Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

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Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

Geospatial health ◽

10.4081/gh.2016.393 ◽

2016 ◽

Vol 11 (1s) ◽

Cited By ~ 8

Author(s):

Joseph Leedale ◽

Adrian M. Tompkins ◽

Cyril Caminade ◽

Anne E. Jones ◽

Grigory Nikulin ◽

...

Keyword(s):

Climate Change ◽

Malaria Transmission ◽

Climate Models ◽

Climate Model ◽

Eastern Africa ◽

Future Climate Change ◽

Climate Projections ◽

Model Ensemble ◽

East African Community ◽

Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

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Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

Climatic Change ◽

10.1007/s10584-019-02550-2 ◽

2019 ◽

Vol 156 (3) ◽

pp. 299-314 ◽

Cited By ~ 6

Author(s):

Gabriel Rondeau-Genesse ◽

Marco Braun

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Ghg Emissions ◽

Internal Variability ◽

Mean Annual Temperature ◽

Large Ensemble ◽

Climate Change Effects ◽

Large Ensembles ◽

The Mean

Abstract The pace of climate change can have a direct impact on the efforts required to adapt. For short timescales, however, this pace can be masked by internal variability (IV). Over a few decades, this can cause climate change effects to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climate models and thus combine both IV and inter-model differences. This study instead uses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) that comprise multiple realizations from a single climate model and a single GHG emission scenario, to quantify the relationship between IV and climate change over the next decades in Canada and the USA. The mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. However, members of the large ensembles in which a slowdown of warming is found during the 2021–2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of IV, remains fairly uncommon for the mean annual temperature, but occurs in 5 to 15% of the large ensemble members for the temperature extremes.

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Ensemble climate predictions using climate models and observational constraints

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2075 ◽

2007 ◽

Vol 365 (1857) ◽

pp. 2029-2052 ◽

Cited By ~ 44

Author(s):

Peter A Stott ◽

Chris E Forest

Keyword(s):

Climate Change ◽

General Circulation ◽

Climate Changes ◽

Climate Models ◽

State Of The Art ◽

General Circulation Models ◽

Computationally Efficient ◽

Temperature Changes ◽

Large Ensembles ◽

Past Climate Change

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

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Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v13.1.p271-285 ◽

2020 ◽

Vol 13 (1) ◽

pp. 271

Author(s):

Rafaela Lisboa Costa ◽

Heliofábio Barros Gomes ◽

Fabrício Daniel Dos Santos Silva ◽

Rodrigo Lins Da Rocha Júnior ◽

Giuliene Carla Dos Santos Silva ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Change Detection ◽

Annual Rainfall ◽

Positive Trend ◽

Total Period ◽

Significant Positive Trend ◽

Total Length ◽

Expert Team ◽

Climate Change Detection

Este trabalho teve como objetivo aplicar e estudar 11 índices de extremos de precipitação formulados pelo ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), para a cidade de Cabaceiras-PB, utilizando dados diários de precipitação contínuos de 90 anos. Os índices foram calculados para o comprimento total da série, 1928 a 2017, assim como para três segmentos de 30 anos (1928-1957, 1958-1987 e 1988-2017). Os resultados evidenciaram que para muitos índices, tendências opostas e estatisticamente significativas podem ser observadas a depender do subperíodo estudado, assim como haver diferença entre estas tendências e as obtidas ao analisar-se o período total dos dados. Exemplos disso aconteceram para os índices R1mm, R10mm, R20mm, CWD e PRCPTOT. Trends in extreme precipitation indexes in Cabaceiras (PB) for different periods A B S T R A C TThis work aimed to apply and analyze 11 precipitation extremes indexes formulated by ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), for the city of Cabaceiras, located in the Borborema mesoregion and microregion of Paraíba Oriental Cariri. A municipality in the semiarid region, it has the title of municipality where it rains less in Brazil, with an annual average of just over 300mm. Daily 90-year continuous precipitation data were used for the extreme indices, with the time series analyzed for four distinct periods, the total length of the series, 1928 to 2017, as well as three 30-year segments (1928-1957, 1958- 1987 and 1988-2017). The results showed that for many indices, opposite and significant trends can be observed depending on the sub period studied, as well as differences between these trends and those obtained by analyzing the total data period. The R1, R10 and R20mm indices show significant negative trends in the 1928-1957 sub period, but positive in the following two sub periods, reflecting a significant positive trend in the total period from 1928 to 2017. Other interesting examples are CDD indices for consecutive dry days, and PRCPTOT, for total annual rainfall with rainfall greater than 1mm. The CDD showed significant positive trend only in the 1928-1957 sub period, but non-significant negative trends in the subsequent sub periods, reflecting non-significant negative trends in the total length of the series. The PRCPTOT index shows behavior opposite to the CDD index, with a significant negative trend in the 1928-1957 sub period, positive in 1958-1987 and negative again in 1988-2017, but for the total length of the series the trend is positive and significant. These results show that the analysis of extreme trends is noticeably sensitive to the sample of the analyzed period, and may not reflect the reality of the time series the longer the total length of the time series, and need to be used with caution.Keywords: climate variability, dry and wet periods, semiarid.

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Intensification of extreme snowfall under future warming

10.5194/egusphere-egu21-10589 ◽

2021 ◽

Author(s):

Lennart Quante ◽

Sven Willner ◽

Robin Middelanis ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Observational Data ◽

Climate Models ◽

Hydrological Cycle ◽

Freezing Point ◽

Model Ensemble ◽

Northern Latitudes ◽

Coupled Climate Models ◽

Future Warming

Due to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming thereby has two opposing influences. Increasing humidity enables potentially intense snowfall, whereas warming temperatures decrease the likelihood of snowfall in the first place. Here we show an intensification of extreme snowfall under future warming, which is robust across all global coupled climate models when they are bias-corrected with observational data. While mean daily snowfall decreases drastically in the model ensemble, both the 99th and the 99.9th percentiles of daily snowfall increase strongly in the next decades. Additionally, the magnitude of high snowfall events increases, which is likely to pose considerable challenge to municipalities in mid to high northern latitudes. We propose that the almost unchanged occurrence of temperatures just below the freezing point of water in combination with the strengthening of the hydrological cycle enables this intensification of extreme snowfall. Thus extreme snowfall events are likely to become an increasingly important impact of climate change on society in the next decades.

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Spatiotemporal Changes in Headwater Flow Contributions to Major Rivers of Germany under Changing Climate

10.5194/egusphere-egu2020-7639 ◽

2020 ◽

Author(s):

Akash Koppa ◽

Thomas Remke ◽

Stephan Thober ◽

Oldrich Rakovec ◽

Sebastian Müller ◽

...

Keyword(s):

Climate Change ◽

Global Warming ◽

Soil Moisture ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Model Ensemble ◽

Hydrologic Models ◽

River Temperature

Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:Climate Modeling: We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). Hydrologic Modeling: To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. Streamflow Routing: To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. River Temperature Modeling: A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project1,2,3,4. The newly created projections in this project will be compared against those created in the EDgE project.&#160; The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.References[1] Marx, A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.[2] Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.[3] Samaniego, L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.[4] Thober, S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.&#160;&#160;&#160;

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Issues in the interpretation of climate model ensembles to inform decisions

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2073 ◽

2007 ◽

Vol 365 (1857) ◽

pp. 2163-2177 ◽

Cited By ~ 125

Author(s):

David A Stainforth ◽

Thomas E Downing ◽

Richard Washington ◽

Ana Lopez ◽

Mark New

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Global Scale ◽

Climate Modelling ◽

Large Ensembles ◽

Recent Developments ◽

Climate Model Simulations ◽

Model Ensembles ◽

A Minor

There is a scientific consensus regarding the reality of anthropogenic climate change. This has led to substantial efforts to reduce atmospheric greenhouse gas emissions and thereby mitigate the impacts of climate change on a global scale. Despite these efforts, we are committed to substantial further changes over at least the next few decades. Societies will therefore have to adapt to changes in climate. Both adaptation and mitigation require action on scales ranging from local to global, but adaptation could directly benefit from climate predictions on regional scales while mitigation could be driven solely by awareness of the global problem; regional projections being principally of motivational value. We discuss how recent developments of large ensembles of climate model simulations can be interpreted to provide information on these scales and to inform societal decisions. Adaptation is most relevant as an influence on decisions which exist irrespective of climate change, but which have consequences on decadal time-scales. Even in such situations, climate change is often only a minor influence; perhaps helping to restrict the choice of ‘no regrets’ strategies. Nevertheless, if climate models are to provide inputs to societal decisions, it is important to interpret them appropriately. We take climate ensembles exploring model uncertainty as potentially providing a lower bound on the maximum range of uncertainty and thus a non-discountable climate change envelope. An analysis pathway is presented, describing how this information may provide an input to decisions, sometimes via a number of other analysis procedures and thus a cascade of uncertainty. An initial screening is seen as a valuable component of this process, potentially avoiding unnecessary effort while guiding decision makers through issues of confidence and robustness in climate modelling information. Our focus is the usage of decadal to centennial time-scale climate change simulations as inputs to decision making, but we acknowledge that robust adaptation to the variability of present day climate encourages the development of less vulnerable systems as well as building critical experience in how to respond to climatic uncertainty.

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ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2011.0305 ◽

2012 ◽

Vol 370 (1962) ◽

pp. 1121-1139 ◽

Cited By ~ 24

Author(s):

Toshichika Iizumi ◽

Mikhail A. Semenov ◽

Motoki Nishimori ◽

Yasushi Ishigooka ◽

Tsuneo Kuwagata

Keyword(s):

Climate Change ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Local Scale ◽

Climate Projections ◽

Climate Change Scenarios ◽

Emission Scenarios ◽

Model Ensemble ◽

Daily Weather Data

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.

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A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea

Sustainability ◽

10.3390/su11071976 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1976 ◽

Cited By ~ 1

Author(s):

Jang Sung ◽

Minsung Kwon ◽

Jong-June Jeon ◽

Seung Seo

Keyword(s):

Climate Change ◽

Extreme Precipitation ◽

Climate Models ◽

Climate Change Impacts ◽

Daily Maximum ◽

Climate Change Scenarios ◽

Model Ensemble ◽

Model Variability ◽

Cmip5 Gcms ◽

Selection Of

The numerous choices between climate change scenarios makes decision-making difficult for the assessment of climate change impacts. Previous studies have used climate models to compare performance in terms of simulating observed climates or preserving model variability among scenarios. In this study, the Katsavounidis-Kuo-Zhang algorithm was applied to select representative climate change scenarios (RCCS) that preserve the variability among all climate change scenarios (CCS). The performance of multi-model ensemble of RCCS was evaluated for reference and future climates. It was found that RCCS was well suited for observations and multi model ensemble of all CCS. Using the RCCS under RCP (Representative Concentration Pathway) 8.5, the future extreme precipitation was projected. As a result, the magnitude and frequency of extreme precipitation increased towards the farther future. Especially, extreme precipitation (daily maximum precipitation of 20-year return-period) during 2070-2099, was projected to occur once every 8.3-year. The RCCS employed in this study is able to successfully represent the performance of all CCS, therefore, this approach can give opportunities managing water resources efficiently for assessment of climate change impacts.

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Rapid attribution of the May/June 2016 flood-inducing precipitation in France and Germany to climate change

10.5194/hess-2016-308 ◽

2016 ◽

Cited By ~ 16

Author(s):

Geert Jan van Oldenborgh ◽

Sjoukje Philip ◽

Emma Aalbers ◽

Robert Vautard ◽

Friederike Otto ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Extreme Rainfall ◽

Return Time ◽

Anthropogenic Climate Change ◽

Positive Trend ◽

Southern Germany ◽

River Flooding ◽

The One

Abstract. The extreme precipitation that would result in historic flooding across areas of northeastern France and southern Germany began on May 26th when a large cut-off low spurred the development of several slow moving low pressure disturbances. The precipitation took different forms in each country. Warm and humid air from the south fueled sustained, large-scale, heavy rainfall over France resulting in significant river flooding on the Seine and Loire (and their tributaries), whereas the rain came from smaller clusters of intense thunderstorms in Germany triggering flash floods in mountainous areas. The floods left tens of thousands without power, caused over a billion Euros in damage in France alone, and are reported to have killed at least 18 people in Germany, France, Romania, and Belgium. The extreme nature of this event left many asking whether anthropogenic climate change may have played a role. To answer this question objectively, a rapid attribution analysis was performed in near-real time, using the best available observational data and climate models. In this rapid attribution study, where results were completed and released to the public in one week and an additional week to finalise this article, we present a first estimate of how anthropogenic climate change affected the likelihood of meteorological variables corresponding to the event, 3-day precipitation averaged over the Seine and Loire basins and the spatial maximum of 1-day precipitation over southern Germany (excluding the Alps). We find that the precipitation in the Seine basin was very rare in April–June, with a return time of hundreds of years in this season. It was less rare on the Loire, roughly 1 in 50 years in April–June. At a given location the return times for 1-day precipitation as heavy as the highest observed in southern Germany is 1 in 3000 years in April–June. This translates to once roughly every 20 years somewhere in this region and season. The probability of 3-day extreme rainfall in this season has increased by about a factor 2.3 (> 1.6) on the Seine a factor 2.0 (> 1.4) on the Loire, with all four climate models that simulated the statistical properties of the extremes agreeing. The observed trend of heavy 1-day precipitation in southern Germany is significantly negative, whereas the one model that has the correct distribution simulates a significant positive trend, making an attribution statement for these thunderstorms impossible at this time.

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