Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

An EPIC model-based wheat drought risk assessment using new climate scenarios in China

There is considerable research interest in future agro-drought risk assessment, since the increasing severity of climate change-related hazards poses a great threat to global food security. Wheat is the most important staple crop in the world, and China’s wheat production has long been impacted by drought. The frequency, intensity, and duration of droughts may increase due to climate change and stressing the need for robust assessment methods for drought risk, as well as adaptation and mitigation strategies. This paper investigates a method for assessing future wheat drought risk using climate scenarios and a crop model. We illustrate the utility of such an approach by assessing the risk of wheat drought under climate change scenarios in China using the Environmental Policy Integrated Climate model. Results show that the risk level of wheat drought is highest under scenario RCP8.5, followed by RCP4.5, RCP6.0, and RCP2.6, in descending order. If current climate change trends continue, wheat drought risk in China will be at risk levels between RCP6.0 and RCP8.5 by the end of the twenty-first century. The wheat drought risk assessment shows a “low-risk, high-risk, low-risk” spatial pattern starting in the spring wheat-planting regions in northern China and progressing to the winter wheat-planting regions in southern China. Significant differences were observed across regions, but in all RCP scenarios, the relative high-risk zones are the Huang-Huai Winter Wheat Region and the North Winter Wheat Region. In addition, wheat drought risk mitigation and adaptation strategies in China are proposed.     

Spatial and temporal drought projections of northwestern Turkey

Theoretical and Applied Climatology - Based on the meteorological data of 60 stations above the Bengbu Sluice of Huaihe River from 1961 to 2015, Crop Water Deficit Index (CWDI) and Relative...     

Future projections of temperature-related climate change impacts on the railway network of Great Britain

Great Britain’s main line railway network is known to experience various temperature-related impacts, e.g. track buckling and overhead power line sag at high ambient temperatures. Climate change could alter the frequency of occurrence of these impacts. We have therefore investigated the climate change impact on various temperature-related issues, identified during workshops with rail industry specialists, using a perturbed physics ensemble (PPE) of the Met Office’s regional climate model (RCM), HadRM3. We have developed novel approaches to combine RCM data with railway industry knowledge, typically by identifying key meteorological thresholds of interest and analysing exceedance of these out to the 2040s. We performed a statistical analysis of the projected changes for each issue, via bootstrapping of the unperturbed PPE member. Although neither the PPE nor the bootstrapping analysis samples the full range of uncertainty in the projections, they nonetheless provide complementary perspectives on the suitability of the projections for use in decision-making. Our main findings include projected increases in the summertime occurrence of temperature conditions associated with (i) track buckling, (ii) overhead power line sag, (iii) exposure of outdoor workers to heat stress, and (iv) heat-related delays to track maintenance; and (v) projected decreases in the wintertime occurrence of temperatures conditions associated with freight train failure owing to brake problems. For (i), the statistical significance varied with track condition and location; for (ii) and (iii), with location; and for (iv) and (v), projected changes were significant across Great Britain. As well as assessing the changes in climate-related hazard, information about the vulnerability of the network to past temperature-related incidents has been summarised. Combining the hazard and vulnerability elements will eventually support a climate risk assessment for the industry.     

A method for regional climate change detection using smooth temporal patterns

This paper introduces an original method for climate change detection, called temporal optimal detection method. The method consists in searching for a smooth temporal pattern in the observations. This pattern can be either the response of the climate system to a specific forcing or to a combination of forcings. Many characteristics of this new method are different from those of the classical “optimal fingerprint” method. It allows to infer the spatial distribution of the detected signal, without providing any spatial guess pattern. The spatial properties of the internal climate variability doesn’t need to be estimated either. The estimation of such quantities being very challenging at regional scale, the proposed method is particularly well-suited for such scale. The efficiency of the method is illustrated by applying it on real homogenized datasets of temperatures and precipitation over France. A multimodel detection is performed in both cases, using an ensemble of atmosphere-ocean general circulation models for estimating the temporal patterns. Regarding temperatures, new results are highlighted, especially by showing that a change is detected even after removing the uniform part of the warming. The sensitivity of the method is discussed in this case, relatively to the computation of the temporal patterns and to the choice of the model. The method also allows to detect a climate change signal in precipitation. This change impacts the spatial distribution of the precipitation more than the mean over the domain. The ability of the method to provide an estimate of the spatial distribution of the change following the prescribed temporal patterns is also illustrated.     

Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971–2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015–2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

     

Assessing spatial associations between perceptions of landscape value and climate change risk for use in climate change planning

This study examines spatially referenced perceived landscape values and climate change risks collected through public participation geographic information systems for potential use in climate change planning. Using survey data from the Southern Fleurieu Peninsula, South Australia, we present a method for identifying perceived landscape values and climate change risks to describe and quantify their spatial associations. Two spatial data models??vector and raster??and two analytical methods??Jaccard coefficients and spatial cross-correlations were used to describe the spatial associations. Results indicate that perceptions of climate change risk are driven, in part, by the values people assign or hold for places on the landscape. Biodiversity and intrinsic landscape values have strong spatial association with biodiversity loss risk while recreation values have strong spatial association with riparian flooding, sea-level rise and wave action risks. Other landscape values show weak to no spatial association with perceived climate change risks. The methodology described in this research provides a mechanism for government agencies to develop place-based adaptation strategies based on these associations.     

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.     

气候变化影响与风险评估方法的研究进展

气候变化影响是指气候变化背景下社会经济或资源环境的响应。气候变化风险是指由于气候变化所引起的社会经济或资源环境的可能损失。气候变化风险评估是对气候变化影响的定性和对风险的量化。针对气候变化风险评估方法的原理和技术体系,本文从风险指数、风险概率和脆弱性评估三个方面,对研究现状、热点问题和通常方法进行了评述,并对当前研究中存在的问题和未来需求进行了归纳和展望。     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

The impact of spatial resolution on area burned and fire occurrence projections in Portugal under climate change

In this study, we investigated the impact of future climate change on fire activity in 12 districts across Portugal. Using historical relationships and the HIRHAM (High Resolution Hamburg Model) 12 and 25 km climate simulations, we assessed the fire weather and subsequent fire activity under a 2 × CO₂ scenario. We found that the fire activity prediction was not affected by the spatial resolution of the climate model used (12 vs. 25 km). Future area burned is predicted to increase 478% for Portugal as a whole, which equates to an increase from 1.4% to 7.8% of the available burnable area burning annually. Fire occurrence will also see a dramatic increase (279%) for all of Portugal. There is significant spatial variation within these results; the north and central districts of the country generally will see larger increases in fire activity.     

Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.     

Climate change in Central America and Mexico: regional climate model validation and climate change projections

Central America has high biodiversity, it harbors high-value ecosystems and it??s important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it??s unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Ni?o events in recent decades that adversely affected species in the region.     

A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1

In order to investigate Last Glacial Maximum and future climate, we “precalibrate” the intermediate complexity model GENIE-1 by applying a rejection sampling approach to deterministic emulations of the model. We develop ~1,000 parameter sets which reproduce the main features of modern climate, but not precise observations. This allows a wide range of large-scale feedback response strengths which generally encompass the range of GCM behaviour. We build a deterministic emulator of climate sensitivity and quantify the contributions of atmospheric (±0.93°C, 1σ) vegetation (±0.32°C), ocean (±0.24°C) and sea–ice (±0.14°C) parameterisations to the total uncertainty. We then perform an LGM-constrained Bayesian calibration, incorporating data-driven priors and formally accounting for structural error. We estimate climate sensitivity as likely (66% confidence) to lie in the range 2.6–4.4°C, with a peak probability at 3.6°C. We estimate LGM cooling likely to lie in the range 5.3–7.5°C, with a peak probability at 6.2°C. In addition to estimates of global temperature change, we apply our ensembles to derive LGM and 2xCO₂ probability distributions for land carbon storage, Atlantic overturning and sea–ice coverage. Notably, under 2xCO₂ we calculate a probability of 37% that equilibrium terrestrial carbon storage is reduced from modern values, so the land sink has become a net source of atmospheric CO₂.     

Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia

A comparison of two approaches for determining probabilistic climate change impacts is presented. In the first approach, ensemble climate projections are applied directly as inputs to an impact model and the risk of impact is computed from the resulting ensemble of outcomes. As this can involve large numbers of projections, the approach may prove to be impractical when applied to complex impact models with demanding input requirements. The second approach is to construct an impact response surface based on a sensitivity analysis of the impact model with respect to changes in key climatic variables, and then to superimpose probabilistic projections of future climate onto the response surface to assess the risk of impact. To illustrate this comparison, an impact model describing the spatial distribution of palsas in Fennoscandia was applied to estimate the risk of palsa disappearance. Palsas are northern mire complexes with permanently frozen peat hummocks, located at the outer limit of the permafrost zone and susceptible to rapid decline due to regional warming. Probabilities of climate changes were derived from an ensemble of coupled atmosphere–ocean general circulation model (AOGCM) projections using a re-sampling method. Results indicated that the response surface approach, though introducing additional uncertainty, gave risk estimates of area decline for palsa suitability that were comparable to those obtained using multiple simulations with the original palsa model. It was estimated as very likely (>90% probability) that a decline of area suitable for palsas to less than half of the baseline distribution will occur by the 2030s and likely (>66%) that all suitable areas will disappear by the end of the twenty-first century under scenarios of medium (A1B) and moderately high (A2) emissions. For a low emissions (B1) scenario, it was more likely than not (>50%) that conditions over a small fraction of the current palsa distribution would remain suitable until the end of the twenty-first century.     

基于SPI指数的当前及未来中国东北地区干旱时空演变特征分析

基于降水量历史观测数据和气候模式预估数据,采用标准化降水量指数(Standandized Precipitation Index,SPI)识别干旱事件,从干旱发生的频率和强度特征分析其危险性,研究东北地区当前及未来不同气候变化情景下干旱时空变化特征.结果显示:(1)bcc-csm1-1 对东北地区降水的模拟效果较好;(...     

The impact of climate change on archaeological resources in Britain: a catchment scale assessment

This paper illustrates the potential impact of future climate change on the archaeological resource of river catchments, specifically in Britain, but with reference to other examples across the globe, when considering issues of generic applicability. It highlights an area of the environmental record often neglected by policy makers and environmental planners when considering the impact of climate change; where cultural heritage has been considered in the past, an emphasis has been placed on the historic built environment and major monuments. Through studying the recent past, particularly the last 1,000 years, geomorphologists and geoarchaeologists can add much empirical data to these debates concerning system response. In addition to the impact of the changing intensity and pattern of natural geomorphic processes, human response to climate change ranging from new farming practices through to the implementation of mitigation strategies to minimise the effects of increased flood frequency and magnitude could be equally as damaging to the archaeological record if not managed through informed decision making.     

Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic Eta regional climate model at 5-km resolution

Theoretical and Applied Climatology - The objective of this work is to assess changes in three metropolitan regions of Southeast Brazil (Rio de Janeiro, São Paulo, and Santos) based on the...     

Assessing future vulnerability and risk of humanitarian crises using climate change and population projections within the INFORM framework

INFORM Risk Index is a global indicator-based disaster risk assessment tool that combines hazards, exposure, vulnerability and lack of coping capacity indicators with the purpose to support humanitarian crisis management decisions considering the current climate and population. In this exploratory study, we extend the Index to include future climate change and population projections using RCP 8.5 climate projections of coastal flood, river flood and drought, and SSP3 and SSP5 population projections for the period 2036 to 2065. For the three hazards considered, annually 1.3 billion people (150% increase), 1.8 billion people (249% increase) and 1.5 billion people (197% increase) in the mid-21st century are projected to be exposed under the 2015, SSP3 and SSP5 population estimates, respectively. Drought shows the highest exposure levels followed by river flood and then coastal flood, with some regional differences. The largest exposed population is projected in Asia, while the largest percent changes are projected in Africa and Oceania. Countries with largest current and projected risk including non-climatic factors are generally located in Africa, West and South Asia and Central America. An uncertainty analysis of the extended index shows that it is generally robust and not influenced by the methodological choices. The projected changes in risk and coping capacity (vulnerability) due to climate change are generally greater than those associated with population changes. Countries in Europe, Western and Northern Asia and Africa tend to show higher reduction levels in vulnerability (lack of coping capacity) required to nullify the adverse impacts of the projected amplified hazards and exposure. The required increase in coping capacity (decreased vulnerability) can inform decision-making processes on disaster risk reduction and adaptation options to maintain manageable risk levels at global and national scale. Overall, the extended INFORM Risk Index is a means to integrate Disaster Risk Reduction and Climate Change Adaptation policy agendas to create conditions for greater policy impact, more efficient use of resources and more effective action in protecting life, livelihoods and valuable assets.