Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Dynamics of the coupled human–climate system resulting from closed-loop control of solar geoengineering

If solar radiation management (SRM) were ever implemented, feedback of the observed climate state might be used to adjust the radiative forcing of SRM in order to compensate for uncertainty in either the forcing or the climate response. Feedback might also compensate for unexpected changes in the system, e.g. a nonlinear change in climate sensitivity. However, in addition to the intended response to greenhouse-gas induced changes, the use of feedback would also result in a geoengineering response to natural climate variability. We use a box-diffusion dynamic model of the climate system to understand how changing the properties of the feedback control affect the emergent dynamics of this coupled human–climate system, and evaluate these predictions using the HadCM3L general circulation model. In particular, some amplification of natural variability is unavoidable; any time delay (e.g., to average out natural variability, or due to decision-making) exacerbates this amplification, with oscillatory behavior possible if there is a desire for rapid correction (high feedback gain). This is a challenge for policy as a delayed response is needed for decision making. Conversely, the need for feedback to compensate for uncertainty, combined with a desire to avoid excessive amplification of natural variability, results in a limit on how rapidly SRM could respond to changes in the observed state of the climate system.     

Climate sensitivity estimated from ensemble simulations of glacial climate

The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO₂ concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO₂, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.     

On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO₂) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO₂ radiative forcing. By considering tropospheric adjustments to CO₂ as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO₂ is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO₂), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.     

Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles

Ensembles of climate model simulations are required for input into probabilistic assessments of the risk of future climate change in which uncertainties are quantified. Here we document and compare aspects of climate model ensembles from the multi-model archive and from perturbed physics ensembles generated using the third version of the Hadley Centre climate model (HadCM3). Model-error characteristics derived from time-averaged two-dimensional fields of observed climate variables indicate that the perturbed physics approach is capable of sampling a relatively wide range of different mean climate states, consistent with simple estimates of observational uncertainty and comparable to the range of mean states sampled by the multi-model ensemble. The perturbed physics approach is also capable of sampling a relatively wide range of climate forcings and climate feedbacks under enhanced levels of greenhouse gases, again comparable with the multi-model ensemble. By examining correlations between global time-averaged measures of model error and global measures of climate change feedback strengths, we conclude that there are no simple emergent relationships between climate model errors and the magnitude of future global temperature change. Algorithms for quantifying uncertainty require the use of complex multivariate metrics for constraining projections.     

IPCC第六次气候变化评估中的气候约束预估方法

得益于第五次评估报告（AR5）以来约束预估研究的迅速发展,观测约束成为政府间气候变化专门委员会（IPCC）第一工作组（WGI）第六次评估报告（AR6）提升对未来预估约束的证据链中的重要一环。IPCC第一工作组第六次评估报告首次利用包括根据历史模拟温度升高幅度得到的观测约束、多模式预估以及第六次评估报告中更新的气候敏感度在内的多条证据链来约束全球地表温度未来变化的预估,减小了多模式预估的不确定性。文中回顾并介绍了IPCC第一工作组第六次评估报告中涉及的几种主要观测约束方法（多模式加权方法、基于归因结论的约束方法（ASK方法）、萌现约束方法）及其应用情况。在IPCC第一工作组第六次评估报告以及很多针对不同区域不同变量的预估研究中,观测约束方法均显示出了订正模式误差、改善模式预估的潜力。相比而言,目前中国在观测约束预估方面的研究还不多,亟待加强观测约束方法研究以及在中国区域气候变化预估中的应用,为中国应对气候变化的政策制定和适应规划提供更丰富、不确定性更小的未来气候信息。      

Uncertainties linked to land-surface processes in climate change simulations

 The impact of climate change on the hydrology of continental surfaces is critical for human activities but the response of the surface to this perturbation may also affect the sensitivity of the climate. This complex feedback is simulated in general circulation models (GCMs) used for climate change predictions by their land-surface schemes. The present study attempts to quantify the uncertainty associated with these schemes and what impact it has on our confidence in the simulated climate anomalies. Four GCMs, each coupled to two different land-surface schemes, are used to explore the spectrum of uncertainties. It is shown that, in this sample, surface processes have a significant contribution to our ability to predict surface temperature changes and perturbations of the hydrological cycle in an environment with doubled greenhouse gas concentration. The results reveal that the uncertainty introduced by land-surface processes in the simulated climate is different from its impact on the sensitivity of GCMs to climate change, indeed an alteration of the surface parametrization with little impact on model climate can affect sensitivity significantly. This result leads us to believe that the validation of land-surface schemes should not be limited to the current climate but should also cover their sensitivity to variations in climatic forcing. Received: 24 June 1999 / Accepted: 20 April 2000     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

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.     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

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.     

大气水汽变化及其反馈效应研究进展

大气水汽变化的反馈作用是影响平衡气候系统敏感性的最大反馈作用之一,能够放大其他温室气体增暖的效应,并可能导致极端天气气候事件的发生趋多趋强。因此,全面分析大气水汽的时空分布特征及其长期变化趋势,评估大气水汽反馈的区域气候效应,对于我们深入认识和理解全球变暖背景下区域气候响应的机理具有重要意义。综合国内外最新研究,已基本能够确定水汽反馈效应为一种使得全球增暖加快近一倍的强烈正反馈,并已能够估计其大致变化范围,但是此估计仍存在较大不确定性。随着卫星和探空技术的发展,目前已有的长期水汽资料日趋丰富,但资料之间也存在一些不确定性问题,同时单个资料本身也存在非均一性问题。最新的气候系统模式已能够大致模拟大气水汽的反馈效应,但近年的进展速度却并不令人乐观。我国的水汽观测和水汽反馈效应的研究也已取得长足进步,可以基本确定为水汽变化与地面温度存在正反馈关系,而与降水的关系虽然也较为密切,但因区域气候变化仍存在较大的不一致性。     

Contributions of carbon cycle uncertainty to future climate projection spread

We have characterized the relative contributions to uncertainty in predictions of global warming amount by year 2100 in the C4MIP model ensemble ( Friedlingstein et al., 2006 ) due to both carbon cycle process uncertainty and uncertainty in the physical climate properties of the Earth system. We find carbon cycle uncertainty to be important. On average the spread in transient climate response is around 40% of that due to the more frequently debated uncertainties in equilibrium climate sensitivity and global heat capacity.
This result is derived by characterizing the influence of different parameters in a global climate-carbon cycle 'box' model that has been calibrated against the 11 General Circulation models (GCMs) and Earth system Models of Intermediate Complexity (EMICs) in the C4MIP ensemble; a collection of current state-of-the-art climate models that include an explicit representation of the global carbon cycle.     

Evaluating the US Mid-Century Strategy for Deep Decarbonization amidst early century uncertainty

The recent change in US presidential administrations has introduced significant uncertainty about both domestic and international policy support for continued reductions in GHG emissions. This brief analysis estimates the potential climate ramifications of changing US leadership, contrasting the Mid-Century Strategy for Deep Decarbonization (MCS) released under the Obama Administration, with campaign statements, early executive actions, and prevailing market conditions to estimate potential emission pathways under the Trump Administration. The analysis highlights areas where GHG reductions are less robust to changing policy conditions, and offers brief recommendations for addressing emissions in the interim. It specifically finds that continued reductions in the electricity sector are less vulnerable to changes in federal policy than those in the built environment and land use sectors. Given the long-lived nature of investments in these latter two sectors, however, opportunities for near-term climate action by willing cities, states, private landowners, and non-profit organizations warrant renewed attention in this time of climate uncertainty.

Key policy insights

• The recent US presidential election has already impacted mitigation goals and practices, injecting considerable uncertainty into domestic and international efforts to address climate change.

• A strategic assessment issued in the final days of the Obama Administration for how to reach long-term climate mitigation objectives provides a baseline from which to gauge potential changes under the Trump Administration.

• Though market trends may continue to foster emission declines in the energy sector, emission reductions in the land use sector and the built environment are subject to considerable uncertainty.

• Regardless of actions to scale back climate mitigation efforts, US emissions are likely to be flat in the coming years. Assuming that emissions remain constant under President Trump and that reductions resume afterwards to meet the Obama Administration mid-century targets in 2050, this near-term pause in reductions yields a difference in total emissions equivalent to 0.3–0.6 years of additional global greenhouse gas emissions, depending on the number of terms served by a Trump Administration.

     

Evaluation of uncertainties in the CRCM-simulated North American climate

This work is a first step in the analysis of uncertainty sources in the RCM-simulated climate over North America. Three main sets of sensitivity studies were carried out: the first estimates the magnitude of internal variability, which is needed to evaluate the significance of changes in the simulated climate induced by any model modification. The second is devoted to the role of CRCM configuration as a source of uncertainty, in particular the sensitivity to nesting technique, domain size, and driving reanalysis. The third study aims to assess the relative importance of the previously estimated sensitivities by performing two additional sensitivity experiments: one, in which the reanalysis driving data is replaced by data generated by the second generation Coupled Global Climate Model (CGCM2), and another, in which a different CRCM version is used. Results show that the internal variability, triggered by differences in initial conditions, is much smaller than the sensitivity to any other source. Results also show that levels of uncertainty originating from liberty of choices in the definition of configuration parameters are comparable among themselves and are smaller than those due to the choice of CGCM or CRCM version used. These results suggest that uncertainty originated by the CRCM configuration latitude (freedom of choice among domain sizes, nesting techniques and reanalysis dataset), although important, does not seem to be a major obstacle to climate downscaling. Finally, with the aim of evaluating the combined effect of the different uncertainties, the ensemble spread is estimated for a subset of the analysed simulations. Results show that downscaled surface temperature is in general more uncertain in the northern regions, while precipitation is more uncertain in the central and eastern US.     

Using climate impacts indicators to evaluate climate model ensembles: temperature suitability of premium winegrape cultivation in the United States

We explore the potential to improve understanding of the climate system by directly targeting climate model analyses at specific indicators of climate change impact. Using the temperature suitability of premium winegrape cultivation as a climate impacts indicator, we quantify the inter- and intra-ensemble spread in three climate model ensembles: a physically uniform multi-member ensemble consisting of the RegCM3 high-resolution climate model nested within the NCAR CCSM3 global climate model; the multi-model NARCCAP ensemble consisting of single realizations of multiple high-resolution climate models nested within multiple global climate models; and the multi-model CMIP3 ensemble consisting of realizations of multiple global climate models. We find that the temperature suitability for premium winegrape cultivation is substantially reduced throughout the high-value growing areas of California and the Columbia Valley region (eastern Oregon and Washington) in all three ensembles in response to changes in temperature projected for the mid-twenty first century period. The reductions in temperature suitability are driven primarily by projected increases in mean growing season temperature and occurrence of growing season severe hot days. The intra-ensemble spread in the simulated climate change impact is smaller in the single-model ensemble than in the multi-model ensembles, suggesting that the uncertainty arising from internal climate system variability is smaller than the uncertainty arising from climate model formulation. In addition, the intra-ensemble spread is similar in the NARCCAP nested climate model ensemble and the CMIP3 global climate model ensemble, suggesting that the uncertainty arising from the model formulation of fine-scale climate processes is not smaller than the uncertainty arising from the formulation of large-scale climate processes. Correction of climate model biases substantially reduces both the inter- and intra-ensemble spread in projected climate change impact, particularly for the multi-model ensembles, suggesting that—at least for some systems—the projected impacts of climate change could be more robust than the projected climate change. Extension of this impacts-based analysis to a larger suite of impacts indicators will deepen our understanding of future climate change uncertainty by focusing on the climate phenomena that most directly influence natural and human systems.     

Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England

Projections of future climate change are plagued with uncertainties, causing difficulties for planners taking decisions on adaptation measures. This paper presents an assessment framework that allows the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties. The framework is applied to a case study of water resources management in the East of England, more specifically to the Anglian Water Services’ 25 year Water Resource Plan (WRP). The paper presents a local sensitivity analysis (a ‘one-at-a-time’ experiment) of the various elements of the modelling framework (e.g., emissions of greenhouse gases, climate sensitivity and global climate models) in order to determine whether or not a decision to adapt to climate change is sensitive to uncertainty in those elements.Water resources are found to be sensitive to uncertainties in regional climate response (from general circulation models and dynamical downscaling), in climate sensitivity and in climate impacts. Aerosol forcing and greenhouse gas emissions uncertainties are also important, whereas uncertainties from ocean mixing and the carbon cycle are not. Despite these large uncertainties, Anglian Water Services’ WRP remains robust to the climate change uncertainties sampled because of the adaptation options being considered (e.g. extension of water treatment works), because the climate model used for their planning (HadCM3) predicts drier conditions than other models, and because ‘one-at-a-time’ experiments do not sample the combination of different extremes in the uncertainty range of parameters. This research raises the question of how much certainty is required in climate change projections to justify investment in adaptation measures, and whether such certainty can be delivered.     

Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback

Climate sensitivity is an important index that measures the relationship between the increase in greenhouse gases and the magnitude of global warming. Uncertainties in climate change projection and climate modeling are mostly related to the climate sensitivity. The climate sensitivities of coupled climate models determine the magnitudes of the projected global warming. In this paper, the authors thoroughly review the literature on climate sensitivity, and discuss issues related to climate feedback processes and the methods used in estimating the equilibrium climate sensitivity and transient climate response (TCR), including the TCR to cumulative CO₂ emissions. After presenting a summary of the sources that affect the uncertainty of climate sensitivity, the impact of climate sensitivity on climate change projection is discussed by addressing the uncertainties in 2°C warming. Challenges that call for further investigation in the research community, in particular the Chinese community, are discussed.