6)°C for average temperature, −48% to +15% (−60% to +6%) for annual precipitation, −0.23 to +0.1 (−0.27 to +0.04) m/hour for wind speed, and from −5.7 to +12.8 (+4.3 to +17) mm for annual PET. Uncertainty in climatic parameters caused great uncertainty in future GR estimations. During the late 21st century, GR simulations varied from −67% to +64% with an average value of −20% under RCP4.5, and from −81% to +8% with an average value of −36% under RCP8.5. The greatest uncertainty resulted from the driving model, whereas the choice of emission scenario had a secondary impact. Since GR is a critical component of feeding arid aquifers, the study's findings emphasize the importance of both considering the uncertainty associated with climatic parameters and the regional climatic information chosen.

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes.

Role of emission controls in reducing the 2050 climate change penalty for PM2.5 in China

Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 μg m−3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 μg m−3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions are key to attaining air quality targets and protecting human health in the context of future global climate change.

Evaluating multiple historical climate products in ecological models under current and projected temperatures.

Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time have received less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, USA. We first compare the fit of, and support for, models employing observed temperatures, GHCP temperatures, and GHCP temperatures with an elevation adjustment, finding (1) greater support for, and better fit using, elevation‐adjusted vs. raw temperature models and (2) overall similar fits of elevation‐adjusted models employing temperatures from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation‐adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2°C, finding good agreement among GHCPs though with between‐GHCP differences and variation primarily at middle elevations (~1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models, particularly in mid‐elevation areas where the position of treeline may be changing, suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.

Evolution of Mediterranean Sea water properties under climate change scenarios in the Med-CORDEX ensemble

Twenty-first century projections for the Mediterranean water properties have been analyzed using the largest ensemble of regional climate models (RCMs) available up to now, the Med-CORDEX ensemble. It is comprised by 25 simulations, 10 historical and 15 scenario projections, from which 11 are ocean–atmosphere coupled runs and 4 are ocean forced simulations. Three different emissions scenarios are considered: RCP8.5, RCP4.5 and RCP2.6. All the simulations agree in projecting a warming across the entire Mediterranean basin by the end of the century as a result of the decrease of heat losses to the atmosphere through the sea surface and an increase in the net heat input through the Strait of Gibraltar. The warming will affect the whole water column with higher anomalies in the upper layer. The temperature change projected by the end of the century ranges between 0.81 and 3.71 °C in the upper layer (0–150 m), between 0.82 and 2.97 °C in the intermediate layer (150–600 m) and between 0.15 and 0.18 °C in the deep layer (600 m—bottom). The intensity of the warming is strongly dependent on the choice of emission scenario and, in second order, on the choice of Global Circulation Model (GCM) used to force the RCM. On the other hand, the local structures reproduced by each simulation are mainly determined by the regional model and not by the scenario or the global model. The salinity also increases in all the simulation due to the increase of the freshwater deficit (i.e. the excess of evaporation over precipitation and river runoff) and the related increase in the net salt transport at the Gibraltar Strait. However, in the upper layer this process can be damped or enhanced depending upon the characteristics of the inflowing waters from the Atlantic. This, in turn, depends on the evolution of salinity in the Northeast Atlantic projected by the GCM. Thus a clear zonal gradient is found in most simulations with large positive salinity anomalies in the eastern basin and a freshening of the upper layer of the western basin in most simulations. The salinity changes projected for the whole basin range between 0 and 0.34 psu in the upper layer, between 0.08 and 0.37 psu in the intermediate layer and between − 0.05 and 0.33 in the deep layer. These changes in the temperature and salinity modify in turn the characteristics of the main water masses as the new waters become saltier, warmer and less dense along the twenty-first century. There is a model consensus that the intensity of the deep water formation in the Gulf of Lions is expected to decrease in the future. The rate of decrease remains however very uncertain depending on the scenario and model chosen. At the contrary, there is no model consensus concerning the change in the intensity of the deep water formation in the Adriatic Sea and in the Aegean Sea, although most models also point to a reduction.

Modelling and evaluating the impacts of climate change on three major crops in south-eastern Australia using regional climate model simulations

The use of regional climate models (RCMs) to localise results from coarse-resolution global climate models has recently attracted more interest in agricultural impact studies. Theoretically, it has advantages over global climate models in terms of realisation of future climate projections. However, there are few studies that have used dynamical downscaling results to assess climate change impacts on Australian cropping systems. In this study, we used post-processing bias-corrected climate data from the NSW/ACT Regional Climate Modelling (NARCliM) project to drive the Agricultural Production Systems SIMulator (APSIM) for modelling and evaluating the response of three major crops (wheat, canola and lupin) to projected climate conditions under various farm management practices in south-eastern Australia. Our results showed that historical crop yields from APSIM simulations forced by RCM output tended to underestimate yields from simulations forced by observations due to large biases in the NARCliM simulations. Therefore, bias correction was used to correct the APSIM outputs before conducting future impact analysis. The bias-corrected results showed that ensemble-mean yields based on 12 RCMs were projected to increase over the study area under the A2 emission scenario. However, the magnitude of yield increase depended on the time periods, crop type and location. Multiple linear regression models showed that the changes of radiation, rainfall and temperature and elevated CO2 concentration could explain 60–78% of crop yield changes. It is interesting to note that residue incorporation and nitrogen application had a large effect on percentage yield increases for wheat due to future climate change, but had limited effects on the response of lupin and canola yields. Our study suggests that sufficient bias-correction method is needed when using NARCliM outputs in crop models. Although uncertainties (e.g. the choice of emission scenarios and RCMs) still exist in our study, the results are of central importance in the development of high-yield adaptive strategies for local farmers and policy makers in south-eastern Australia.

Cumulative carbon emissions budgets consistent with 1.5 °C global warming

The Paris Agreement 1 commits ratifying parties to pursue efforts to limit the global temperature increase to 1.5 °C relative to pre-industrial levels. Carbon budgets2–5 consistent with remaining below 1.5 °C warming, reported in the IPCC Fifth Assessment Report (AR5)2,6,8, are directly based on Earth system model (Coupled Model Intercomparison Project Phase 5) 7 responses, which, on average, warm more than observations in response to historical CO2 emissions and other forcings8,9. These models indicate a median remaining budget of 55 PgC (ref. 10 , base period: year 1870) left to emit from January 2016, the equivalent to approximately five years of emissions at the 2015 rate11,12. Here we calculate warming and carbon budgets relative to the decade 2006–2015, which eliminates model–observation differences in the climate–carbon response over the historical period 9 , and increases the median remaining carbon budget to 208 PgC (33–66% range of 130–255 PgC) from January 2016 (with mean warming of 0.89 °C for 2006–2015 relative to 1861–188013–18). There is little sensitivity to the observational data set used to infer warming that has occurred, and no significant dependence on the choice of emissions scenario. Thus, although limiting median projected global warming to below 1.5 °C is undoubtedly challenging19–21, our results indicate it is not impossible, as might be inferred from the IPCC AR5 carbon budgets2,8. Limiting warming to 1.5 °C requires staying within an allowable carbon budget. An analysis of warming and carbon budgets from the past decade shows that the median remaining budget is 208 PgC, corresponding to about 20 years of emissions at the 2015 rate.

Future warming rates over the Hawaiian Islands based on elevation‐dependent scaling factors

ABSTRACTRegional climate change scenarios are important for understanding their impacts on environments of oceanic islands. This study presents a statistical method that estimates elevation‐dependent warming rates for the Hawaiian Islands. The downscaling is facilitated by two important aspects found in the climate change scenarios produced by general circulation models. First, climate model simulations show a strong relationship between height and amplitude of the temperature change over the northern subtropical Pacific. Second, the ratio between the upper air temperature and the surface temperature change is independent of the global warming rate. This information is exploited to produce high‐resolution maps with the expected range of future temperature changes for the Hawaiian Islands by the mid and late 21st century. The expected surface warming and its uncertainty is analysed with an ensemble of global climate scenarios for two Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and 8.5). It is found that the highest mountain elevations warm by a factor 1.5 ± 0.2 compared with the ambient surface temperature anomalies at the sea level. In scenario RCP8.5, high elevations above 3000 m reach up to 4–5 °C warmer temperatures by the late 21st century. The uncertainty due to multi‐model ensemble spread is overall larger than the statistical downscaling uncertainty, except for the highest elevations where both contribute equally to the uncertainty range. For the late 21st century, however, the largest uncertainty stems from the choice of emission scenarios.

The method of producing climate change datasets impacts the resulting policy guidance and chance of mal-adaptation

Impact, adaptation and vulnerability (IAV) research underpin strategies for adaptation to climate change and help to conceptualise what life may look like in decades to come. Research draws on information from global climate models (GCMs) though typically post-processed into a secondary product with finer resolution through methods of downscaling. Through worked examples set in an Australian context we assess the influence of GCM sub-setting, geographic area sub-setting and downscaling method on the regional change signal. Examples demonstrate that choices impact on the final results differently depending on factors such as application needs, range of uncertainty of the projected variable, amplitude of natural variability, and size of study region. For heat extremes, the choice of emissions scenario is of prime importance, but for a given scenario the method of preparing data can affect the magnitude of the projection by a factor of two or more, strongly affecting the indicated adaptation decision. For catchment level runoff projections, the choice of emission scenario is less dominant. Rather the method of selecting and producing application-ready datasets is crucial as demonstrated by results with opposing sign of change, raising the real possibility of mal-adaptive decisions. This work illustrates the potential pitfalls of GCM sub-sampling or the use of a single downscaled product when conducting IAV research. Using the broad range of change from all available model sources, whilst making the application more complex, avoids the larger problem of over-confidence in climate projections and lessens the chance of mal-adaptation.

Future risk of record-breaking summer temperatures and its mitigation

The probability that summer temperatures in the future will exceed the hottest on record during 1920–2014 is projected to increase at all land locations with global warming. Within the BRACE project framework we investigate the sensitivity of this projected change in probability to the choice of emissions scenario using two large ensembles of simulations with the Community Earth System Model. The large ensemble size allows for a robust assessment of the probability of record-breaking temperatures. Globally, the probability that any summer during the period 2061–2081 will be warmer than the hottest on record is 80 % for RCP 8.5 and 41 % for RCP 4.5. Hence, mitigation can reduce the risk of record-breaking temperatures by 39 %. The potential for risk reduction is greatest for some of the most populated regions of the globe. In Europe, for example, a potential risk reduction of over 50 % is projected. Model biases and future changes in temperature variance have only minor effects on the results, as their contribution stays well below 10 % for almost all locations.

Quantifying Sources of Uncertainty in Projections of Future Climate*

Abstract A simple statistical model is used to partition uncertainty from different sources, in projections of future climate from multimodel ensembles. Three major sources of uncertainty are considered: the choice of climate model, the choice of emissions scenario, and the internal variability of the modeled climate system. The relative contributions of these sources are quantified for mid- and late-twenty-first-century climate projections, using data from 23 coupled atmosphere–ocean general circulation models obtained from phase 3 of the Coupled Model Intercomparison Project (CMIP3). Similar investigations have been carried out recently by other authors but within a statistical framework for which the unbalanced nature of the data and the small number (three) of scenarios involved are potentially problematic. Here, a Bayesian analysis is used to overcome these difficulties. Global and regional analyses of surface air temperature and precipitation are performed. It is found that the relative contributions to uncertainty depend on the climate variable considered, as well as the region and time horizon. As expected, the uncertainty due to the choice of emissions scenario becomes more important toward the end of the twenty-first century. However, for midcentury temperature, model internal variability makes a large contribution in high-latitude regions. For midcentury precipitation, model internal variability is even more important and this persists in some regions into the late century. Implications for the design of climate model experiments are discussed.

Hydrological Impacts of Warmer and Wetter Climate in Troutlake and Sturgeon River Basins in Central Canada

The impact of climate change on water availability in two river basins located in central Canada is investigated. Several statistical downscaling methods are used to generate temperature and precipitation scenarios from the third-generation Canadian Coupled General Circulation Model, forced with different emission scenarios. The hydrological model SLURP is used to simulate runoff. All downscaling methods agree that temperature will increase with time and that precipitation will also increase, although there is considerably more uncertainty in the magnitude of precipitation change. The study concludes that the change in total annual precipitation does not necessarily translate into similar changes in runoff. The seasonal distribution of precipitation changes is important for runoff, as is the increase in evapotranspiration. The choice of downscaling method appears to have a greater impact on runoff projections than the choice of emission scenario. Therefore, it is important to consider several downscaling methods when evaluating the impact of climate change on runoff.

Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments

AbstractProjections of discharge are key for future water resources management. These projections are subject to uncertainties, which are difficult to handle in the decision process on adaptation strategies. Uncertainties arise from different sources such as the emission scenarios, the climate models and their postprocessing, the hydrological models, and the natural variability. Here we present a detailed and quantitative uncertainty assessment, based on recent climate scenarios for Switzerland (CH2011 data set) and covering catchments representative for midlatitude alpine areas. This study relies on a particularly wide range of discharge projections resulting from the factorial combination of 3 emission scenarios, 10–20 regional climate models, 2 postprocessing methods, and 3 hydrological models of different complexity. This enabled us to decompose the uncertainty in the ensemble of projections using analyses of variance (ANOVA). We applied the same modeling setup to six catchments to assess the influence of catchment characteristics on the projected streamflow, and focused on changes in the annual discharge cycle. The uncertainties captured by our setup originate mainly from the climate models and natural climate variability, but the choice of emission scenario plays a large role by the end of the 21st century. The contribution of the hydrological models to the projection uncertainty varied strongly with catchment elevation. The discharge changes were compared to the estimated natural decadal variability, which revealed that a climate change signal emerges even under the lowest emission scenario (RCP2.6) by the end of the century. Limiting emissions to RCP2.6 levels would nevertheless reduce the largest regime changes by the end of the century by approximately a factor of two, in comparison to impacts projected for the high emission scenario SRES A2. We finally show that robust regime changes emerge despite the projection uncertainty. These changes are significant and are consistent across a wide range of scenarios and catchments. We propose their identification as a way to aid decision making under uncertainty.

Climatic controls on biophysical interactions in the Black Sea under present day conditions and a potential future (A1B) climate scenario

A dynamical downscaling approach has been applied to investigate climatic controls on biophysical interactions and lower trophic level dynamics in the Black Sea. Simulations were performed under present day conditions (1980–1999) and a potential future (2080–2099) climate scenario, based on the Intergovernmental Panel for Climate Change A1B greenhouse gas emission scenario. Simulations project a 3.7°C increase in SST, a 25% increase in the stability of the seasonal thermocline and a 37day increase in the duration of seasonal stratification. Increased winter temperatures inhibited the formation of Cold Intermediate Layer (CIL) waters resulting in near complete erosion of the CIL, with implications for the ventilation of intermediate water masses and the subduction of riverine nutrients. A 4% increase in nitrate availability within the upper 30m of the water column reflected an increase in the retention time of river water within the surface mixed-layer. Changes in thermohaline structure, combined with a 27% reduction in positive wind stress curl, forced a distinct change in the structure of the basin-scale circulation. The predominantly cyclonic circulation characteristic of contemporary conditions was reversed within the southern and eastern regions of the basin, where under A1B climatic conditions, anticyclonic circulation prevailed. The change in circulation structure significantly altered the horizontal advection and dispersion of high nutrient river waters originating on the NW self. Net primary production increased by 5% on average, with much spatial variability in the response, linked to advective processes. Phytoplankton biomass also increased by 5% and the higher nutrient environment of the future scenario caused a shift in species composition in favour of larger phytoplankton. No significant change in zooplankton biomass was projected. These results constitute one of many possible future scenarios for the Black Sea, being dependent on the modelling systems employed in addition to the choice of emission scenario. Our results emphasise in particular the sensitivity of dynamical downscaling studies to the regional wind forcing fields extracted from global models (these being typically model dependent). As atmospheric warming is projected with a high degree of confidence warming of the Black Sea upper layer, increased water column stability, and erosion of the CIL are believed to be robust results of this study.

Quantification of uncertainty sources in a probabilistic climate change assessment of future water shortages

As the incorporation of probabilistic climate change information into UK water resource management gathers apace, understanding the relative scales of the uncertainty sources in projections of future water shortage metrics is necessary for the resultant information to be understood and used effectively. Utilising modified UKCP09 weather generator data and a multi-model approach, this paper represents a first attempt at extending an uncertainty assessment of future stream flows under forced climates to consider metrics of water shortage based on the triggering of reservoir control curves. It is found that the perturbed physics ensemble uncertainty, which describes climate model parameter error uncertainty, is the cause of a far greater proportion of both the overall flow and water shortage per year probability uncertainty than that caused by SRES emissions scenario choice in the 2080s. The methodology for producing metrics of future water shortage risk from UKCP09 weather generator information described here acts as the basis of a robustness analysis of the North Staffordshire WRZ to climate change, which provides an alternative approach for making decisions despite large uncertainties, which will follow.

An assessment of uncertainties in using volume-area modelling for computing the twenty-first century glacier contribution to sea-level change

Abstract. A large part of present-day sea-level change is formed by the melt of glaciers and ice caps (GIC). This study focuses on the uncertainties in the calculation of the GIC contribution on a century timescale. The model used is based on volume-area scaling, combined with the mass balance sensitivity of the GIC. We assess different aspects that contribute to the uncertainty in the prediction of the contribution of GIC to future sea-level rise, such as (1) the volume-area scaling method (scaling factor), (2) the glacier data, (3) the climate models, and (4) the emission scenario. Additionally, a comparison of the model results to the 20th century GIC contribution is presented. We find that small variations in the scaling factor cause significant variations in the initial volume of the glaciers, but only limited variations in the glacier volume change. If two existing glacier inventories are tuned such that the initial volume is the same, the GIC sea-level contribution over 100 yr differs by 0.027 m or 18 %. It appears that the mass balance sensitivity is also important: variations of 20 % in the mass balance sensitivity have an impact of 17 % on the resulting sea-level projections. Another important factor is the choice of the climate model, as the GIC contribution to sea-level change largely depends on the temperature and precipitation taken from climate models. Connected to this is the choice of emission scenario, used to drive the climate models. Combining all the uncertainties examined in this study leads to a total uncertainty of 0.052 m or 35 % in the GIC contribution to global mean sea level. Reducing the variance in the climate models and improving the glacier inventories will significantly reduce the uncertainty in calculating the GIC contributions, and are therefore crucial actions to improve future sea-level projections.

Uncertainties in downscaled relative humidity for a semi-arid region in India

Monthly scenarios of relative humidity (RH) were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978–2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978–2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for RH due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of RH, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978–2000) was studied using three measures (1) Nash– Sutcliffe error estimate (Ef), (2) mean absolute error (MAE), and (3) product moment correlation (P ). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for Ef, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of RH developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% RH in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001–2100), a variation of 6 to 15% RH was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.

Bioclimate envelope model predictions for natural resource management: dealing with uncertainty

Summary 1. Bioclimate envelope models are widely used to predict the potential distribution of species under climate change, but they are conceptually also suitable to match policies and practices to anticipated or observed climate change, for example through species choice in reforestation. Projections of bioclimate envelope models, however, come with large uncertainties due to different climate change scenarios, modelling methods and other factors. 2 In this paper we present a novel approach to evaluate uncertainty in model‐based recommendations for natural resource management. Rather than evaluating variability in modelling results as a whole, we extract a particular statistic of interest from multiple model runs, e.g. species suitability for a particular reforestation site. Then, this statistic is subjected to analysis of variance, aiming to narrow the range of projections that practitioners need to consider. 3. In four case studies for western Canada we evaluate five sources of uncertainty with two to five treatment levels, including modelling methods, interpolation type for climate data, inclusion of topo‐edaphic variables, choice of general circulation models, and choice of emission scenarios. As dependent variables, we evaluate changes to tree species habitat and ecosystem distributions under 144 treatment combinations. 4. For these case studies, we find that the inclusion of topo‐edaphic variables as predictors reduces projected habitat shifts by a quarter, and general circulation models had major main effects. Our contrasting modelling approaches primarily contributed to uncertainty through interaction terms with climate change predictions, i.e. the methods behaved differently for particular climate change scenarios (e.g. warm & moist scenarios) but similar for others. 5. Synthesis and applications. Partitioning of variance components helps with the interpretation of modelling results and reveals how models can most efficiently be improved. Quantifying variance components for main effects and interactions among sources of uncertainty also offers researchers the opportunity to filter out biologically and statistically unreasonable modelling results, providing practitioners with an improved range of predictions for climate‐informed natural resource management.

Assessing the impact of aviation on climate

We present an assessment of the marginal climate impacts of new aviation activities. We use impulse response functions derived from carbon-cycle and atmospheric models to estimate changes in surface temperature for various aviation impacts (CO 2 , NO X on methane, NO x on ozone, sulfates, soot, and contrails/induced cirrus). We use different damage functions and discount rates to explore health, welfare and ecological costs for a range of assumptions and scenarios. Since uncertainty is high regarding many aviation effects, we explicitly capture some uncertainty by representing several model parameters as probabilistic distributions. The uncertainties are then propagated using Monte Carlo analysis to derive estimates for the impact of these uncertainties on the marginal future climate impacts. Our goal is to provide a framework that will communicate the potential impacts of aviation on climate change under different scenarios and assumptions, and that will allow decision-makers to compare these potential impacts to other aviation environmental impacts. We present results to describe the influence of parametric uncertainties, scenarios, and assumptions for valuation on the expected marginal future costs of aviation impacts. Estimates of the change in global average surface temperature due to aviation are most sensitive to changes in climate sensitivity, the radiative forcing attributed to short-lived effects (in particular those related to contrails and aviation-induced cirrus), and the choice of emissions scenario. Estimates of marginal future costs of aviation are most sensitive to assumptions regarding the discount rate, followed by assumptions regarding climate sensitivity, and the choice of emissions scenario.