Contributions of aerosol‐cloud interactions to mid‐Piacenzian seasonally sea ice‐free Arctic Ocean

Feng, Ran; Otto‐Bliesner, Bette L.; Xu, Yangyang; Brady, Esther C.; Fletcher, Tamara; Ballantyne, Ashley P.

doi:10.1029/2019gl083960

Cited by 23 publications

(25 citation statements)

References 69 publications

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“…These new parameterizations in CAM5 produce a cloud simulation that agrees much better with satellite observations (Kay (Feng et al, 2019), the last glacial maximum (Zhu et al, 2017a), Heinrich events (Zhu et al, 2017b), and the last millennium (Otto-Bliesner et al, 2015;Thibodeau et al, 2018). To make the model suitable for a paleoclimate simulation with a high CO 2 level, the model code has been slightly modified to incorporate an upgrade to the radiation code that corrects the missing diffusivity angle specifications for certain longwave bands.…”

Section: Cesm Model Descriptionmentioning

confidence: 99%

DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Lunt¹,

Bragg²,

Chan³

et al. 2020

Preprint

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Abstract. We present results from an ensemble of seven climate models, each of which has carried out simulations of the early Eocene climate optimum (EECO, ~ 50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with identical paleogeographic and vegetation boundary conditions. The results indicate that these non-CO2 boundary conditions contribute between 3 and 5 °C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show reduced spread of global mean surface temperature response across the ensemble, for a given atmospheric CO2 concentration. In a marked departure from the results from previous simulations, at least two of the DeepMIP models (CESM and GFDL) are consistent with proxy indicators of global mean temperature, and atmospheric CO2, and meridional SST gradients. The best agreement with global SST proxies from these models occurs at CO2 concentrations of around 2400 ppmv. At a more regional scale the models lack skill in reproducing the proxy SSTs, in particular in the southwest Pacific, around New Zealand and south Australia, where the modelled anomalies are substantially less than indicated by the proxies. However, in these regions modelled continental surface air temperature anomalies are consistent with surface air temperature proxies, implying an inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography and aerosols.

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Section: Cesm Model Descriptionmentioning

confidence: 99%

DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Lunt¹,

Bragg²,

Chan³

et al. 2020

Preprint

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“…Lunt et al, 2012). Haywood et al (2013aHaywood et al ( , 2013b discussed the possible contributing factors to the noted discrepancies in DMC, noting three primary causal groupings: uncertainty in model boundary conditions, uncertainty in the interpretation of proxy data and uncertainty in model physics (for example, recent studies have demonstrated that this modelproxy mismatch has been reduced by including explicit aerosol-cloud interactions in the newer generations of models (Sagoo and Storelvmo 2017;Feng et al, 2019)).…”

Section: From Pliomip1 To Pliomip2mentioning

confidence: 99%

A return to large-scale features of Pliocene climate: the Pliocene Model Intercomparison Project Phase 2

Haywood

Tindall

Dowsett

et al. 2020

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Abstract. The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ~ 400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution and based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.4 and 4.7 °C relative to pre-industrial with a multi-model mean value of 2.8 °C. Annual mean total precipitation rates increase by 6 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases are 1.3 °C greater over the land than over the oceans, and there is a clear pattern of polar amplification with warming polewards of 60° N and 60° S exceeding the global mean warming by a factor of 2.4. In the Atlantic and Pacific Oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. Although there are some modelling constraints, there is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (Equilibrium Climate Sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble earth system response to doubling of CO2 (including ice sheet feedbacks) is approximately 50 % greater than ECS, consistent with results from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea-surface temperatures are used to assess model estimates of ECS and indicate a range in ECS from 2.5 to 4.3 °C. This result is in general accord with the range in ECS presented by previous IPCC Assessment Reports.

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“…This data-model discord may have been caused by uncertainties in model physics, boundary conditions, or reconstructions (Haywood et al, 2013a) Uncertainties in model physics include unconstrained forcings and uncertainties in model parameters. It was found that the inclusion of chemistry-climate feedbacks from vegetation and wildfire changes leads to substantial global warming (Unger and Yue, 2014, while excluding industrial pollutants and explicitly simulating aerosol-cloud interactions (Feng et al, 2019), and decreasing atmospheric dust loading (Sagoo and Storelvmo, 2017) leads to increased Arctic warming in mPWP simulations. Similarly, in simulations of the Eocene, two models that implemented modified aerosols had better skill than other models at representing polar amplification (Lunt et al, 2020).…”

Section: In the Present Work We Analyze The Simulated Arctic Warmingmentioning

confidence: 99%

“…It is thus likely that other sources of error contribute to the data-model discord, such as uncertainties in model physics (e.g. Feng et al, 2019;Howell et al, 2016b;Lunt et al, 2020;Sagoo and Storelvmo, 2017;Unger and Yue, 2014) and boundary conditions (e.g. Brierley and Fedorov, 2016;Feng et al, 2017Feng et al, , 2017Hill, 2015;Howell et al, 2016b;Otto-Bliesner et al, 2017;Prescott et al, 2014;Robinson et al, 2011;Salzmann et al, 2013).…”

Section: Uncertaintiesmentioning

confidence: 99%

Evaluation of Arctic warming in mid-Pliocene climate simulations

Nooijer¹,

Zhang²,

Li³

et al. 2020

Preprint

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Abstract. Palaeoclimate simulations improve our understanding of the climate, inform us about the performance of climate models in a different climate scenario, and help to identify robust features of the climate system. Here, we analyse Arctic warming in an ensemble of 16 simulations of the mid-Pliocene Warm Period (mPWP), derived from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The PlioMIP2 ensemble simulates Arctic (60–90° N) annual mean surface air temperature (SAT) increases of 3.7 to 11.6 °C compared to the pre-industrial, with a multi-model mean (MMM) increase of 7.2 °C. The Arctic warming amplification ratio relative to global SAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea ice extent anomalies range from −3.0 to −10.4 × 06 km2 with a MMM anomaly of −5.6 × 106 km2, which constitutes a decrease of 53 % compared to the pre-industrial. The majority (11 out of 16) models simulate summer sea ice-free conditions (≤ 1 × 06 km2) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions. The simulations with the highest Arctic SAT anomalies tend to match the proxy dataset in its current form better. The ensemble shows some agreement with reconstructions of sea ice, particularly with regards to seasonal sea ice. Large uncertainties limit the confidence that can be placed in the findings and the compatibility of the different proxy datasets. We show that, while reducing uncertainties in the reconstructions could decrease the SAT data-model discord substantially, further improvements are likely to be found in enhanced boundary conditions or model physics. Lastly, we compare the Arctic warming in the mPWP to projections of future Arctic warming and find that the PlioMIP2 ensemble simulates greater Arctic amplification, an increase instead of a decrease in AMOC strength compared to pre-industrial, and a lesser strengthening of northern modes of variability than CMIP5 future climate simulations. The results highlight the importance of slow feedbacks in equilibrium climate simulations, and that caution must be taken when using simulations of the mPWP as an analogue for future climate change.

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Contributions of aerosol‐cloud interactions to mid‐Piacenzian seasonally sea ice‐free Arctic Ocean

Cited by 23 publications

References 69 publications

DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

A return to large-scale features of Pliocene climate: the Pliocene Model Intercomparison Project Phase 2

Evaluation of Arctic warming in mid-Pliocene climate simulations

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