Towards multi-method and multi-scale attribution of global wildfire danger

2020 ◽  
Author(s):  
Zhongwei Liu ◽  
Jonathan Eden ◽  
Bastien Dieppois ◽  
Matthew Blackett
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Methodological Approach ◽  
Multi Scale ◽  
Attribution Analysis ◽  
Event Attribution ◽  
Large Fires ◽  
Future Risk ◽  
Warming World

<p>Wildfires constitute a major natural hazard and pose huge risk to many regions of the world. The series of large fires across both hemispheres in recent years have led to inevitable questions about how human-induced climate change may be altering the character of such events. Providing answers to these questions is a crucial step to increasing resilience to major wildfires.</p><p>Long-term projections produced by state-of-the-art climate models, even when reliable, are not always a suitable means of communicating risk. Methodologies to attribute trends in meteorological phenomena associated with high-impact events to anthropogenic influence have the potential to better communicate risk and guide adaptation strategies. While the link between a warming world and heat-related extremes (e.g. heatwaves and droughts) is reasonably well-understood, there is a lack of consensus on the most appropriate and effective methodological approach for many variables, potentially impacted by warming climate, such as wildfire attribution. The link with climate change remains poorly understood and wildfires have been largely ignored by attribution studies to date.</p><p>As a first step towards the development of a seamless, globally-applicable framework for assessing past, present and future risk in wildfire danger, we present a global attribution analysis of wildfire danger. With initial focus on observational records, we use both established and novel empirical-statistical methods to attribute historical trends in episodes of extreme weather and climate conducive to wildfire ignition and spread. Particular consideration is given to the sensitivity of attribution findings to the spatial scale upon which the analysis is conducted. We also draw attention to a series of important, often overlooked, conceptual and technical challenges in event attribution, including validation and bias-correction of climate models and discuss the value of linking attribution of recent wildfire events with future risk assessment.</p>

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

scholarly journals Past temperature reconstructions from deep ice cores: relevance for future climate change

2006 ◽  
Vol 2 (2) ◽  
pp. 145-165 ◽  
Author(s):  
V. Masson-Delmotte ◽  
G. Dreyfus ◽  
P. Braconnot ◽  
S. Johnsen ◽  
J. Jouzel ◽  
...  
Keyword(s):  
Climate Change ◽  
Temperature Change ◽  
Climate Models ◽  
Ice Core ◽  
Ice Cores ◽  
Future Climate ◽  
Future Climate Change ◽  
Interglacial Period ◽  
Temperature Reconstructions

Abstract. Ice cores provide unique archives of past climate and environmental changes based only on physical processes. Quantitative temperature reconstructions are essential for the comparison between ice core records and climate models. We give an overview of the methods that have been developed to reconstruct past local temperatures from deep ice cores and highlight several points that are relevant for future climate change. We first analyse the long term fluctuations of temperature as depicted in the long Antarctic record from EPICA Dome C. The long term imprint of obliquity changes in the EPICA Dome C record is highlighted and compared to simulations conducted with the ECBILT-CLIO intermediate complexity climate model. We discuss the comparison between the current interglacial period and the long interglacial corresponding to marine isotopic stage 11, ~400 kyr BP. Previous studies had focused on the role of precession and the thresholds required to induce glacial inceptions. We suggest that, due to the low eccentricity configuration of MIS 11 and the Holocene, the effect of precession on the incoming solar radiation is damped and that changes in obliquity must be taken into account. The EPICA Dome C alignment of terminations I and VI published in 2004 corresponds to a phasing of the obliquity signals. A conjunction of low obliquity and minimum northern hemisphere summer insolation is not found in the next tens of thousand years, supporting the idea of an unusually long interglacial ahead. As a second point relevant for future climate change, we discuss the magnitude and rate of change of past temperatures reconstructed from Greenland (NorthGRIP) and Antarctic (Dome C) ice cores. Past episodes of temperatures above the present-day values by up to 5°C are recorded at both locations during the penultimate interglacial period. The rate of polar warming simulated by coupled climate models forced by a CO2 increase of 1% per year is compared to ice-core-based temperature reconstructions. In Antarctica, the CO2-induced warming lies clearly beyond the natural rhythm of temperature fluctuations. In Greenland, the CO2-induced warming is as fast or faster than the most rapid temperature shifts of the last ice age. The magnitude of polar temperature change in response to a quadrupling of atmospheric CO2 is comparable to the magnitude of the polar temperature change from the Last Glacial Maximum to present-day. When forced by prescribed changes in ice sheet reconstructions and CO2 changes, climate models systematically underestimate the glacial-interglacial polar temperature change.

scholarly journals Describing the Relationship between a Weather Event and Climate Change: A New Statistical Approach

2020 ◽  
Vol 33 (15) ◽  
pp. 6297-6314 ◽  
Author(s):  
Aurélien Ribes ◽  
Soulivanh Thao ◽  
Julien Cattiaux
Keyword(s):  
Climate Change ◽  
Sources Of Information ◽  
Single Model ◽  
Coupled Models ◽  
Weather Event ◽  
Event Attribution ◽  
Transient Simulations ◽  
Induced Changes ◽  
The Relationship

AbstractDescribing the relationship between a weather event and climate change—a science usually termed event attribution—involves quantifying the extent to which human influence has affected the frequency or the strength of an observed event. In this study we show how event attribution can be implemented through the application of nonstationary statistics to transient simulations, typically covering the 1850–2100 period. The use of existing CMIP-style simulations has many advantages, including their availability for a large range of coupled models and the fact that they are not conditional to a given oceanic state. We develop a technique for providing a multimodel synthesis, consistent with the uncertainty analysis of long-term changes. Last, we describe how model estimates can be combined with historical observations to provide a single diagnosis accounting for both sources of information. The potential of this new method is illustrated using the 2003 European heat wave and under a Gaussian assumption. Results suggest that (i) it is feasible to perform event attribution using transient simulations and nonstationary statistics, even for a single model; (ii) the use of multimodel synthesis in event attribution is highly desirable given the spread in single-model estimates; and (iii) merging models and observations substantially reduces uncertainties in human-induced changes. Investigating transient simulations also enables us to derive insightful diagnostics of how the targeted event will be affected by climate change in the future.

scholarly journals Attribution of the Australian bushfire risk to anthropogenic climate change

2020 ◽  
Author(s):  
Geert Jan van Oldenborgh ◽  
Folmer Krikken ◽  
Sophie Lewis ◽  
Nicholas J. Leach ◽  
Flavio Lehner ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Southern Annular Mode ◽  
Warming Trend ◽  
Anthropogenic Climate Change ◽  
Fire Season ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index

Abstract. Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically-based index of fire weather, the Fire Weather Index, long-term observations of heat and drought, and eleven large ensembles of state-of-the-art climate models. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor two due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season September–February. The observations, however, show a weak drying trend in the annual mean. Finally, we find large trends in the Fire Weather Index in the ERA5 reanalysis, and a smaller but significant increase by at least 30 % in the models. The trend is mainly driven by the increase of temperature extremes and hence also likely underestimated. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean dipole and Southern Annular Mode. These factors are included in the analysis. The study reveals the complexity of the 2019/20 bushfire event, with some, but not all drivers showing an imprint of anthropogenic climate change.

Uncertainty Quantification in Multi-Model Ensembles

2018 ◽  
Author(s):  
Benjamin Mark Sanderson
Keyword(s):  
Climate Change ◽  
Uncertainty Quantification ◽  
Climate Models ◽  
Climate Model ◽  
Initial Condition ◽  
Model Errors ◽  
Model Framework ◽  
Validation Data ◽  
Model Ensembles

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.

scholarly journals Varying soil moisture-atmosphere feedbacks explain divergent temperature extremes and precipitation projections in Central Europe

2018 ◽  
Author(s):  
Martha M. Vogel ◽  
Jakob Zscheischler ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Central Europe ◽  
Climate Models ◽  
Global Climate ◽  
Summer Precipitation ◽  
Global Climate Models ◽  
Temperature Extremes ◽  
Extreme Temperatures

Abstract. The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In Central Europe extreme temperatures are projected to change more strongly than global mean temperatures and soil moisture-temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in Central Europe are prone to large uncertainties. In order to understand and potentially reduce uncertainties of extreme temperatures projections in Europe, we analyze global climate models from the CMIP5 ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for summer latent heat flux, top soil moisture, and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land-atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a too strong warming. This is particularly relevant for global warming levels above 2 °C. The analysis allows for the first time to substantially reduce uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in Central Europe are about 20 % lower than projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for Central Europe.

scholarly journals Climate Adaptation in the Water Sector in India

2019 ◽  
pp. 498-518
Author(s):  
Veena Srinivasan
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
Climate Models ◽  
Climate Adaptation ◽  
Hydrologic Cycle ◽  
Rainfall Trends ◽  
Droughts And Floods ◽  
Regional Redistribution ◽  
Short Term Variability

Climate change is likely to affect both the short-term variability of water resources through increased frequency and intensity of droughts and floods, and long-term changes in mean renewable water supply. Both models and historical data suggest that temperatures have increased in most parts of India, affecting the hydrologic cycle through decreased Himalayan snowpack, increased evaporation, and evapotranspirative demand by vegetation. In contrast, there are uncertainties about the climate–rainfall relationship. While most climate models predict intensification of the Indian monsoon, past rainfall trends suggest a weakening and a regional redistribution, perhaps due to local factors such as aerosols, land use change, and sea surface temperatures. Translating these uncertain projections to water availability is complicated by sparse hydrologic records and human modifications of catchments. Empirical research suggests that climate change is not the only stressor. As climate and socio-economic futures are interlinked, this requires participatory, adaptive management and mainstreaming of adaptation across agencies.

The Data Imaginaries of Climate Art: The Manifest Data Project

Leonardo ◽  
2021 ◽  
pp. 1-10
Author(s):  
Tom Corby ◽  
Gavin Baily ◽  
Jonathan Mackenzie ◽  
Giles Lane ◽  
Erin Dickson ◽  
...  
Keyword(s):  
Climate Change ◽  
Southern Ocean ◽  
Climate Models ◽  
Methodological Approach ◽  
Ways Of Knowing ◽  
Data Driven ◽  
Ocean Studies

Abstract We discuss a series of artworks produced since 2009 including The Southern Ocean Studies (2012), The Northern Polar Studies (2014) and Carbon Topographies (2020). Through this work we explore how climate models can be employed to develop data driven imaginaries of climate change, its impacts and causes. We argue for the experiential potential of this information for producing differently situated ways of knowing climate, framing this through a methodological approach described as ‘data manifestation’.

scholarly journals Climate change and water resources management of Oran region

2016 ◽  
Vol 8 (2) ◽  
pp. 348-361
Author(s):  
Rachid Bouklia-Hassane ◽  
Djilali Yebdri ◽  
Abdellatif El-Bari Tidjani
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Balance ◽  
Methodological Approach ◽  
Two Dimensions ◽  
Modeling Framework ◽  
Southern Mediterranean ◽  
Socioeconomic Dimension ◽  
Oran Region

Our work aims to contribute to the literature on the prospective study of the water balance in the Oran region, a major southern Mediterranean metropolis, by considering the socioeconomic dimension of this region and the dynamic of its climate change through 2011–2030. These two dimensions are important for the analysis of future changes in water stress in the region because they affect both the demand and the supply of the water resources. Unlike other studies, our methodological approach is based on an explicit modeling of the socioeconomic evolution in the region as well as of the dynamic of climate change. For this, we used a time-series modeling framework to predict the effects of change in climate. In addition to the assessment of the effects of the socioeconomic and climate changes on the water balance of the region. Our results, based on simulations using the Water Evaluation and Planning (WEAP) software, show that the current decoupling between the drinking sector from that of irrigation in the Oran region is not sustainable. Climate change will exacerbate this vulnerability. Only by integrating these two sectors, through a reuse of wastewater, can we consider the irrigation issue from a perspective of long-term sustainability in the region.

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