scholarly journals Cluster-based ensemble means for climate model intercomparison

2018 ◽  
Author(s):  
Richard Hyde ◽  
Ryan Hossaini ◽  
Amber Leeson
Keyword(s):  
Tropospheric Ozone ◽  
Climate Model ◽  
Grid Cell ◽  
Model Output ◽  
Model Intercomparison ◽  
Simple Arithmetic ◽  
Clustering Techniques ◽  
Cell Basis ◽  
Absolute Bias ◽  
Insight Into

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally-efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) may yet to be fully realised. In this paper, we explore the specific application of clustering to the calculation of multi-model means from climate model output. A standard rudimentary approach to multi-model mean (MMM) calculation simply involves taking the arithmetic mean of all models in a given ensemble, over a particular space/time domain (a one model one vote approach). We hypothesise that clustering can provide a useful data-driven method of (a) excluding poor model data from MMM calculations, on a grid-cell basis, thus (b) maximising retention of good data, and avoiding the blanket exclusion of models, where appropriate. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Cluster-based MMM fields of tropospheric column ozone were generated from the ACCMIP ensemble using the Data Density based Clustering (DDC) algorithm. The cluster-based MMM was compared to the simple arithmetic MMM (one model one vote approach) and each MMM was evaluated against an observed satellite-based tropospheric ozone climatology, as used in the original ACCMIP study. As a proof of concept, we show the proposed clustering technique can offer improvement in terms of reducing the absolute bias between the MMMs and observations. For example, the global mean absolute bias from the cluster-based MMM is reduced in all months, up to ~ 15 %, compared to the simple arithmetic MMM. On a grid-cell basis, the bias is reduced at more than 60 % of all locations. Some locations are found to be unaffected by the clustering process, while in others the bias increases, albeit slightly. This and other caveats of the clustering techniques are discussed. Finally, while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement between models and a qualitative measure of diversity across an ensemble.

scholarly journals Cluster-based analysis of multi-model climate ensembles

2018 ◽  
Vol 11 (6) ◽  
pp. 2033-2048 ◽  
Author(s):  
Richard Hyde ◽  
Ryan Hossaini ◽  
Amber A. Leeson
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Models ◽  
Climate Model ◽  
Complex Data ◽  
Similar Data ◽  
Climate Data ◽  
Efficient Manner ◽  
Clustering Techniques ◽  
Insight Into

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

scholarly journals Observations for Model Intercomparison Project (Obs4MIPs): Status for CMIP6

2019 ◽  
Author(s):  
Duane Waliser ◽  
Peter J. Gleckler ◽  
Robert Ferraro ◽  
Karl E. Taylor ◽  
Sasha Ames ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Climate Model ◽  
Surface Wind ◽  
Ocean Surface ◽  
Shortwave Radiation ◽  
Model Output ◽  
Earth System ◽  
Model Intercomparison ◽  
Wide Range ◽  
Intercomparison Project

Abstract. The Observations for Model Intercomparison Projects (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs: 1) targets observed variables that can be compared to CMIP model variables, 2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output, 3) provides brief technical documentation for each dataset, designed for non-experts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits and limitations, and 4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80, many undergoing updates, with another 20 or so in preparation, and more than 100 proposed and under consideration. Current global satellite-based datasets include, but are not limited to, humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top of atmosphere longwave and shortwave radiation; along with ozone (O3), methane (CH4) and carbon dioxide (CO2) products. Proposed products expected for inclusion for CMIP6 analysis include, but are not limited to, alternative products for the above quantities, along with additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g. FAPAR, LAI, burnt area fraction), ice sheet mass and height, carbon monoxide (CO) and nitrogen dioxide (NO2). While most obs4MIPs datasets are delivered as monthly and global, greater emphasis is being places on products with higher time resolution (e.g. daily) and/or regional products. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including: 1) an updated obs4MIPs data specifications document that provides for additional search facets and generally improves congruence with CMIP6 specifications for model datasets, 2) a set of six easily understood indicators that help guide users as to a dataset’s maturity and suitability for application, and 3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in-situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth System models used in CMIP.

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 The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations

2016 ◽  
Vol 9 (9) ◽  
pp. 3427-3446 ◽  
Author(s):  
Dirk Notz ◽  
Alexandra Jahn ◽  
Marika Holland ◽  
Elizabeth Hunke ◽  
François Massonnet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Heat Budget ◽  
Model Output ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations ◽  
Ice Model

Abstract. A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed Sea-Ice Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget, and the mass budget. In this contribution, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.

scholarly journals Tropospheric ozone changes, radiative forcing and attribution to emissions in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

2013 ◽  
Vol 13 (6) ◽  
pp. 3063-3085 ◽  
Author(s):  
D. S. Stevenson ◽  
P. J. Young ◽  
V. Naik ◽  
J.-F. Lamarque ◽  
D. T. Shindell ◽  
...  
Keyword(s):  
Climate Change ◽  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Model ◽  
Anthropogenic Emissions ◽  
Model Intercomparison ◽  
A Value ◽  
Intercomparison Project ◽  
Column Ozone ◽  
Global Mean

Abstract. Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (±1 standard deviation) in RFs of ±17%. Three different radiation schemes were used – we find differences in RFs between schemes (for the same ozone fields) of ±10%. Applying two different tropopause definitions gives differences in RFs of ±3%. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of ±30% for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44±12%), nitrogen oxides (31 ± 9%), carbon monoxide (15 ± 3%) and non-methane volatile organic compounds (9 ± 2%); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m−2 DU−1, a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m−2; relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

scholarly journals Observational constraints on ozone radiative forcing from the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP)

2012 ◽  
Vol 12 (9) ◽  
pp. 23603-23644 ◽  
Author(s):  
K. Bowman ◽  
D. Shindell ◽  
H. Worden ◽  
J. F. Lamarque ◽  
P. J. Young ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Longwave Radiation ◽  
Model Intercomparison ◽  
Ensemble Mean ◽  
Intercomparison Project

Abstract. We use simultaneous observations of ozone and outgoing longwave radiation (OLR) from the Tropospheric Emission Spectrometer (TES) to evaluate ozone distributions and radiative forcing simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES for 2005–2010. These biases lead to substantial differences in ozone instantaneous radiative forcing between TES and the ACCMIP simulations. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean has a low bias in the SH tropics of up to 100 m W m−2 locally and a global low bias of 35 ± 44 m W m−2 relative to TES. Combining ACCMIP preindustrial ozone and the TES present-day ozone, we calculate an observationally constrained estimate of tropospheric ozone radiative forcing (RF) of 399 ± 70 m W m−2, which is about 7% higher than using the ACCMIP models alone but with the same standard deviation (Stevenson et al., 2012). In addition, we explore an alternate approach to constraining radiative forcing estimates by choosing a subset of models that best match TES ozone, which leads to an ozone RF of 369 ± 42 m W m−2. This estimate is closer to the ACCMIP ensemble mean RF but about a 40% reduction in standard deviation. These results point towards a profitable direction of combining observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

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

2012 ◽  
Vol 12 (8) ◽  
pp. 21615-21677 ◽  
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 ◽  
Intercomparison Project ◽  
High Bias ◽  
Ozone Column ◽  
Global Mean

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 multi-model 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, although there is a suggestion of a high bias in the Northern Hemisphere and a low bias in the Southern Hemisphere. Compared to the present day multi-model mean tropospheric ozone burden of 337 Tg, the multi-model 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 for the tropospheric ozone burden in 2030 (2100) for the different RCPs are: −5% (−22%) for RCP2.6, 3% (−8%) for RCP4.5, 0% (−9%) for RCP6.0, and 5% (15%) for RCP8.5. Model agreement on the magnitude of the change is greatest for larger changes. Reductions in precursor emissions are common across the RCPs and drive ozone decreases in all but RCP8.5, where doubled methane and a larger stratospheric influx increase ozone. Models with high ozone abundances for the present day also have high ozone levels for the other time slices, but there are no models consistently predicting large or small changes. 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 is recommended to help future studies attribute ozone changes and inter-model differences more clearly.

scholarly journals Tropospheric ozone in CMIP6 simulations

2021 ◽  
Vol 21 (5) ◽  
pp. 4187-4218
Author(s):  
Paul T. Griffiths ◽  
Lee T. Murray ◽  
Guang Zeng ◽  
Youngsub Matthew Shin ◽  
N. Luke Abraham ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Models ◽  
Climate Model ◽  
Decadal Variability ◽  
Stratospheric Ozone ◽  
Ozone Production ◽  
Model Intercomparison ◽  
Chemical Production ◽  
Intercomparison Project

Abstract. The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled atmosphere–ocean chemistry–climate models, focusing on the CMIP Historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric-ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde and satellite observations of the past several decades and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The multi-model mean tropospheric-ozone burden increases from 247 ± 36 Tg in 1850 to a mean value of 356 ± 31 Tg for the period 2005–2014, an increase of 44 %. Modelled present-day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP: 337 ± 23 Tg; Tropospheric Ozone Assessment Report, TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden increases to 416 ± 35 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production (PO3) and loss (LO3) diagnostics. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production (PO3-LO3) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere–troposphere transport reaches a minimum around the same time before recovering in the 21st century, while dry deposition increases steadily over the period 1850–2100. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.

scholarly journals Sea Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations

2016 ◽  
Author(s):  
Dirk Notz ◽  
Alexandra Jahn ◽  
Marika Holland ◽  
Elizabeth Hunke ◽  
François Massonnet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Heat Budget ◽  
Model Output ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations ◽  
Ice Model

Abstract. A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic CMIP6 Sea-Ice Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice related variables from climate-model simulations that allow for a better understanding, and ultimately improvement, of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget and the mass budget. In this contribution, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.

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