Ensemble Of Scenarios Research Articles

Characteristics of Enhanced Heatwaves over Tanzania and Scenario Projection in the 21st Century

Extreme hot temperature is dangerous to the bioeconomy, and would worsen with time. Ambient heatwaves accompanied by unusual droughts are major threats to poverty eradication in Tanzania. Due to sparsity of observation data and proper heatwave detection metrics, there has been a paucity of knowledge about heatwave events in Tanzania. In this study, the Heatwave Magnitude Index daily (HWMId) was adopted to quantitatively analyze heatwave characteristics throughout Tanzania at mid-21st century (2041–2070) and end of 21st century (2071–2100), relative to the reference period (1983–2012) using the CHIRTS-daily quasi-global high-resolution temperature dataset and climate simulations from a multi-modal ensemble of median scenarios (RCP4.5, from CORDEX-Africa). The results showed that moderate to super-extreme heatwaves occurred in Tanzania between 1983 and 2012, particularly in 1999, when ultra-extreme heatwaves (HWMId > 32) occurred in the Lake Victoria basin. It is projected that by mid-21st century, the upper category of HWMId would be hotter and longer, and would occur routinely in Tanzania. The spatial extent of all of the HWMId categories is projected to range from 34% to 73% by the end of the 21st century with a duration of 8 to 35 days, compared to 1 to 5 days during the reference period. These findings will contribute to increasing public awareness of the need for adaptation.

Carbon price dynamics in ambitious climate mitigation scenarios: an analysis based on the IAMC 1.5 °C scenario explorer

We analyse global carbon price trajectories from integrated assessment studies of 2 °C and below-compatible emission pathways based on a new scenario ensemble that has been made publicly available together with other relevant data sets in the IAMC 1.5 °C Scenario Explorer. We complement and extent the findings of the initial study on carbon price variations in integrated assessment models of (Guivarch and Rogelji 2017 Carbon price variations in 2 °C scenarios explored, Carbon Pricing Leadership Coalition) by providing a broader and more robust empirical assessment based on a comprehensive statistical analysis. We discuss the prospects and challenges of in‐depth bivariate econometric analyses of key impact factors in data sets from integrated assessment models. We show that the amount of meta‐information reported for individual models differs significantly across all variables where a large part of all recorded scenario explorer variables can be attributed to only a small number of applied models. We analyse the trend patterns emerging from the analysed global carbon price trajectories based on a statistical trend identification procedure. About half of the analysed carbon price projections seem to be best characterised by long run exponential growth patterns in carbon prices. Moreover, we break down the explanatory contribution of individual components on global carbon prices by the Kaya identity, i.e. global GDP, primary energy intensity and emission intensity. We show that the price of carbon is lower in baseline scenarios with faster economic growth per capita, low‐energy consumption patterns and high potentials for low carbon technologies compared to fossil fuels. In contrast to previous findings, the observed carbon price developments are impacted much more strongly by scenario‐specific than by model‐specific influences. Next to the diagnostic indicators for models, further indicators for the categorization of scenarios describing key context and policy parameterisations applied in individual model runs should be developed and included in descriptions of integrated assessment studies.

Empirical testing of the visualizations of climate change mitigation scenarios with citizens: A comparison among Germany, Poland, and France

While scenarios are used extensively for communication about climate change mitigation, little is known about the interpretation of these scenarios by citizens. We conducted a cross-country empirical evaluation of scenario visualizations for global mitigation, using online surveys in Germany (N = 379), Poland (N = 223), and France (N = 225). Each respondent received visualizations of the required changes in global carbon dioxide emissions and composition of electricity supply (fossil fuels, nuclear, and renewable sources) for limiting global warming to 1.5 °C. We evaluated the effects of respondents’ demographics, prior beliefs, numeracy, and graph literacy on the reading accuracy and knowledge gains from the visualizations. We also included an experimental between-groups design on visualization format, where four groups received different graph formats (steep or gradual graphs with depictions of uncertainty ranges or scenario ensembles) and the fifth group received a table. Results showed that higher education level, numeracy, and graph literacy increased reading accuracy in all countries, while age reduced them. Respondents with prior beliefs about climate change mitigation that matched the information in the visualizations had also higher reading accuracy and knowledge gains. While the effects of different visualization formats were comparatively minor, customizing formats according to demographic and country differences was used to reduce adverse effects from these differences. These results emphasize the need to design visualizations that match characteristics of the intended audience and could inform better communication of climate change mitigation scenarios to non-expert audience.

Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

Energy system developments and investments in the decisive decade for the Paris Agreement goals

The Paris Agreement does not only stipulate to limit the global average temperature increase to well below 2 °C, it also calls for ‘making finance flows consistent with a pathway towards low greenhouse gas emissions’. Consequently, there is an urgent need to understand the implications of climate targets for energy systems and quantify the associated investment requirements in the coming decade. A meaningful analysis must however consider the near-term mitigation requirements to avoid the overshoot of a temperature goal. It must also include the recently observed fast technological progress in key mitigation options. Here, we use a new and unique scenario ensemble that limit peak warming by construction and that stems from seven up-to-date integrated assessment models. This allows us to study the near-term implications of different limits to peak temperature increase under a consistent and up-to-date set of assumptions. We find that ambitious immediate action allows for limiting median warming outcomes to well below 2 °C in all models. By contrast, current nationally determined contributions for 2030 would add around 0.2 °C of peak warming, leading to an unavoidable transgression of 1.5 °C in all models, and 2 °C in some. In contrast to the incremental changes as foreseen by current plans, ambitious peak warming targets require decisive emission cuts until 2030, with the most substantial contribution to decarbonization coming from the power sector. Therefore, investments into low-carbon power generation need to increase beyond current levels to meet the Paris goals, especially for solar and wind technologies and related system enhancements for electricity transmission, distribution and storage. Estimates on absolute investment levels, up-scaling of other low-carbon power generation technologies and investment shares in less ambitious scenarios vary considerably across models. In scenarios limiting peak warming to below 2 °C, while coal is phased out quickly, oil and gas are still being used significantly until 2030, albeit at lower than current levels. This requires continued investments into existing oil and gas infrastructure, but investments into new fields in such scenarios might not be needed. The results show that credible and effective policy action is essential for ensuring efficient allocation of investments aligned with medium-term climate targets.

Climate Change Impacts on the Future of Forests in Great Britain

Forests provide important ecosystem services but are being affected by climate change, not only changes in temperature and precipitation but potentially also directly through the plant-physiological effects of increases in atmospheric CO2. We applied a tree-species-based dynamic model (LPJ-GUESS) at a high 5-km spatial resolution to project climate and CO2 impacts on tree species and thus forests in Great Britain. Climatic inputs consisted of a novel large climate scenario ensemble derived from a regional climate model (RCM) under an RCP 8.5 emission scenario. The climate change impacts were assessed using leaf area index (LAI) and net primary productivity (NPP) for the 2030s and the 2080s compared to baseline (1975–2004). The potential CO2 effects, which are highly uncertain, were examined using a constant CO2 level scenario for comparison. Also, a climate vulnerability index was developed to assess the potential drought impact on modeled tree species. In spite of substantial future reductions in rainfall, the mean projected LAI and NPP generally showed an increase over Britain, with a larger increment in Scotland, northwest England, and west Wales. The CO2 increase led to higher projected LAI and NPP, especially in northern Britain, but with little effect on overall geographical patterns. However, without accounting for plant-physiological effects of elevated CO2, NPP in Southern and Central Britain and easternmost parts of Wales showed a decrease relative to 2011, implying less ecosystem service provisioning, e.g., in terms of timber yields and carbon storage. The projected change of LAI and NPP varied from 5 to 100% of the mean change, due to the uncertainty arising from natural weather-induced variability, with Southeast England being most sensitive to this. It was also the most susceptible to climate change and drought, with reduced suitability for broad-leaved trees such as beech, small-leaved lime, and hornbeam. These could lead to important changes in woodland composition across Great Britain.

How do the properties of training scenarios influence the robustness of reservoir operating policies to climate uncertainty?

Reservoir control policies provide a flexible option to adapt to the uncertain hydrologic impacts of climate change. This challenge requires robust policies capable of navigating scenarios that are wetter, drier, or more variable than anticipated. While a number of prior studies have trained robust policies using large scenario ensembles, there remains a need to understand how the properties of training scenarios impact policy robustness. Specifically, this study investigates scenario properties including annual runoff, snowpack, and baseline regret—the difference between baseline policy and perfect foresight performance in an individual scenario. Results indicate that policies trained to scenario subsets with high baseline regret outperform those generated with other training sets in both wetter and drier futures, largely by adopting an intra-annual hedging strategy. The approach highlights the potential to improve the efficiency and robustness of policy training by considering both the hydrologic properties and baseline regret of the training ensemble.

Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water scarcity. The key objective of this research is to applying different scenarios for GWR potential mapping by means of a classifier ensemble approach, namely a combination of Maximum Entropy (ME) and Frequency Ratio (FR) models in a semi-arid mountainous, Marboreh Watershed of Iran. To consider the ensemble effect of these models, 15 input layers were generated and used in two models and then the models were combined in seven scenarios. According to marginal response curves (MRCs) and the Jackknife technique, quaternary formations (Qft1 and Qft2) of lithology, sandy-clay-loam (Sa. Cl. L) class of soil, 0–4% class of slope, and agriculture & rangeland classes of land use, offered the highest percolation potential. Results of the FR model showed that the highest weight belonged to Qft1 rocks and Sa. Cl. L textures. Seven scenarios were used for GWR potential maps by different ensembles based on basic mathematical operations. Correctly Classified Instances (CCI), and the AUC indices were applied to validate model predictions. The validation indices showed that scenarios 5 had the best performance. The combination of models by different ensemble scenarios enhances the efficiency of these models. This study serves as a basis for future investigations and provides useful information for prediction of sites with groundwater recharge potential through combination of state-of-the-art statistical and machine learning models. The proposed ensemble model reduced the machine learning and statistical models’ limitations gaps and promoted the accuracy of the model where combining, especially for data-scarce areas. The results of present study can be used for the GWR potential mapping, land use planning, and groundwater development plans.

Can we advance individual-level heat-health research through the application of stochastic weather generators?

Individuals living in every region of the world are increasingly vulnerable to negative health outcomes due to extreme heat exposure. Children, in particular, may face long-term consequences associated with heat stress that affect their educational attainment and later life health and well-being. Retrospective individual-level analyses are useful for determining the effects of extreme heat exposure on health outcomes. Typically, future risk is inferred by extrapolating these effects using future warming scenarios that are applied uniformly over space and time without consideration of topographical or climatological gradients. We propose an alternative approach using a stochastic weather generator. This approach employs a 1 °C warming scenario to produce an ensemble of plausible future weather scenarios, and subsequently a distribution of future health risks. We focus on the effect of global warming on fetal development as measured by birth weight in Ethiopia. We demonstrate that predicted changes in birth weight are sensitive to the evolution of temperatures not quantified in a uniform warming scenario. Distributions of predicted changes in birth weight vary in magnitude and variability depending on geographic and socioeconomic region. We present these distributions alongside results from the uniform warming scenario and discuss the spatiotemporal variability of these predicted changes.

Is interactive air sea coupling relevant for simulating the future climate of Europe?

The majority of regional climate change assessments for the Euro-CORDEX region is based on high resolution atmosphere models. These models use prescribed lower boundary conditions, such as sea surface temperatures (SST) from global ocean General Circulation Models (GCMs), that do not respond to changes simulated by the regional atmosphere model, thus lacking an important feedback to the atmosphere. However, research during the past decade indicated that the use of coupled atmosphere–ocean models can lead to significantly altered model solutions compared to standalone atmosphere models for the present day climate imposing some uncertainty on the widely used uncoupled future scenarios. We here present the first multi-model and multi scenario (RCP2.6, RCP4.5, RCP8.5) ensemble of future climate change scenarios downscaled with a coupled atmosphere—ocean model in which sea surface temperature and sea ice fields are explicitly simulated by a coupled state-of-the-art high resolution ocean model and communicated to the atmosphere at 3-hourly time steps. Our ensemble generally confirms results of previous uncoupled ensembles over land areas implying that the coupling effect is restricted mainly to the coupled area and the adjacent coastal zone. By contrast, over the North Sea and Baltic Sea small scale processes point to important coupling effects that mediate the response to climate change and that can not be simulated by uncoupled models. Our results therefore impose general uncertainty on the usage of regional climate change data from uncoupled ensembles over marine areas such as for purposes of offshore wind or mussel farming, the planing of marine protected areas, and marine recreation along the coastal zone. It further sets in question the usage of uncoupled scenario data (such as Euro-CORDEX) to force high resolution ocean models. Comparing coupled and uncoupled hindcast simulations reveals that the coupling effect over land is most pronounced during the warm season when prescribed and modelled sea surface temperatures (SST) differ strongest. In addition, a generally weaker wind regime in summer damps the heat dispersion in the atmosphere so that air temperature anomalies can extent further over land compared to winter. Future projections are discussed under consideration of land-sea warming characteristics for selected climate indices as well as mean seasonal climate change. At the end of the century a clear land-sea pattern is seen in all scenarios with stronger warming over land than over open sea areas. On average land areas warm at a rate 1.5 times faster than areas over the open ocean. Over the coupled area, i.e. the North Sea and Baltic Sea tropical nights are impacted strongest and the Baltic Sea turns out to be a hot spot in future climate. This has been unrecognized in previous studies using high resolution atmosphere models with prescribed SSTs from global models which do not represent small scale ocean processes in the Baltic Sea adequately.

Transparency, trust, and integrated assessment models: An ethical consideration for the Intergovernmental Panel on Climate Change

AbstractFollowing the trenchant criticism of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concerning the lack of transparency in integrated assessment models (IAMs), much attention has been given to addressing this issue in the preparation of the Sixth Assessment Report (AR6). The issue of IAM transparency has been an ongoing concern for approximately two decades regarding the cloaking of value‐laden assumptions and output uncertainties. Due to the opaque nature of IAMs, the credibility of modeling results and the associated policy recommendations are patently limited, with policymakers inevitably having reservations as to the robustness of modeling outcomes given the deficit of information regarding the underlying assumptions. In an attempt to address the issue of a lack of transparency concerning IAM usage by the IPCC, a database containing the climate mitigation scenario ensemble which underpinned the Special Report on Global Warming of 1.5°C was made publicly available, the IAMC 1.5°C Scenario Explorer database. Despite this database, the omission of critical model input data and accompanying supporting documentation from its content fails to fulfill its raison d'être, that is to say, to ensure reproducibility and transparency. If the issue of IAM transparency is not fully addressed in the upcoming AR6 with respect to the provision of IAM input data, accompanied by supporting documentation, then the IPCC will have failed to meet its own declared commitment for this assessment cycle.This article is categorized under: Integrated Assessment of Climate Change > Integrated Assessment Modeling

Models on the wrong track: Model-based electricity supply scenarios in Switzerland are not aligned with the perspectives of energy experts and the public

Model-based scenarios have become the key method to explore uncertainties in the electricity supply transition of many countries. While retrospective scenario studies show that multi-organization, multi-model scenario ensembles increase the diversity of considered uncertainties, it remains unclear whether such ensembles align with the perspectives of transition stakeholders, including the wider public. This study compares a multi-organization, multi-model ensemble of 82 Swiss electricity supply scenarios for 2035 from a review of 19 studies between 2011 and 2018 with preferred scenarios from three samples of stakeholders: citizens from an online survey (N=61), informed citizens from participatory workshops (N=46), and energy experts from another online survey (N=60). The results show that most informed citizens and experts preferred an almost 100% domestic renewable electricity supply in Switzerland in 2035. On the contrary, most model-based scenarios relied significantly on fossil fuel-based generation and net electricity imports. Possible reasons for this misalignment include the lack of broad stakeholder participation in the development of such scenarios and the modeling choices such as cost-optimization models that are known to underrepresent renewable electricity. For both scenario developers and users, this study offers a word of caution that even a rich scenario ensemble could focus on alternatives that are not preferred by stakeholders and that diverse stakeholder and public perspectives can enrich scenario ensembles. For the Swiss electricity supply transition, the results indicate that a large-scale deployment of renewable electricity before 2035 is preferred by the expert and citizen samples and, therefore, such scenarios should be modeled more in the future.

Future Distribution of Suitable Habitat for Pelagic Sharks in Australia Under Climate Change Models

Global oceans are absorbing over 90% of the heat trapped in our atmosphere due to accumulated anthropogenic greenhouse gases, resulting in increasing ocean temperatures. Such changes may influence marine ectotherms, such as sharks, as their body temperature concurrently increases towards their upper thermal limits. Sharks are high trophic level predators that play a key role in the regulation of ecosystem structure and health. Because many sharks are already threatened, it is especially important to understand the impact of climate change on these species. We used shark occurrence records collected by commercial fisheries within the Australian continental Exclusive Economic Zone (EEZ) to predict changes in future (2050 to 2099) relative to current (1956 to 2005) habitat suitability for pelagic sharks based on an ensemble of climate models and emission scenarios. Our predictive models indicate that future sea temperatures are likely to shift the location of suitable shark habitat within the Australian EEZ. On average, suitable habitat is predicted to decrease within the EEZ for requiem and increase for mackerel sharks, however, the direction and severity of change was highly influenced by the choice of climate model. Our results indicate the need to consider climate change scenarios as part of future shark management and suggest that more broad-scale studies are needed for these pelagic species.

Climate change promotes transitions to tall evergreen vegetation in tropical Asia.

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO2 on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO2 than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.

Identifying decarbonisation opportunities using marginal abatement cost curves and energy system scenario ensembles

The European Union has a target to reduce greenhouse gas emissions to 80% below 1990 levels by 2050 to keep global warming within 2 °C relative to pre-industrial levels. The more recent Paris climate agreement has a more ambitious target of limiting the temperature rise to well below 2 °C and the EU ambition is growing to become climate-neutral by 2050. This research explores decarbonization options using an innovative analytical approach that combines energy systems analysis and marginal abatement cost curves (MACCs). System-wide MACCs are derived using scenario ensembles from Irish TIMES energy systems model. Decomposition analysis is then used to associate decarbonization options with carbon abatement costs. In addition, energy systems analysis is carried out on the underlying scenario ensembles that are used to generate the MACCs to capture more technological details and to reflect interactions among technologies. The results show that MACCs are highly dependent on model assumptions and the availabilities of bioenergy and carbon capture and storage (CCS) technologies play critical roles. The paper also shows how key mitigation measures can be ranked and classified into categories of resilient (wind energy, CCS, electric vehicles, biofuels), tipping-point (electrifying building heating, biomass plants), and niche (ocean energy) technologies.

Scenario ensemble modelling of possible future earthquake impacts in Bhutan

Recent large earthquakes in the Himalaya have resulted in tens of thousands of fatalities, yet these events are thought to have had relatively moderate magnitudes for the region. Evidence suggests multiple events throughout the Himalaya in the last 1000 years have had M > 8.0 and at least two have had M > 8.5. Despite this, understanding of earthquake risk in the region is poorly constrained, particularly in Bhutan, where research on both past and future earthquakes is notably scarce. While recent work has clearly shown the potential for large earthquakes here, the impacts from potential future earthquakes in Bhutan are entirely unknown. This study attempts to address this by modelling the potential fatalities associated with a range of plausible earthquakes through a scenario ensemble analysis in order to inform contingency planning and preparedness. The results show that both the timing and location of future earthquakes are critical factors in determining the number of fatalities, with night-time earthquakes, and those located in the west of the country proving most fatal. The worst case involves ~ 9000 fatalities and results from an M8.5 earthquake directly beneath Bhutan. Nevertheless, at the local scale the number of fatalities appears to saturate at ~ M7.5, since larger earthquakes do not result in significantly larger modelled fatalities. This suggests that local-scale impacts approaching the worst case may be relatively common and emergency planning could focus on comparatively moderate-sized earthquakes since larger, less frequent events may not necessarily result in significantly more fatalities.

Hydrological simple water balance modeling for increasing geographically isolated doline wetland functions and its application to climate change

In this study, we investigate the hydro-meteorological and hydro-geomorphological factors that influence the formation of wetlands in the doline. To this end, a set of ordinary differential equations are drawn to represent hydrological responses to hydro-meteorological forcings in wetland catchments and wetlands. The relative influence of hydro-meteorological forcings and hydro-geomorphological parameters on hydrological circulation in wetlands is explored through simple water balance models that reflect key hydrological processes in wetland catchments and wetlands. Among the hydro-geomorphological parameters of wetland catchments, the surface runoff coefficient have been found to play the most important role in determining inflow into wetlands. Among the hydro-geomorphological parameters of wetlands, we found that the relative size of the wetland area to the surrounding wetland catchment area and the exfiltration capacity at the wetland bottom have a significant effect on the formation and persistence of the doline wetland. These findings suggest that in-depth management of surface runoff in wetland catchments and the prevention of wetland bottom damage are critical for the conservation of doline wetlands. In addition, it is found that rainfall in doline wetlands in Korea has a much greater influence on wetland hydrological circulation than potential evapotranspiration. It is analyzed that the seasonality of rainfall has the greatest effect on wetland formation and persistence under general hydro-geomorphological conditions due to the nature of monsoon climate in Korea. Applying various future climate change scenario ensembles, it is projected that general doline wetlands will not be significantly affected by future climate change. However, it is revealed that doline wetlands, which are in a relatively good environment, are likely to be sensitive to future climatic conditions.

A bricolage-style exploratory scenario analysis to manage uncertainty in socio-environmental systems modeling: investigating integrated water management options

Exploratory analysis, while useful in assessing the implications of model assumptions under large uncertainty, is considered at best a semi-structured activity. There is no algorithmic way for performing exploratory analysis and the existing canonical techniques have their own limitations. To overcome this, we advocate a bricolage-style exploratory scenario analysis, which can be crafted by pragmatically and strategically combining different methods and practices. Our argument is illustrated using a case study in integrated water management in the Murray-Darling Basin, Australia. Scenario ensembles are generated to investigate potential policy innovations, climate and crop market conditions, as well as the effects of uncertainties in model components and parameters. Visualizations, regression trees and marginal effect analyses are exploited to make sense of the ensemble of scenarios. The analysis includes identifying patterns within a scenario ensemble, by visualizing initial hypotheses that are informed by prior knowledge, as well as by visualizing new hypotheses based on identified influential variables. Context-specific relationships are explored by analyzing which values of drivers and management options influence outcomes. Synthesis is achieved by identifying context-specific solutions to consider as part of policy design. The process of analysis is cast as a process of finding patterns and formulating questions within the ensemble of scenarios that merit further examination, allowing end-users to make the decision as to what underlying assumptions should be accepted, and whether uncertainties have been sufficiently explored. This approach is especially advantageous when the precise intentions of management are still subject to deliberations. By describing the reasoning and steps behind a bricolage-style exploratory analysis, we hope to raise awareness of the value of sharing this kind of (common but not often documented) analysis process, and motivate further work to improve sharing of know-how about bricolage in practice.

An efficient framework for ensemble of natural disaster simulations as a service

Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors. Such large ensembles cannot typically be run on a single computer due to the limited computational resources available. Cloud Computing offers an attractive alternative, with an almost unlimited capacity for computation, storage, and network bandwidth. However, there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources. As such, this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud. The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models. We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements. The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.

Climatic fluctuations modeled for carbon and sulfur emissions from end-Triassic volcanism

Throughout the history of complex life, Earth's climate and biogeochemical cycles have been perturbed by Large Igneous Province (LIP) volcanism, with several LIP episodes correlating with major mass extinction events. Yet many aspects of the interplay between geological, climatic and ecological processes in the Earth System during these times of global upheaval remain poorly understood. This study focuses on the Central Atlantic Magmatic Province and the associated extinction event in the latest Triassic, about 201 million years ago. Although climate and carbon cycle models successfully reproduce aspects of the end-Triassic environmental changes, many questions regarding the causal and temporal relations behind them remain unresolved. Here, we report an effort to model and quantify the dynamic response of the Earth System to short pulses of volcanogenic volatile emissions for an ensemble of emission scenarios. For the first time in the context of the end-Triassic events, this is done with a coupled climate model and under consideration of both carbon and sulfur emissions. Tested are pulses with ∼1−6 kyr duration during which 2500−7500GtC are emitted and 0−500GtS form stratospheric sulfate aerosols. The simultaneous emission of carbon and sulfur during one pulse of volcanic activity causes climatic fluctuations on annual to millennial timescales: A sequence of transient global cooling and subsequent sustained warming, overprinted with high interannual variability. The simulated maximum global warming ranges from +1.8 to +4.4°C, while the amplitude of cooling is considerably higher in the upper range of the tested sulfur emission scenarios. The magnitude of temperature change varies regionally, being lowest in the Tethys realm. Changes in steric sea level (∼1−3 m) and ocean overturning strength, a surface ocean pH decrease (∼0.2−0.4) and a drop of the carbonate saturation especially in the Tethys are also obtained from the simulations during each emission pulse. By evaluating the simulated temperature changes against thermal tolerance limits of stony corals in a simplified manner, we find that these are not clearly transgressed on a global scale in the simulated warming scenarios. However, the climatic variability potentially introduced by the volcanic forcing would have represented significant stress for marine organisms. This study represents a significant step towards integrating multiple volcanic forcing mechanisms and environmental response processes in space and time to yield a more complete picture of impacts of CAMP volcanism and LIPs in general.