scholarly journals Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles

2021 ◽  
Author(s):  
Nicola Maher
Keyword(s):  
Deep Learning ◽  
Climate Model ◽  
Hydrological Extremes ◽  
Learning Techniques ◽  
The Usa ◽  
Climate Model Ensembles ◽  
Model Ensembles

scholarly journals Reliability and importance of structural diversity of climate model ensembles

2013 ◽  
Vol 41 (9-10) ◽  
pp. 2745-2763 ◽  
Author(s):  
Tokuta Yokohata ◽  
James D. Annan ◽  
Matthew Collins ◽  
Charles S. Jackson ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Climate Model ◽  
Structural Diversity ◽  
Climate Model Ensembles ◽  
Model Ensembles

The implications of soil erosion model conceptualization, bias-correction methods and climate model ensembles on soil erosion projections under climate change

2020 ◽  
Author(s):  
Joris de Vente ◽  
Joris Eekhout
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Erosion Model ◽  
Distribution Mapping ◽  
Climate Model Ensembles ◽  
Model Ensembles ◽  
The Impact

<p>Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. The impact of climate change on soil erosion is most often assessed by applying a soil erosion model forced by bias-corrected climate model output. A literature review among more than 100 papers showed that many studies use different soil erosion models, bias-correction methods and climate model ensembles. In this study, we assessed how these differences affect the outcome of climate change impact assessments on soil erosion. The study was performed in two contrasting Mediterranean catchments (SE Spain), where climate change is projected to lead to a decrease in annual precipitation sum and an increase in extreme precipitation, based on the RCP8.5 emission scenario. First, we assessed the impact of soil erosion model selection using the three most widely used model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). Depending on the model, soil erosion in the study area is projected to decrease (RUSLE) or increase (MUSLE and MMF). The differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. Second, we evaluated the implications of three bias‐correction methods, i.e. delta change, quantile mapping and scaled distribution mapping. Scaled distribution mapping best reproduces the raw climate change signal, in particular for extreme precipitation. Depending on the bias‐correction method, soil erosion is projected to decrease (delta change) or increase (quantile mapping and scaled distribution mapping). Finally, we assessed the effect of climate model ensembles on soil erosion projections. We showed that individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion. Furthermore, the impact of climate change on soil erosion can only accurately be assessed with a bias‐correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.</p>

scholarly journals Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

2019 ◽  
Vol 32 (9) ◽  
pp. 2591-2603 ◽  
Author(s):  
Emily Hogan ◽  
Robert E. Nicholas ◽  
Klaus Keller ◽  
Stephanie Eilts ◽  
Ryan L. Sriver
Keyword(s):  
United States ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Extreme Temperature ◽  
The United States ◽  
Temperature Extremes ◽  
Warm Temperature ◽  
Climate Model Ensembles ◽  
Model Ensembles

Abstract Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

scholarly journals Influence of blocking on Northern European and Western Russian heatwaves in large climate model ensembles

2018 ◽  
Vol 13 (5) ◽  
pp. 054015 ◽  
Author(s):  
N Schaller ◽  
J Sillmann ◽  
J Anstey ◽  
E M Fischer ◽  
C M Grams ◽  
...  
Keyword(s):  
Climate Model ◽  
Northern European ◽  
Climate Model Ensembles ◽  
Model Ensembles

scholarly journals The extremely warm summer of 2018 in Sweden – set in a historical context

2020 ◽  
Vol 11 (4) ◽  
pp. 1107-1121
Author(s):  
Renate Anna Irma Wilcke ◽  
Erik Kjellström ◽  
Changgui Lin ◽  
Daniela Matei ◽  
Anders Moberg ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Human Life ◽  
Historical Context ◽  
Lower Probability ◽  
Warm Summer ◽  
Long Time ◽  
Climate Model Ensembles ◽  
Model Ensembles ◽  
The One

Abstract. Two long-lasting high-pressure systems in summer 2018 led to persisting heatwaves over Scandinavia and other parts of Europe and an extended summer period with devastating impacts on agriculture, infrastructure, and human life. We use five climate model ensembles and the unique 263-year-long Stockholm temperature time series along with a composite 150-year-long time series for the whole of Sweden to set the latest heatwave in the summer of 2018 into historical perspective. With 263 years of data, we are able to grasp the pre-industrial period well and see a clear upward trend in temperature as well as upward trends in five heatwave indicators. With five climate model ensembles providing 20 580 simulated summers representing the latest 70 years, we analyse the likelihood of such a heat event and how unusual the 2018 Swedish summer actually was. We find that conditions such as those observed in summer 2018 are present in all climate model ensembles. An exception is the monthly mean temperature for May for which 2018 was warmer than any member in one of the five climate model ensembles. However, even if the ensembles generally contain individual years like 2018, the comparison shows that such conditions are rare. For the indices assessed here, anomalies such as those observed in 2018 occur in a maximum of 5 % of the ensemble members, sometimes even in less than 1 %. For all of the indices evaluated, we find that the probability of a summer such as that in 2018 has increased from relatively low values in the pre-industrial era (1861–1890, one ensemble) and the recent past (1951–1980, all five ensembles) to higher values in the most recent decades (1989–2018). An implication of this is that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed 2018, occurring in Sweden. Despite this, we still find such summers in the pre-industrial climate in our simulations, albeit with a lower probability.

Sign in / Sign up

Export Citation Format

Share Document