scholarly journals Selecting a climate model subset to optimise key ensemble properties

2018 ◽  
Vol 9 (1) ◽  
pp. 135-151 ◽  
Author(s):  
Nadja Herger ◽  
Gab Abramowitz ◽  
Reto Knutti ◽  
Oliver Angélil ◽  
Karsten Lehmann ◽  
...  
Keyword(s):  
Climate Model ◽  
Climate Projections ◽  
Ensemble Spread ◽  
Flexible Tool ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Weighted Model ◽  
Climate Model Simulations ◽  
Model Ensembles ◽  
Processing Steps

Abstract. End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

scholarly journals Selecting a climate model subset to optimise key ensemble properties

2017 ◽  
Author(s):  
Nadja Herger ◽  
Gab Abramowitz ◽  
Reto Knutti ◽  
Oliver Angélil ◽  
Karsten Lehmann ◽  
...  
Keyword(s):  
Climate Model ◽  
Climate Projections ◽  
Ensemble Spread ◽  
Flexible Tool ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Weighted Model ◽  
Climate Model Simulations ◽  
Model Ensembles ◽  
Processing Steps

Abstract. End-users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally-weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product and pre-processing steps used.

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

scholarly journals Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

2019 ◽  
Vol 23 (3) ◽  
pp. 1741-1749
Author(s):  
Jan Hnilica ◽  
Martin Hanel ◽  
Vladimír Puš
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Dependence Structure ◽  
Climate Projections ◽  
Model Simulations ◽  
Correlation Structures ◽  
Climate Model Simulations ◽  
Dependence Structures

Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.

An overview of the EUCP project – towards improved European Climate Predictions and Projections

2020 ◽  
Author(s):  
Jason A. Lowe ◽  
Carol McSweeney ◽  
Chris Hewitt
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Public And Private ◽  
The People ◽  
Local Actors ◽  
Model Ensembles ◽  
Public And Private Sector

<p>There is clear evidence that, even with the most favourable emission pathways over coming decades, there will be a need for society to adapt to the impacts of climate variability and change. To do this regional, national and local actors need up-to-date information on the changing climate with clear accompanying detail on the robustness of the information. This needs to be communicated to both public and private sector organisations, ideally as part of a process of co-developing solutions.</p><p>EUCP is an H2020 programme that began in December 2017 with the aim of researching and testing the provision of improved climate predictions and projections for Europe for the next 40+ years, and drawing on the expertise of researchers from a number of major climate research institutes across Europe. It is also engaging with users of climate change information through a multiuser forum (MUF) to ensure that what we learn will match the needs of the people who need if for decision making and planning.</p><p>The first big issue that EUCP seeks to address is how better to use ensembles of climate model projections, moving beyond the one-model-one-vote philosophy. Here, the aim is to better understand how model ensembles might be constrained or sub-selected, and how multiple strands of information might be combined into improved climate change narratives or storylines. The second area where EUCP is making progress is in the use of very high-resolution regional climate simulations that are capable of resolving aspects of atmospheric convection. Present day and future simulations from a new generation of regional models ae being analysed in EUCP and will be used in a number of relevant case studies. The third issue that EUCP will consider is how to make future simulations more seamless across those time scales that are most relevant user decision making. This includes generating a better understanding of predictability over time and its sources in initialised forecasts, and also how to transition from the initialised forecasts to longer term boundary forced climate projections.</p><p>This presentation will provide an overview of the challenges being addressed by EUCP and the approaches the project is using.</p><p><br><br></p><p> </p>

Quantifying uncertainty in projections of future European climate: a multi-model multi-method approach

2020 ◽  
Author(s):  
Lukas Brunner ◽  
Carol McSweeney ◽  
Daniel Befort ◽  
Chris O'Reilly ◽  
Ben Booth ◽  
...  
Keyword(s):  
Climate Model ◽  
Probability Distributions ◽  
Internal Variability ◽  
Precipitation Change ◽  
Future Climate Change ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
European Climate ◽  
Climate Model Simulations ◽  
Quantifying Uncertainty

<p>Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. Different approaches to constrain, filter, or weight climate model simulations into probabilistic projections have been proposed to provide such estimates. Here six methods are applied to European climate projections using a consistent framework in order to allow a quantitative comparison.  Focus is given to summer temperature and precipitation change in three different spatial regimes in Europe in the period 2041-2060 relative to 1995-2014. The analysis draws on projections from several large initial condition ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5.  <br>The methods included are diverse in their approach to quantifying uncertainty, and include those which apply weighting schemes based on baseline performance and inter-model relationships, so-called ASK (Allen, Stott and Kettleborough) techniques which use optimal fingerprinting to scale the scale the response to external forcings, to those found in observations and Bayesian approaches to estimating probability distributions. Some of the key differences between methods are the uncertainties covered, the treatment of internal variability, and variables and regions used to inform the methods. In spite of these considerable methodological differences, the median projection from the multi-model methods agree on a statistically significant increase in temperature by mid-century by about 2.5°C in the European average. The estimates of spread, in contrast, differ substantially between methods. Part of this large difference in the spread reflects the fact that different methods attempt to capture different sources of uncertainty, and some are more comprehensive in this respect than others. This study, therefore, highlights the importance of providing clear context about how different methods affect the distribution of projections, particularly the in the upper and lower percentiles that are of interest to 'risk averse' stakeholders. Methods find less agreement in precipitation change with most methods indicating a slight increase in northern Europe and a drying in the central and Mediterranean regions, but with considerably different amplitudes. Further work is needed to understand how the underlying differences between methods lead to such diverse results for precipitation. </p>

Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER

2021 ◽  
Author(s):  
Sebastian Bathiany ◽  
Diana Rechid ◽  
Susanne Pfeifer ◽  
Juliane El Zohbi ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Climate Change ◽  
Elevated Temperatures ◽  
Climate Model ◽  
Regional Climate ◽  
Weather Conditions ◽  
Climate Projections ◽  
Public Attention ◽  
Compound Events ◽  
Climate Model Simulations ◽  
The Individual

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.</p><p>In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.</p>

Issues in the interpretation of climate model ensembles to inform decisions

2007 ◽  
Vol 365 (1857) ◽  
pp. 2163-2177 ◽  
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.

scholarly journals Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

2015 ◽  
Vol 28 (6) ◽  
pp. 2332-2348 ◽  
Author(s):  
G. Abramowitz ◽  
C. H. Bishop
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Annual Rainfall ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Twenty First Century

Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

scholarly journals Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

2013 ◽  
Vol 13 (4) ◽  
pp. 2063-2090 ◽  
Author(s):  
P. J. Young ◽  
A. T. Archibald ◽  
K. W. Bowman ◽  
J.-F. Lamarque ◽  
V. Naik ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Model ◽  
Stratospheric Ozone ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Ensemble Mean ◽  
Intercomparison Project ◽  
High Bias ◽  
Ozone Precursor

Abstract. Present day tropospheric ozone and its changes between 1850 and 2100 are considered, analysing 15 global models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean compares well against present day observations. The seasonal cycle correlates well, except for some locations in the tropical upper troposphere. Most (75 %) of the models are encompassed with a range of global mean tropospheric ozone column estimates from satellite data, but there is a suggestion of a high bias in the Northern Hemisphere and a low bias in the Southern Hemisphere, which could indicate deficiencies with the ozone precursor emissions. Compared to the present day ensemble mean tropospheric ozone burden of 337 ± 23 Tg, the ensemble mean burden for 1850 time slice is ~30% lower. Future changes were modelled using emissions and climate projections from four Representative Concentration Pathways (RCPs). Compared to 2000, the relative changes in the ensemble mean tropospheric ozone burden in 2030 (2100) for the different RCPs are: −4% (−16%) for RCP2.6, 2% (−7%) for RCP4.5, 1% (−9%) for RCP6.0, and 7% (18%) for RCP8.5. Model agreement on the magnitude of the change is greatest for larger changes. Reductions in most precursor emissions are common across the RCPs and drive ozone decreases in all but RCP8.5, where doubled methane and a 40–150% greater stratospheric influx (estimated from a subset of models) increase ozone. While models with a high ozone burden for the present day also have high ozone burdens for the other time slices, no model consistently predicts large or small ozone changes; i.e. the magnitudes of the burdens and burden changes do not appear to be related simply, and the models are sensitive to emissions and climate changes in different ways. Spatial patterns of ozone changes are well correlated across most models, but are notably different for models without time evolving stratospheric ozone concentrations. A unified approach to ozone budget specifications and a rigorous investigation of the factors that drive tropospheric ozone is recommended to help future studies attribute ozone changes and inter-model differences more clearly.

scholarly journals Precipitation ensembles conforming to natural variations derived from Regional Climate Model using a new bias correction scheme

2015 ◽  
Vol 12 (10) ◽  
pp. 10261-10287
Author(s):  
K. B. Kim ◽  
H.-H. Kwon ◽  
D. Han
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Natural Variability ◽  
Ensemble Spread ◽  
Correction Scheme ◽  
Model Ensembles ◽  
Primary Advantage ◽  
Hadley Centre

Abstract. This study presents a novel bias correction scheme for Regional Climate Model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. Since the observation is only one case of many possible realizations due to the climate natural variability, bias correction scheme should preserve ensemble spread within the bounds of natural variability (i.e. sampling uncertainty). To demonstrate the proposed methodology, an application to the Thorverton catchment in the southwest of England is presented. For the ensemble, 11-members from the Hadley Centre Regional Climate Model (HadRM3-PPE) Data are used and monthly bias correction has been done for the baseline time period from 1961 to 1990. In the typical conventional method, monthly mean precipitation of each of the ensemble members are nearly identical to the observation, i.e. the ensemble spread is removed. In contrast, the proposed method corrects the biases while maintain ensemble spread within the natural variability of observations.

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