| 1. |
|
|
| 2. |
- Fiedler, Stephanie, et al.
(författare)
-
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
- 2024
-
Ingår i: Geoscientific Model Development. - 1991-959X .- 1991-9603. ; 17:6, s. 2387-2417
-
Forskningsöversikt (refereegranskat)abstract
- The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts.
|
|
| 3. |
- Rowlinson, Matthew J., et al.
(författare)
-
Tropospheric ozone radiative forcing uncertainty due to pre-industrial fire and biogenic emissions
- 2020
-
Ingår i: Atmospheric Chemistry and Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 20:18, s. 10937-10951
-
Tidskriftsartikel (refereegranskat)abstract
- pTropospheric ozone concentrations are sensitive to natural emissions of precursor compounds. In contrast to existing assumptions, recent evidence indicates that terrestrial vegetation emissions in the pre-industrial era were larger than in the present day. We use a chemical transport model and a radiative transfer model to show that revised inventories of pre-industrial fire and biogenic emissions lead to an increase in simulated pre-industrial ozone concentrations, decreasing the estimated pre-industrial to present-day tropospheric ozone radiative forcing by up to 34 % (0.38 to 0.25 W mspan classCombining double low line"inline-formula"-2/span). We find that this change is sensitive to employing biomass burning and biogenic emissions inventories based on matching vegetation patterns, as the co-location of emission sources enhances the effect on ozone formation. Our forcing estimates are at the lower end of existing uncertainty range estimates (0.2-0.6 W mspan classCombining double low line"inline-formula"-2/span), without accounting for other sources of uncertainty. Thus, future work should focus on reassessing the uncertainty range of tropospheric ozone radiative forcing.
|
|
| 4. |
- Smith, Christopher J., et al.
(författare)
-
Effective radiative forcing and adjustments in CMIP6 models
- 2020
-
Ingår i: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 20:16, s. 9591-9618
-
Tidskriftsartikel (refereegranskat)abstract
- The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (+/- 0.23) W m(-2), comprised of 1.81 (+/- 0.09) Wm(-2) from CO2, 1.08 (+/- 0.21) Wm(-2) from other well-mixed greenhouse gases, -1.01 (+/- 0.23) W m(-2) from aerosols and -0.09 (+/- 0.13) W m(-2) from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m(-2). The majority of the remaining 0.21 W m(-2) is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol-cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from -0.63 to -1.37 W m(-2), exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4 x CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.
|
|
