Implementation of the CMIP6 Forcing Data in the IPSL‐CM6A‐LR Model

Lurton, Thibaut; Balkanski, Yves; Bastrikov, Vladislav; Bekki, Slimane; Bopp, Laurent; Braconnot, Pascale; Brockmann, Patrick; Cadule, Patricia; Contoux, Camille; Cozic, Anne; Cugnet, David; Dufresne, Jean-Louis; Éthé, Christian; Foujols, Marie-Alice; Ghattas, Joséfine; Hauglustaine, D.; Hu, Rong‐Ming; Kageyama, Masa; Khodri, Myriam; Lebas, Nicolas; Levavasseur, Guillaume; Marchand, Marion; Ottlé, Catherine; Peylin, Philippe; Sima, Adriana; Szopa, Sophie; Thiéblemont, Rémi; Vuichard, Nicolas; Boucher, Oliviér

doi:10.1029/2019ms001940

Cited by 127 publications

(87 citation statements)

References 64 publications

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“…In South America, the regional time series shows high peaks in AOD, associated with forest fires in the late summer, whose intensity greatly varies from year to year. In Africa, a strong seasonal contrast is also found due to the transport of desert dust at altitude in the summer months (Mortier et al, 2016;Ogunjobi et al, 2008). The computation of the seasonal trends would allow characterization of the tendencies in such extreme or synoptic aerosol events.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis

et al. 2020

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Abstract. This study presents a multiparameter analysis of aerosol trends over the last 2 decades at regional and global scales. Regional time series have been computed for a set of nine optical, chemical-composition and mass aerosol properties by using the observations from several ground-based networks. From these regional time series the aerosol trends have been derived for the different regions of the world. Most of the properties related to aerosol loading exhibit negative trends, both at the surface and in the total atmospheric column. Significant decreases in aerosol optical depth (AOD) are found in Europe, North America, South America, North Africa and Asia, ranging from −1.2 % yr−1 to −3.1 % yr−1. An error and representativity analysis of the spatially and temporally limited observational data has been performed using model data subsets in order to investigate how much the observed trends represent the actual trends happening in the regions over the full study period from 2000 to 2014. This analysis reveals that significant uncertainty is associated with some of the regional trends due to time and space sampling deficiencies. The set of observed regional trends has then been used for the evaluation of 10 models (6 AeroCom phase III models and 4 CMIP6 models) and the CAMS reanalysis dataset and of their skills in reproducing the aerosol trends. Model performance is found to vary depending on the parameters and the regions of the world. The models tend to capture trends in AOD, the column Ångström exponent, sulfate and particulate matter well (except in North Africa), but they show larger discrepancies for coarse-mode AOD. The rather good agreement of the trends, across different aerosol parameters between models and observations, when co-locating them in time and space, implies that global model trends, including those in poorly monitored regions, are likely correct. The models can help to provide a global picture of the aerosol trends by filling the gaps in regions not covered by observations. The calculation of aerosol trends at a global scale reveals a different picture from that depicted by solely relying on ground-based observations. Using a model with complete diagnostics (NorESM2), we find a global increase in AOD of about 0.2 % yr−1 between 2000 and 2014, primarily caused by an increase in the loads of organic aerosols, sulfate and black carbon.

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Section: Discussionmentioning

confidence: 99%

Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis

et al. 2020

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“…The surface energy balance (SEB) decomposition approach is used to infer the contribution of changes in energy fluxes to changes in the surface temperature (T surf ) (e.g. Luyssaert et al, 2014). Through the Stefan-Boltzmann law, changes in longwave radiation emitted from the surface are directly linked to T surf .…”

Section: Methodsmentioning

confidence: 99%

Global climate response to idealized deforestation in CMIP6 models

et al. 2020

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Abstract. Changes in forest cover have a strong effect on climate through the alteration of surface biogeophysical and biogeochemical properties that affect energy, water and carbon exchange with the atmosphere. To quantify biogeophysical and biogeochemical effects of deforestation in a consistent setup, nine Earth system models (ESMs) carried out an idealized experiment in the framework of the Coupled Model Intercomparison Project, phase 6 (CMIP6). Starting from their pre-industrial state, models linearly replace 20×106 km2 of forest area in densely forested regions with grasslands over a period of 50 years followed by a stabilization period of 30 years. Most of the deforested area is in the tropics, with a secondary peak in the boreal region. The effect on global annual near-surface temperature ranges from no significant change to a cooling by 0.55 ∘C, with a multi-model mean of -0.22±0.21 ∘C. Five models simulate a temperature increase over deforested land in the tropics and a cooling over deforested boreal land. In these models, the latitude at which the temperature response changes sign ranges from 11 to 43∘ N, with a multi-model mean of 23∘ N. A multi-ensemble analysis reveals that the detection of near-surface temperature changes even under such a strong deforestation scenario may take decades and thus longer than current policy horizons. The observed changes emerge first in the centre of deforestation in tropical regions and propagate edges, indicating the influence of non-local effects. The biogeochemical effect of deforestation are land carbon losses of 259±80 PgC that emerge already within the first decade. Based on the transient climate response to cumulative emissions (TCRE) this would yield a warming by 0.46 ± 0.22 ∘C, suggesting a net warming effect of deforestation. Lastly, this study introduces the “forest sensitivity” (as a measure of climate or carbon change per fraction or area of deforestation), which has the potential to provide lookup tables for deforestation–climate emulators in the absence of strong non-local climate feedbacks. While there is general agreement across models in their response to deforestation in terms of change in global temperatures and land carbon pools, the underlying changes in energy and carbon fluxes diverge substantially across models and geographical regions. Future analyses of the global deforestation experiments could further explore the effect on changes in seasonality of the climate response as well as large-scale circulation changes to advance our understanding and quantification of deforestation effects in the ESM frameworks.

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“…The AMOC, a major oceanic current transporting heat into the Arctic (Mahajan et al, 2011), is inferred to have been significantly stronger in the mPWP compared to pre-industrial values based on proxy evidence (Dowsett et al, 2009;Frank et al, 2002;Frenz et al, 2006;McKay et al, 2012;Ravelo and Andreasen, 2000;Raymo et al, 1996). An analysis of AMOC changes in PlioMIP2 simulations shows that, indeed, the maximum AMOC strength increases: by 4 % to 53 % ( Fig.…”

Section: Atlantic Meridional Overturning Circulationmentioning

confidence: 96%

Evaluation of Arctic warming in mid-Pliocene climate simulations

Nooijer

Zhang

et al. 2020

Clim. Past

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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 period, 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 -10.4×106 km2, with a MMM anomaly of -5.6×106 km2, which constitutes a decrease of 53 % compared to the pre-industrial period. The majority (11 out of 16) of models simulate summer sea-ice-free conditions (≤1×106 km2) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions, although the degree of underestimation varies strongly between the simulations. 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 regard 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 than CMIP5 future climate simulations and an increase instead of a decrease in Atlantic Meridional Overturning Circulation (AMOC) strength compared to pre-industrial period. 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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Implementation of the CMIP6 Forcing Data in the IPSL‐CM6A‐LR Model

Cited by 127 publications

References 64 publications

Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis

Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis

Global climate response to idealized deforestation in CMIP6 models

Evaluation of Arctic warming in mid-Pliocene climate simulations

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