Description: We compare results obtained from modeling the mid-Pliocene warm period using the Community Earth System Models (COSMOS, version: COSMOS-landveg r2413, 2009) with the two different modeling methodologies and sets of boundary conditions prescribed for the two phases of the Pliocene Model Intercomparison Project (PlioMIP), tagged PlioMIP1 and PlioMIP2. Here, we bridge the gap between our contributions to PlioMIP1 (Stepanek and Lohmann, 2012) and PlioMIP2 (Stepanek et al., 2020). We highlight some of the effects that differences in the chosen mid-Pliocene model setup (PlioMIP2 vs. PlioMIP1) have on the climate state as derived with COSMOS, as this information will be valuable in the framework of the model–model and model–data comparison within PlioMIP2. We evaluate the model sensitivity to improved mid-Pliocene boundary conditions using PlioMIP's core mid-Pliocene experiments for PlioMIP1 and PlioMIP2 and present further simulations in which we test model sensitivity to variations in paleogeography, orbit, and the concentration of CO2. Firstly, we highlight major changes in boundary conditions from PlioMIP1 to PlioMIP2 and also the challenges recorded from the initial effort. The results derived from our simulations show that COSMOS simulates a mid-Pliocene climate state that is 0.29°C colder in PlioMIP2 if compared to PlioMIP1 (17.82°C in PlioMIP1, 17.53°C in PlioMIP2; values based on simulated surface skin temperature). On the one hand, high-latitude warming, which is supported by proxy evidence of the mid-Pliocene, is underestimated in simulations of both PlioMIP1 and PlioMIP2. On the other hand, spatial variations in surface air temperature (SAT), sea surface temperature (SST), and the distribution of sea ice suggest improvement of simulated SAT and SST in PlioMIP2 if employing the updated paleogeography. Our PlioMIP2 mid-Pliocene simulation produces warmer SSTs in the Arctic and North Atlantic Ocean than those derived from the respective PlioMIP1 climate state. The difference in prescribed CO2 accounts for 0.5°C of temperature difference in the Arctic, leading to an ice-free summer in the PlioMIP1 simulation, and a quasi ice-free summer in PlioMIP2. Beyond the official set of PlioMIP2 simulations, we present further simulations and analyses that sample the phase space of potential alternative orbital forcings that have acted during the Pliocene and may have impacted geological records. Employing orbital forcing, which differs from that proposed for PlioMIP2 (i.e., corresponding to pre-industrial conditions) but falls into the mid-Pliocene time period targeted in PlioMIP, leads to pronounced annual and seasonal temperature variations. Our result identifies the changes in mid-Pliocene paleogeography from PRISM3 to PRISM4 as the major driver of the mid-Pliocene warmth within PlioMIP and not the minor differences in forcings.

Description: 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.

Description: In recent years, many components of the connection between Eurasian autumn snow cover and wintertime North Atlantic Oscillation (NAO) have been investigated, suggesting that November snow cover distribution has strong prediction power for the upcoming Northern Hemisphere winter climate. However, the non-stationarity of this relationship could impact its use for prediction routines. Here we use snow products from long-term reanalyses to investigate interannual and interdecadal links between autumnal snow cover and atmospheric conditions in winter. We find evidence for a negative NAO-like signal after November with a strong west-to-east snow cover gradient, which is valid throughout the last 150 years. This correlation is consistently linked to a weak stratospheric polar vortex state. Nevertheless, decadal evolution of this link shows episodes of decreased correlation strength, which co-occur with episodes of low variability in the November snow index. By contrast, periods with high prediction skill for winter NAO are found in periods of high November snow variability, which co-occur with the Arctic warming periods of the 20th century, namely the early 20th-century Arctic warming between 1920 and 1940 and the ongoing anthropogenic global warming at the end of the 20th century. A strong snow dipole itself is consistently associated with reduced Barents–Kara sea ice concentration, increased Ural blocking frequency and negative temperature anomalies in eastern Eurasia.

Description: Energy balance models (EBMs) are highly simplified models of the climate system, providing admissible conceptual tools for understanding climate changes. The global temperature is calculated by the radiation budget through the incoming energy from the Sun and the outgoing energy from the Earth. The argument that the temperature can be calculated by this simple radiation budget is revisited. The underlying assumption for a realistic temperature distribution is explored: one has to assume a moderate diurnal cycle due to the large heat capacity and the fast rotation of the Earth. Interestingly, the global mean in the revised EBM is very close to the originally proposed value. The main point is that the effective heat capacity and its temporal variation over the daily and seasonal cycle needs to be taken into account when estimating surface temperature from the energy budget. Furthermore, the time-dependent EBM predicts a flat meridional temperature gradient for large heat capacities, reducing the seasonal cycle and the outgoing radiation and increasing global temperature. Motivated by this finding, a sensitivity experiment with a complex model is performed where the vertical diffusion in the ocean has been increased. The resulting temperature gradient, reduced seasonal cycle, and global warming is also found in climate reconstructions, providing a possible mechanism for past climate changes prior to 3 million years ago.

Description: The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Description: We present here the first results, for the preindustrial and mid-Holocene climatological periods, of the newly developed isotope-enhanced version of the fully coupled Earth system model MPI-ESM, called hereafter MPI-ESM-wiso. The water stable isotopes H16O, H18O and HDO have been implemented into all components of the coupled model setup. The mid-Holocene provides the opportunity to evaluate the model response to changes in the seasonal and latitudinal distribution of insolation induced by different orbital forcing conditions. The results of our equilibrium simulations allow us to evaluate the performance of the isotopic model in simulating the spatial and temporal variations of water isotopes in the different compartments of the hydrological system for warm climates. For the preindustrial climate, MPI-ESM-wiso reproduces very well the observed spatial distribution of the isotopic content in precipitation linked to the spatial variations in temperature and precipitation rate. We also find a good model–data agreement with the observed distribution of isotopic composition in surface seawater but a bias with the presence of surface seawater that is too 18O-depleted in the Arctic Ocean. All these results are improved compared to the previous model version ECHAM5/MPIOM. The spatial relationships of water isotopic composition with temperature, precipitation rate and salinity are consistent with observational data. For the preindustrial climate, the interannual relationships of water isotopes with temperature and salinity are globally lower than the spatial ones, consistent with previous studies. Simulated results under mid-Holocene conditions are in fair agreement with the isotopic measurements from ice cores and continental speleothems. MPI-ESM-wiso simulates a decrease in the isotopic composition of precipitation from North Africa to the Tibetan Plateau via India due to the enhanced monsoons during the mid-Holocene. Over Greenland, our simulation indicates a higher isotopic composition of precipitation linked to higher summer temperature and a reduction in sea ice, shown by positive isotope–temperature gradient. For the Antarctic continent, the model simulates lower isotopic values over the East Antarctic plateau, linked to the lower temperatures during the mid-Holocene period, while similar or higher isotopic values are modeled over the rest of the continent. While variations of isotopic contents in precipitation over West Antarctica between mid-Holocene and preindustrial periods are partly controlled by changes in temperature, the transport of relatively 18O-rich water vapor near the coast to the western ice core sites could play a role in the final isotopic composition. So, more caution has to be taken about the reconstruction of past temperature variations during warm periods over this area. The coupling of such a model with an ice sheet model or the use of a zoomed grid centered on this region could help to better describe the role of the water vapor transport and sea ice around West Antarctica. The reconstruction of past salinity through isotopic content in sea surface waters can be complicated for regions with strong ocean dynamics, variations in sea ice regimes or significant changes in freshwater budget, giving an extremely variable relationship between the isotopic content and salinity of ocean surface waters over small spatial scales. These complicating factors demonstrate the complexity of interpreting water isotopes as past climate signals of warm periods like the mid-Holocene. A systematic isotope model intercomparison study for further insights on the model dependency of these results would be beneficial.

Description: 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 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 1.7 and 5.2 °C relative to the pre-industrial era with a multi-model mean value of 3.2 °C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 °C over land and 2.8 °C over the oceans. 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 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. 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 a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8°C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

Description: In the last decades, changing climate conditions have had a severe impact on sea ice at the western Antarctic Peninsula (WAP), an area rapidly transforming under global warming. To study the development of spring sea ice and environmental conditions in the pre-satellite era we investigated three short marine sediment cores for their biomarker inventory with a particular focus on the sea ice proxy IPSO25 and micropaleontological proxies. The core sites are located in the Bransfield Strait in shelf to deep basin areas characterized by a complex oceanographic frontal system, coastal influence and sensitivity to large-scale atmospheric circulation patterns. We analyzed geochemical bulk parameters, biomarkers (highly branched isoprenoids, glycerol dialkyl glycerol tetraethers, sterols), and diatom abundances and diversity over the past 240 years and compared them to observational data, sedimentary and ice core climate archives, and results from numerical models. Based on biomarker results we identified four different environmental units characterized by (A) low sea ice cover and high ocean temperatures, (B) moderate sea ice cover with decreasing ocean temperatures, (C) high but variable sea ice cover during intervals of lower ocean temperatures, and (D) extended sea ice cover coincident with a rapid ocean warming. While IPSO25 concentrations correspond quite well to satellite sea ice observations for the past 40 years, we note discrepancies between the biomarker-based sea ice estimates, the long-term model output for the past 240 years, ice core records, and reconstructed atmospheric circulation patterns such as the El Niño–Southern Oscillation (ENSO) and Southern Annular Mode (SAM). We propose that the sea ice biomarker proxies IPSO25 and PIPSO25 are not linearly related to sea ice cover, and, additionally, each core site reflects specific local environmental conditions. High IPSO25 and PIPSO25 values may not be directly interpreted as referring to high spring sea ice cover because variable sea ice conditions and enhanced nutrient supply may affect the production of both the sea-ice-associated and phytoplankton-derived (open marine, pelagic) biomarker lipids. For future interpretations we recommend carefully considering individual biomarker records to distinguish between cold sea-ice-favoring and warm sea-ice-diminishing environmental conditions.