The use of the multi-model ensemble in probabilistic climate projections

2007 ◽  
Vol 365 (1857) ◽  
pp. 2053-2075 ◽  
Author(s):  
Claudia Tebaldi ◽  
Reto Knutti
Keyword(s):  
Climate Models ◽  
Future Climate ◽  
Climate Projections ◽  
Model Design ◽  
Model Ensemble ◽  
Making Sense ◽  
Probabilistic Climate Projections ◽  
Regional Temperature ◽  
Structural Uncertainties ◽  
Model Ensembles

Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an ‘ensemble of opportunity’, are discussed in detail.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
Future Climate Projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

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

2016 ◽  
Vol 11 (1s) ◽  
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.

Soil moisture and the water cycle in the UK Climate Projections

2020 ◽  
Author(s):  
Jennifer Pirret ◽  
Fai Fung ◽  
John. F.B. Mitchell ◽  
Rachel McInnes
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
Water Balance ◽  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Plant Cultivation

<p>Soil moisture is a key environmental factor for plant cultivation: too little and plant growth is restricted due to drought conditions; too much and soil becomes water-logged. It is important to understand how well climate models can represent current soil moisture processes as well as how soil moisture will respond to a changing climate, to inform adaptation of plant cultivation to future climate change. We explore current and future climate soil moisture conditions alongside water cycle processes such as evaporation and run-off in the latest UK Climate Projections (UKCP). Three model ensembles are available: UKCP Global, Regional and Local, with horizontal resolutions of 60km, 12km and 2.2km respectively. These each contain the Joint UK Land Environment Simulator (JULES) model as their land surface component. This suite of models offers the opportunity to understand the effects of parameter uncertainty and spatial resolution. Firstly, we assess the performance of the Global and Regional simulations by evaluating results from the baseline period (1981-2010) in terms of soil moisture (and the overall water balance) by comparing it to observations and to JULES driven by observations. Secondly, we assess how the water balance responds to a high future greenhouse gas concentration pathway. We find that soil moisture is likely to be lower in the summer and early autumn and spends a longer time below levels optimal for plant growth. The potential drivers of this change are explored, including future changes in precipitation and evaporation.</p>

scholarly journals Towards an objective assessment of climate multi-model ensembles. A case study in the Senegalo-Mauritanian upwelling region

2019 ◽  
Author(s):  
Juliette Mignot ◽  
Carlos Mejia ◽  
Charles Sorror ◽  
Adama Sylla ◽  
Michel Crépon ◽  
...  
Keyword(s):  
Climate Models ◽  
Numerical Models ◽  
Objective Assessment ◽  
Model Ensemble ◽  
Self Organizing Maps ◽  
Climate Simulations ◽  
Mean Sea Surface ◽  
The Mean ◽  
Model Ensembles

Abstract. Climate simulations require very complex numerical models. Unfortunately, they typically present biases due to parameterizations, choices of numerical schemes, and the complexity of many physical processes. Beyond improving the models themselves, a way to improve the performance of the modeled climate is to consider multi-model averages. Here, we propose an objective method to select the models that yield an efficient multi-model ensemble average. We used a neural classifier (Self-Organizing Maps), associated with a multi-correspondence analysis to identify the models that best represent some target climate property. One can then determine an efficient multi-model ensemble. We illustrate the methodology with results focusing on the mean sea surface temperature seasonal cycle over the Senegalo-Mauritanian region. We compare 47 CMIP5 model configurations to available observations. The method allowed us to identify a performing multi-model ensemble by averaging 12 climate models only. Future behavior of the Senegalo-Mauritanian upwelling was then assessed using this multi-model ensemble.

scholarly journals Can we trust empirical marine DMS parameterisations within projections of future climate?

2010 ◽  
Vol 7 (5) ◽  
pp. 1645-1656 ◽  
Author(s):  
P. R. Halloran ◽  
T. G. Bell ◽  
I. J. Totterdell
Keyword(s):  
Climate Models ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Feedback ◽  
Climate Projections ◽  
Earth System ◽  
Aerosol Formation ◽  
System Models ◽  
Annual Variability ◽  
Earth System Models

Abstract. Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted inter-annual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed inter-annual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high inter-annual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed.

Probabilistic climate projections with Minimal CMIP Emulator (MCE)

2021 ◽  
Author(s):  
Junichi Tsutsui
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Model Parameters ◽  
Climate Projections ◽  
Second Phase ◽  
Impulse Response Functions ◽  
Model Intercomparison ◽  
Probabilistic Climate Projections ◽  
Intercomparison Project

<p>One of the key applications of simple climate models is probabilistic climate projections to assess a variety of emission scenarios in terms of their compatibility with global warming mitigation goals. The second phase of the Reduced Complexity Model Intercomparison Project (RCMIP) compares nine participating models for their probabilistic projection methods through scenario experiments, focusing on consistency with given constraints for climate indicators including radiative forcing, carbon budget, warming trends, and climate sensitivity. The MCE is one of the nine models, recently developed by the author, and has produced results that well match the ranges of the constraints. The model is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A parameter subset associated with CO<sub>2</sub>-induced warming is assured to have a covariance structure as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). The model's simplicity and the successful results imply that a method with less complicated structures and fewer control parameters has an advantage when building reasonable perturbed ensembles in a transparent way despite less capacity to emulate detailed Earth system components. Experimental results for future scenarios show that the climate sensitivity of CMIP models is overestimated overall, suggesting that probabilistic climate projections need to be constrained with observed warming trends.</p>

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

2012 ◽  
Vol 370 (1962) ◽  
pp. 1121-1139 ◽  
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.

scholarly journals Limited impact of climate forcing products on future glacier evolution in Scandinavia and Iceland

2021 ◽  
pp. 1-17
Author(s):  
Loris Compagno ◽  
Harry Zekollari ◽  
Matthias Huss ◽  
Daniel Farinotti
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Climate Forcing ◽  
Climate Projections ◽  
Climate Data ◽  
Surface Mass ◽  
Future Evolution ◽  
The Future ◽  
Data Products

Abstract Due to climate change, worldwide glaciers are rapidly declining. The trend will continue into the future, with consequences for sea level, water availability and tourism. Here, we assess the future evolution of all glaciers in Scandinavia and Iceland until 2100 using the coupled surface mass-balance ice-flow model GloGEMflow. The model is initialised with three distinct past climate data products (E-OBS, ERA-I, ERA-5), while future climate is prescribed by both global and regional climate models (GCMs and RCMs), in order to analyze their impact on glacier evolution. By 2100, we project Scandinavian glaciers to lose between 67 ± 18% and 90 ± 7% of their present-day (2018) volume under a low (RCP2.6) and a high (RCP8.5) emission scenario, respectively. Over the same period, losses for Icelandic glaciers are projected to be between 43 ± 11% (RCP2.6) and 85 ± 7% (RCP8.5). The projected evolution is only little impacted by both the choice of climate data products used in the past and the spatial resolution of the future climate projections, with differences in the ice volume remaining by 2100 of 7 and 5%, respectively. This small sensitivity is attributed to our model calibration strategy that relies on observed glacier-specific mass balances and thus compensates for differences between climate forcing products.

scholarly journals Combining Global Climate Models Using Graph Cuts

2021 ◽  
Author(s):  
Soulivanh Thao ◽  
Mats Garvik ◽  
Grégoire Mariethoz ◽  
Mathieu Vrac
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Grid Point ◽  
Graph Cuts ◽  
Global Climate Models ◽  
Climate Projections ◽  
Model Combination ◽  
Spatial Continuity ◽  
Spatial Consistency ◽  
Model Ensembles

Abstract Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity.

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