scholarly journals ATTRICI v1.1 – counterfactual climate for impact attribution

2021 ◽  
Vol 14 (8) ◽  
pp. 5269-5284
Author(s):  
Matthias Mengel ◽  
Simon Treu ◽  
Stefan Lange ◽  
Katja Frieler
Keyword(s):  
Climate Change ◽  
Climate Impact ◽  
Climate Impacts ◽  
General Definition ◽  
Impact Model ◽  
Climate Data ◽  
Climate Trends ◽  
Long Term Changes ◽  
The Impact

Abstract. Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.

Supporting European coastal sectors to adapt to changes in extreme sea levels with climate change

2020 ◽  
Author(s):  
Sanne Muis ◽  
Maialen Irazoqui Apecechea ◽  
Job Dullaart ◽  
Joao de Lima Rego ◽  
Kristine S. Madsen ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Storm Surges ◽  
Climate Impact ◽  
Climate Impacts ◽  
Water Levels ◽  
Climate Data ◽  
Local Climate ◽  
Sea Levels ◽  
Extreme Sea Levels

<p>Climate change will lead to increases in the flood risk in low-lying coastal areas. Understanding the magnitude and impact of such changes is vital to design adaptive strategies and create awareness. In  the  context  of  the  CoDEC  project  (Coastal  Dataset  for  Evaluation  of  Climate  impact),  we  developed a consistent European dataset of extreme sea levels, including climatic changes from 1979 to 2100. To simulate extreme sea levels, we apply the Global Tide and Surge Model v3.0 (GTSMv3.0), a 2D hydrodynamic model with global coverage. GTSM has a coastal resolution of 2.5 km globally and 1.25 km in Europe, and incorporates dynamic interactions between sea-level  rise,  tides  and  storm surges. Validation of the dataset shows a good performance with a mean bias of 0-.04 m for the 1 in 10-year water levels. When analyzing changes in extreme sea levels for the future climate scenarios, it is projected that by the end of the century the 1 in 10-year water levels are likely to increase up to 0.5 m. This change is largely driven by the increase in mean sea levels, although locally changes in storms surge and interaction with tides can amplify the impacts of sea-level rise with changes up to 0.2 m in the 1 in 10-year water level.</p><p>The CoDEC dataset will be made accessible through a web portal on Copernicus Climate Data Store (C3S). The dataset includes a set of Climate Impact Indicators (CII’s) and new tools designed to evaluate the impacts of climate change on different sectors and industries. This data service will support European coastal sectors to adapt to changes in sea levels associated with climate change. In this presentation we will also demonstrate how the C3S coastal service can be used to enhance the understanding of local climate impacts.</p>

scholarly journals Climate impacts on human livelihoods: where uncertainty matters in projections of water availability

2014 ◽  
Vol 5 (1) ◽  
pp. 403-442 ◽  
Author(s):  
T. K. Lissner ◽  
D. E. Reusser ◽  
J. Schewe ◽  
T. Lakes ◽  
J. P. Kropp
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
Water Scarcity ◽  
Climate Change Impacts ◽  
Well Being ◽  
Global Scale ◽  
Climate Impact ◽  
Climate Impacts ◽  
Impact Model ◽  
First Results

Abstract. Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target-measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models as well as greenhouse gas scenarios are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure Adequate Human livelihood conditions for wEll-being And Development (AHEAD). Based on a transdisciplinary sample of influential concepts addressing human well-being, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows identifying and differentiating uncertainty of climate and impact model projections. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions – and where it is not. The results indicate that in many countries today, livelihood conditions are compromised by water scarcity. However, more often, AHEAD fulfilment is limited through other elements. Moreover, the analysis shows that for 44 out of 111 countries, the water-specific uncertainty ranges are outside relevant thresholds for AHEAD, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy-decisions.

scholarly journals Climate impacts on human livelihoods: where uncertainty matters in projections of water availability

2014 ◽  
Vol 5 (2) ◽  
pp. 355-373 ◽  
Author(s):  
T. K. Lissner ◽  
D. E. Reusser ◽  
J. Schewe ◽  
T. Lakes ◽  
J. P. Kropp
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
Water Scarcity ◽  
Climate Change Impacts ◽  
Well Being ◽  
Global Scale ◽  
Climate Impact ◽  
Climate Impacts ◽  
Impact Model ◽  
First Results

Abstract. Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions – and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions.

scholarly journals A methodology to quantify and monitor the impact of environmental restoration using climate data records from satellite observations

2021 ◽  
Author(s):  
Mendy van der Vliet ◽  
Richard de Jeu ◽  
Jaap Schellekens ◽  
Robin van der Schalie
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
European Space Agency ◽  
Quality Data ◽  
Climate Mitigation ◽  
Environmental Restoration ◽  
Climate Data ◽  
Great Opportunity ◽  
The Impact

<p>Environmental restoration has the potential to constrain human-induced land degradation, loss of biodiversity and climate change. Although the practise is increasingly integrated into natural resource and climate mitigation strategies, scientific studies underline that the effectiveness and impact of these restoration projects are currently difficult to monitor and assess. In order to measure the global community’s progress towards the Sustainable Development Goals (SDGs), restoration interventions need to be assessed in a systematic and objective manner. However, the long-term and high-quality data records that are required for this are often lacking in both time and space. Satellite data products that can detect changes in land use, surface temperature and hydrological conditions over time in a consistent manner, can fill this gap.</p><p>Over the last few decades, the scientific community has made great efforts to merge different satellites into multi-decadal historical datasets of climate variables. Examples of such long-term climate data records (CDRs) are the soil moisture (from 1978 onwards), land surface temperature (since 1995) and land cover (since 2008) datasets of the European Space Agency Climate Change Initiative (ESA CCI). These consistent datasets, combined with near real-time observations, offer a great opportunity to quantify and monitor the impact of restoration interventions on degraded landscapes. In order to monitor restoration projects affecting areas smaller than the native resolutions of these datasets (up to approximately 25 km), downscaling techniques can be used to increase the spatial level of detail (approximately in the 0.1-1 km range). The resulting monitoring service could help managers of restoration programs and green investment funds to steer decisions and communicate on effectiveness towards their donors. </p><p>The satellite datasets were investigated in space and time in relation to the effects of the restoration projects. For each restoration project area, several surface conditions were monitored and compared to those in an unaffected control area to detect and attribute the effects of the restoration program. The present work focuses on several case studies in which the relevance of satellite-based CDRs for the end users’ operational practises related to impact monitoring is assessed in the context of the SDGs 12 (Responsible production and consumption), 13 (Life on land) and 15 (Climate action).</p>

scholarly journals A strategy for climate evaluation of aircraft technology: an efficient climate impact assessment tool – AirClim

2007 ◽  
Vol 7 (4) ◽  
pp. 12185-12229 ◽  
Author(s):  
V. Grewe ◽  
A. Stenke
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Impact ◽  
Climate Impacts ◽  
Assessment Tools ◽  
Nox Emissions ◽  
Near Surface ◽  
Temperature Changes ◽  
Concentration Changes ◽  
The Impact

Abstract. Climate change is a challenge to society and to cope with requires assessment tools which are suitable to evaluate new technology options with respect to their impact on climate. Here we present AirClim, a model which comprises a linearisation of the processes occurring from the emission to an estimate in near surface temperature change, which is presumed to be a reasonable indicator for climate change. The model is designed to be applicable to aircraft technology, i.e.~the climate agents CO2, H2O, CH4 and O3 (latter two resulting from NOx-emissions) and contrails are taken into account. It employs a number of precalculated atmospheric data and combines them with aircraft emission data to obtain the temporal evolution of atmospheric concentration changes, radiative forcing and temperature changes. The linearisation is based on precalculated data derived from 25 steady-state simulations of the state-of-the-art climate-chemistry model E39/C, which include sustained normalised emissions at various atmospheric regions. The results show that strongest climate impacts from ozone changes occur for emissions in the tropical upper troposphere (60 mW/m²; 80 mK for 1 TgN emitted), whereas from methane in the middle tropical troposphere (–2.7% change in methane lifetime; –30 mK per TgN). The estimate of the temperature changes caused by the individual climate agents takes into account a perturbation lifetime, related to the region of emission. A comparison of this approach with results from the TRADEOFF and SCENIC projects shows reasonable agreement with respect to concentration changes, radiative forcing, and temperature changes. The total impact of a supersonic fleet on radiative forcing (mainly water vapour) is reproduced within 5%. For subsonic air traffic (sustained emissions after 2050) results show that although ozone-radiative forcing is much less important than that from CO2 for the year 2100. However the impact on temperature is of comparable size even when taking into account temperature decreases from CH4. That implies that all future measures for climate stabilisation should concentrate on both CO2 and NOx emissions. A direct comparison of super- with subsonic aircraft (250 passengers, 5400 nm) reveals a 5 times higher climate impact of supersonics.

Assessing Climate Change Impact on Asphalt Binder Grade Selection and its Implications

2021 ◽  
pp. 036119812110130
Author(s):  
Surya T. Swarna ◽  
Kamal Hossain ◽  
Harshdutta Pandya ◽  
Yusuf A. Mehta
Keyword(s):  
Climate Change ◽  
Asphalt Binder ◽  
Climate Impacts ◽  
Future Climate ◽  
Pavement Performance ◽  
Global Changes ◽  
Second Phase ◽  
Climate Data ◽  
Climate Trends ◽  
Change Models

Anthropogenic climate change is having and will continue to have unpredictable effects on Canadian weather. Trends in average annual temperatures have been rapidly increasing over the last 50 years. The severe climatic variations in Canada are in line with global changes in climate occurring as a result of increased greenhouse gas concentrations in the atmosphere. Under the current CO2 emission scenarios, scientists predict the climate trends to further intensify in the near future. It is well known that asphalt binder is highly sensitive to climate factors. For this reason, reviewing asphalt binder grade is a vital step, and can help decelerate pavement deterioration. The objective of this study was to assess the change in asphalt binder grade for the future climate and to determine the influence of change in binder grade on the performance of pavements in Canada. To achieve this, the analysis was carried out in five phases. In the first phase, statistically downscaled climate change models were gathered from the Pacific Climate Impacts Consortium database. In the second phase, the temperature and precipitation data were extracted for the selected locations in southern Canada. In the third phase, the asphalt binder grade was determined for future climate data. In the fourth phase, the pavement materials, traffic, and structural data were collected from the Long-Term Pavement Performance database. Lastly, the pavement performance with the base binder and the upgraded binder were assessed using AASHTOware Mechanistic–Empirical Pavement Design. The results reemphasize the necessity of upgrading the asphalt binder grade in various provinces of Canada.

scholarly journals Climate impacts on vegetation and fire dynamics since the last deglaciation at Moossee (Switzerland)

2020 ◽  
Vol 16 (4) ◽  
pp. 1347-1367 ◽  
Author(s):  
Fabian Rey ◽  
Erika Gobet ◽  
Christoph Schwörer ◽  
Albert Hafner ◽  
Sönke Szidat ◽  
...  
Keyword(s):  
Climate Change ◽  
Fagus Sylvatica ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Central European ◽  
Vegetation Shifts ◽  
Temperate Trees ◽  
The Impact

Abstract. Since the Last Glacial Maximum (LGM; end ca. 19 000 cal BP) central European plant communities have been shaped by changing climatic and anthropogenic disturbances. Understanding long-term ecosystem reorganizations in response to past environmental changes is crucial to draw conclusions about the impact of future climate change. So far, it has been difficult to address the post-deglaciation timing and ecosystem dynamics due to a lack of well-dated and continuous sediment sequences covering the entire period after the LGM. Here, we present a new paleoecological study with exceptional chronological time control using pollen, spores and microscopic charcoal from Moossee (Swiss Plateau, 521 m a.s.l.) to reconstruct the vegetation and fire history over the last ca. 19 000 years. After lake formation in response to deglaciation, five major pollen-inferred ecosystem rearrangements occurred at ca. 18 800 cal BP (establishment of steppe tundra), 16 000 cal BP (spread of shrub tundra), 14 600 cal BP (expansion of boreal forests), 11 600 cal BP (establishment of the first temperate deciduous tree stands composed of, e.g., Quercus, Ulmus, Alnus) and 8200 cal BP (first occurrence of mesophilous Fagus sylvatica trees). These vegetation shifts were caused by climate changes at ca. 19 000, 16 000, 14 700, 11 700 and 8200 cal BP. Vegetation responses occurred with no apparent time lag to climate change when the mutual chronological uncertainties are considered. This finding is in agreement with further evidence from southern and central Europe and might be explained by the proximity to the refugia of boreal and temperate trees (<400 km) and rapid species spreads. Our palynological record sets the beginning of millennial-scale land use with periodically increased fire and agricultural activities of the Neolithic period at ca. 7000 cal BP. Subsequently, humans rather than climate triggered changes in vegetation composition and structure. We conclude that Fagus sylvatica forests were resilient to long-term anthropogenic and climatic impacts of the Mid and the Late Holocene. However, future climate warming and in particular declining moisture availability may cause unprecedented reorganizations of central European beech-dominated forest ecosystems.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

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