Calibrating large-ensemble European climate projections using observational data

In this study methods of calibrating the output of large single model ensembles are examined. The methods broadly involve fitting seasonal ensemble data to observations over a reference period and scaling the ensemble signal and spread so as to optimize the fit over the reference period. These calibration methods are then applied to the future (or out-of-sample) projections. The calibration methods are tested and give indistinguishable results so the simplest of these methods, namely Homogenous Gaussian Regression, is selected. An extension to this method, applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components), is found to improve the reliability of the calibrated projections. The calibration methods were tested and verified using an &#8220;imperfect model&#8221; approach using the historical/RCP8.5 simulations from the CMIP5 archive. The verification indicates that this relatively straight-forward calibration produces more reliable and accurate projections than the uncalibrated (bias-corrected) ensemble for projections of future climate over Europe. When the two large ensembles are applied to observational data, the 2041-2060 climate projections for Europe for the RCP 8.5 scenario are more consistent between the two ensembles, with a slight reduction in warming but an increase in the uncertainty of the projected changes.&#160;

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Calibrating large-ensemble European climate projections using observational data

10.5194/esd-2020-6 ◽

2020 ◽

Author(s):

Christopher H. O'Reilly ◽

Daniel J. Befort ◽

Antje Weisheimer

Keyword(s):

Observational Data ◽

Regional Climate ◽

Calibration Method ◽

Climate Projections ◽

Large Ensemble ◽

Calibration Methods ◽

Out Of Sample ◽

Large Ensembles ◽

Projected Changes ◽

Gaussian Regression

Abstract. This study examines methods of calibrating projections of future regional climate using large single model ensembles (the CESM Large Ensemble and MPI Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an imperfect model approach using the historical/RCP 8.5 simulations from the CMIP5 archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely Homogeneous Gaussian Regression, is used for the subsequent analysis. An extension to this method – applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components) – is also tested. The verification indicates that this calibration method produces more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.

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Calibrating large-ensemble European climate projections using observational data

Earth System Dynamics ◽

10.5194/esd-11-1033-2020 ◽

2020 ◽

Vol 11 (4) ◽

pp. 1033-1049

Author(s):

Christopher H. O'Reilly ◽

Daniel J. Befort ◽

Antje Weisheimer

Keyword(s):

Observational Data ◽

Regional Climate ◽

Coupled Model ◽

Calibration Method ◽

Climate Projections ◽

Max Planck ◽

Large Ensemble ◽

Calibration Methods ◽

Projected Changes ◽

Model Approach

Abstract. This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an “imperfect model” approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.

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Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing

10.5194/esd-2018-51 ◽

2018 ◽

Cited By ~ 2

Author(s):

Gab Abramowitz ◽

Nadja Herger ◽

Ethan Gutmann ◽

Dorit Hammerling ◽

Reto Knutti ◽

...

Keyword(s):

Ad Hoc ◽

Model Performance ◽

Coupled Model ◽

Data Sets ◽

Model Code ◽

Model Dependence ◽

Out Of Sample ◽

Large Ensembles ◽

Sample Testing ◽

Model Ensembles

Abstract. The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates, so that a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognized by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.

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Selecting a climate model subset to optimise key ensemble properties

Earth System Dynamics ◽

10.5194/esd-9-135-2018 ◽

2018 ◽

Vol 9 (1) ◽

pp. 135-151 ◽

Cited By ~ 39

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.

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Selecting a climate model subset to optimise key ensemble properties

10.5194/esd-2017-28 ◽

2017 ◽

Cited By ~ 1

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.

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ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing

Earth System Dynamics ◽

10.5194/esd-10-91-2019 ◽

2019 ◽

Vol 10 (1) ◽

pp. 91-105 ◽

Cited By ~ 22

Author(s):

Gab Abramowitz ◽

Nadja Herger ◽

Ethan Gutmann ◽

Dorit Hammerling ◽

Reto Knutti ◽

...

Keyword(s):

Ad Hoc ◽

Model Performance ◽

Coupled Model ◽

Data Sets ◽

Model Code ◽

Model Dependence ◽

Out Of Sample ◽

Large Ensembles ◽

Sample Testing ◽

Model Ensembles

Abstract. The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates; therefore, a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near-duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognised by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.

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More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century

Nature Communications ◽

10.1038/s41467-020-20635-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Nicola Maher ◽

Scott B. Power ◽

Jochem Marotzke

Keyword(s):

Temporal Variability ◽

Internal Variability ◽

Forced Response ◽

Climate Projections ◽

Temperature And Precipitation ◽

Large Ensembles ◽

Model Ensembles ◽

Climatic Responses ◽

Precipitation Changes ◽

Accurate Quantification

AbstractSeparating how model-to-model differences in the forced response (UMD) and internal variability (UIV) contribute to the uncertainty in climate projections is important, but challenging. Reducing UMD increases confidence in projections, while UIV characterises the range of possible futures that might occur purely by chance. Separating these uncertainties is limited in traditional multi-model ensembles because most models have only a small number of realisations; furthermore, some models are not independent. Here, we use six largely independent single model initial-condition large ensembles to separate the contributions of UMD and UIV in projecting 21st-century changes of temperature, precipitation, and their temporal variability under strong forcing (RCP8.5). We provide a method that produces similar results using traditional multi-model archives. While UMD is larger than UIV for both temperature and precipitation changes, UIV is larger than UMD for the changes in temporal variability of both temperature and precipitation, between 20° and 80° latitude in both hemispheres. Over large regions and for all variables considered here except temporal temperature variability, models agree on the sign of the forced response whereas they disagree widely on the magnitude. Our separation method can readily be extended to other climate variables.

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Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136760 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6760

Author(s):

Isaac Kwesi Nooni ◽

Daniel Fiifi T. Hagan ◽

Guojie Wang ◽

Waheed Ullah ◽

Jiao Lu ◽

...

Keyword(s):

Extreme Events ◽

Coupled Model ◽

Baseline Period ◽

Model Intercomparison ◽

Additional Information ◽

Intercomparison Project ◽

Key Drivers ◽

Model Ensembles ◽

Projected Changes ◽

Near Future

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

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Mediterranean climate change projections: an update from CMIP5 and CMIP6.

10.5194/egusphere-egu21-12803 ◽

2021 ◽

Author(s):

Josep Cos ◽

Francisco J Doblas-Reyes ◽

Martin Jury

Keyword(s):

Climate Change ◽

21St Century ◽

Hot Spot ◽

Weighting Scheme ◽

Temperature And Precipitation ◽

High Emission ◽

The Mediterranean ◽

Model Ensembles ◽

Projected Changes ◽

Model Weighting

The Mediterranean has been identified as a climate change hot-spot due to increased warming trends and precipitation decline. Recently, CMIP6 was found to show a higher climate sensitivity than its predecessor CMIP5, potentially further exacerbating related impacts on the Mediterranean region.To estimate the impacts of the ongoing climate change on the region, we compare projections of various CMIP5 and CMIP6 experiments and scenarios. In particular, we focus on summer and winter changes in temperature and precipitation for the 21st century under RCP2.6/SSP1-2.6, RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5 as well as the high resolution HighResMIP experiments. Additionally, to give robust estimates of projected changes we apply a novel model weighting scheme, accounting for historical performance and inter-independence of the multi-member multi-model ensembles, using ERA5, JRA55 and WFDE5 as observational reference.&#160;Our results indicate a significant and robust warming over the Mediterranean during the 21st century irrespective of the used ensemble and experiments. Nevertheless, the often attested amplified Mediterranean warming is only found for summer. The projected changes vary between the CMIP5 and CMIP6, with the latter projecting a stronger warming. For the high emission scenarios and without weighting, CMIP5 indicates a warming between 4 and 7.7&#186;C in summer and 2.7 and 5&#186;C in winter, while CMIP6 projects temperature increases between 5.6 and 9.2&#186;C in summer and 3.2 to 6.8&#186;C in winter until 2081-2100 in respect to 1985-2005. In contrast to temperature, precipitation changes show a higher level of uncertainty and spatial heterogeneity. However, for the high emission scenario, a robust decline in precipitation is projected for large parts of the Mediterranean during summer. First results applying the model weighting scheme indicate reductions in CMIP6 and increases in CMIP5 warming trends, thereby reducing differences between the two ensembles.

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Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation

10.5194/egusphere-egu21-1889 ◽

2021 ◽

Author(s):

Anna Ukkola ◽

Martin De Kauwe ◽

Michael Roderick ◽

Gab Abramowitz ◽

Andy Pitman

Keyword(s):

Climate Models ◽

Water Security ◽

Meteorological Drought ◽

Resource Planning ◽

Climate Projections ◽

Land Area ◽

Natural Ecosystems ◽

Drought Duration ◽

Drought Intensity ◽

Projected Changes

Understanding how climate change affects droughts guides adaptation planning in agriculture, water security, and ecosystem management. Earlier climate projections have highlighted high uncertainty in future drought projections, hindering effective planning. We use the latest CMIP6 projections and find more robust projections of meteorological drought compared to mean precipitation. We find coherent projected changes in seasonal drought duration and frequency (robust over >45% of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of the land area). Future drought changes are larger and more consistent in CMIP6 compared to CMIP5. We find regionalised increases and decreases in drought duration and frequency that are driven by changes in both precipitation mean and variability. Conversely, drought intensity increases over most regions but is not simulated well historically by the climate models. These more robust projections of meteorological drought in CMIP6 provide clearer direction for water resource planning and the identification of agricultural and natural ecosystems at risk.

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