ALBERT

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: Antarctic geothermal heat flow (GHF) affects the temperature of the ice sheet, determining its ability to slide and internally deform, as well as the behaviour of the continental crust. However, GHF remains poorly constrained, with few and sparse local, borehole-derived estimates and large discrepancies in the magnitude and distribution of existing continent-scale estimates from geophysical models. We review the methods to estimate GHF, discussing the strengths and limitations of each approach; compile borehole and probe-derived estimates from measured temperature profiles; and recommend the following future directions. (1) Obtain more borehole-derived estimates from the subglacial bedrock and englacial temperature profiles. (2) Estimate GHF from inverse glaciological modelling, constrained by evidence for basal melting and englacial temperatures (e.g. using microwave emissivity). (3) Revise geophysically derived GHF estimates using a combination of Curie depth, seismic, and thermal isostasy models. (4) Integrate in these geophysical approaches a more accurate model of the structure and distribution of heat production elements within the crust and considering heterogeneities in the underlying mantle. (5) Continue international interdisciplinary communication and data access.

Description: Northwestern Alaska has been highly affected by changing climatic patterns with new temperature and precipitation maxima over the recent years. In particular, the Baldwin and northern Seward peninsulas are characterized by an abundance of thermokarst lakes that are highly dynamic and prone to lake drainage like many other regions at the southern margins of continuous permafrost. We used Sentinel-1 synthetic aperture radar (SAR) and Planet CubeSat optical remote sensing data to analyze recently observed widespread lake drainage. We then used synoptic weather data, climate model outputs and lake ice growth simulations to analyze potential drivers and future pathways of lake drainage in this region. Following the warmest and wettest winter on record in 2017/2018, 192 lakes were identified as having completely or partially drained by early summer 2018, which exceeded the average drainage rate by a factor of ∼ 10 and doubled the rates of the previous extreme lake drainage years of 2005 and 2006. The combination of abundant rain- and snowfall and extremely warm mean annual air temperatures (MAATs), close to 0 ∘C, may have led to the destabilization of permafrost around the lake margins. Rapid snow melt and high amounts of excess meltwater further promoted rapid lateral breaching at lake shores and consequently sudden drainage of some of the largest lakes of the study region that have likely persisted for millennia. We hypothesize that permafrost destabilization and lake drainage will accelerate and become the dominant drivers of landscape change in this region. Recent MAATs are already within the range of the predictions by the University of Alaska Fairbanks' Scenarios Network for Alaska and Arctic Planning (UAF SNAP) ensemble climate predictions in scenario RCP6.0 for 2100. With MAAT in 2019 just below 0 ∘C at the nearby Kotzebue, Alaska, climate station, permafrost aggradation in drained lake basins will become less likely after drainage, strongly decreasing the potential for freeze-locking carbon sequestered in lake sediments, signifying a prominent regime shift in ice-rich permafrost lowland regions.

Description: The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 to monitor the precise changes in the thickness of terrestrial ice sheets and marine floating ice. To do that, CryoSat orbits the planet at an altitude of around 720 km with a retrograde orbit inclination of 92∘ and a quasi repeat cycle of 369 d (30 d subcycle). To reach the mission goals, the CryoSat products have to meet the highest quality standards to date, achieved through continual improvements of the operational processing chains. The new CryoSat Ice Baseline-D, in operation since 27 May 2019, represents a major processor upgrade with respect to the previous Ice Baseline-C. Over land ice the new Baseline-D provides better results with respect to the previous baseline when comparing the data to a reference elevation model over the Austfonna ice cap region, improving the ascending and descending crossover statistics from 1.9 to 0.1 m. The improved processing of the star tracker measurements implemented in Baseline-D has led to a reduction in the standard deviation of the point-to-point comparison with the previous star tracker processing method implemented in Baseline-C from 3.8 to 3.7 m. Over sea ice, Baseline-D improves the quality of the retrieved heights inside and at the boundaries of the synthetic aperture radar interferometric (SARIn or SIN) acquisition mask, removing the negative freeboard pattern which is beneficial not only for freeboard retrieval but also for any application that exploits the phase information from SARIn Level 1B (L1B) products. In addition, scatter comparisons with the Beaufort Gyre Exploration Project (BGEP; https://www.whoi.edu/beaufortgyre, last access: October 2019) and Operation IceBridge (OIB; Kurtz et al., 2013) in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0.85 to −0.14 m. For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %–10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations.

Description: The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface to bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of water samples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are: data from 106 more cruises added, extension of time coverage until 2019, and the inclusion of available discrete fugacity of CO2 (fCO2) values in the merged product files. GLODAPv2.2020 includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control, especially systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 106 new cruises with the data from the 840 quality-controlled cruises of the GLODAPv2.2019 data product. They correct for errors related to measurement, calibration, and data handling practices, while taking into account any known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 μmol kg−1 in dissolved inorganic carbon, 4 μmol kg−1 in total alkalinity, 0.01–0.02, depending on region, in pH, and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete fCO2 were not subjected to bias comparison or adjustments. The original data, their documentation and doi codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, last access: 22 June 2020). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). The bias corrected product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2020 methods and provides a broad overview of the secondary quality control procedures and results.

Description: Glacial isostatic adjustment (GIA) is a major source of uncertainty for ice and ocean mass balance estimates derived from satellite gravimetry. In Antarctica the gravimetric effect of cryospheric mass change and GIA are of the same order of magnitude. Inverse estimates from geodetic observations hold some promise for mass signal separation. Here, we investigate the combination of satellite gravimetry and altimetry and demonstrate that the choice of input data sets and processing methods will influence the resultant GIA inverse estimate. This includes the combination that spans the full GRACE record (April 2002–August 2016). Additionally, we show the variations that arise from combining the actual time series of the differing data sets. Using the inferred trends, we assess the spread of GIA solutions owing to (1) the choice of different degree-1 and C20 products, (2) viable candidate surface-elevation-change products derived from different altimetry missions corresponding to different time intervals, and (3) the uncertainties associated with firn process models. Decomposing the total-mass signal into the ice mass and the GIA components is strongly dependent on properly correcting for an apparent bias in regions of small signal. Here our ab initio solutions force the mean GIA and GRACE trend over the low precipitation zone of East Antarctica to be zero. Without applying this bias correction, the overall spread of total-mass change and GIA-related mass change using differing degree-1 and C20 products is 68 and 72 Gt a−1, respectively, for the same time period (March 2003–October 2009). The bias correction method collapses this spread to 6 and 5 Gt a−1, respectively. We characterize the firn process model uncertainty empirically by analysing differences between two alternative surface mass balance products. The differences propagate to a 10 Gt a−1 spread in debiased GIA-related mass change estimates. The choice of the altimetry product poses the largest uncertainty on debiased mass change estimates. The spread of debiased GIA-related mass change amounts to 15 Gt a−1 for the period from March 2003 to October 2009. We found a spread of 49 Gt a−1 comparing results for the periods April 2002–August 2016 and July 2010–August 2016. Our findings point out limitations associated with data quality, data processing, and correction for apparent biases.

Description: Landfast sea ice (fast ice) attached to Antarctic (near-)coastal elements is a critical component of the local physical and ecological systems. Through its direct coupling with the atmosphere and ocean, fast-ice properties are also a potential indicator of processes related to a changing climate. However, in situ fast-ice observations in Antarctica are extremely sparse because of logistical challenges and harsh environmental conditions. Since 2010, a monitoring program observing the seasonal evolution of fast ice in Atka Bay has been conducted as part of the Antarctic Fast Ice Network (AFIN). The bay is located on the northeastern edge of Ekström Ice Shelf in the eastern Weddell Sea, close to the German wintering station Neumayer III. A number of sampling sites have been regularly revisited each year between annual ice formation and breakup to obtain a continuous record of sea-ice and sub-ice platelet-layer thickness, as well as snow depth and freeboard across the bay. Here, we present the time series of these measurements over the last 9 years. Combining them with observations from the nearby Neumayer III meteorological observatory as well as auxiliary satellite images enables us to relate the seasonal and interannual fast-ice cycle to the factors that influence their evolution. On average, the annual consolidated fast-ice thickness at the end of the growth season is about 2 m, with a loose platelet layer of 4 m thickness beneath and 0.70 m thick snow on top. Results highlight the predominately seasonal character of the fast-ice regime in Atka Bay without a significant interannual trend in any of the observed variables over the 9-year observation period. Also, no changes are evident when comparing with sporadic measurements in the 1980s and 1990s. It is shown that strong easterly winds in the area govern the year-round snow distribution and also trigger the breakup of fast ice in the bay during summer months. Due to the substantial snow accumulation on the fast ice, a characteristic feature is frequent negative freeboard, associated flooding of the snow–ice interface, and a likely subsequent snow ice formation. The buoyant platelet layer beneath negates the snow weight to some extent, but snow thermodynamics is identified as the main driver of the energy and mass budgets for the fast-ice cover in Atka Bay. The new knowledge of the seasonal and interannual variability of fast-ice properties from the present study helps to improve our understanding of interactions between atmosphere, fast ice, ocean, and ice shelves in one of the key regions of Antarctica and calls for intensified multidisciplinary studies in this region.

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.