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This paper reviews sea level contributions from land ice across the Arctic, including Greenland. We summarize ice loss measurement methods, ice loss mechanisms, and recent observations and projections, and highlight research advances over the last 3-5 years and remaining scientific challenges.
Ongoing research in the GMAO seeks to improve the representation atmospheric, land, and ocean processes in the boreal Arctic and other polar regions. This talk touches on a range of topics, including improvements to sea ice and land ice representation in our subseasonal-to-seasonal prediction system and the initiation of a new collaborative effort with the Cryospheric Sciences Laboratory to develop a high resolution, polar-focused atmospheric reanalysis.
The Microwave Limb Sounder (MLS) observed ozone decreases in the Arctic Vortex beginning in January 1997 at 585K and in February 1997 at 465K.
We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.
Accurately resolving spatio-temporal variations in sea surface height across the polar oceans is key to improving our understanding of ocean circulation variability and change. Here, we examine the first two years (2018-2020) of Arctic Ocean sea surface height anomalies (SSHA) from the photon-counting laser altimeter onboard NASA’s ICE, Cloud, and Land Elevation Satellite-2 (ICESat-2). ICESat-2 SSHA estimates are compared to estimates from ESA’s CryoSat-2 mission, including semi-synchronous along-track measurements from the recent CRYO2ICE orbit alignment campaign. There are documented residual centimeter-scale range biases between the ICESat-2 beams (in release-003 data) and we opted for a single-beam approach in our comparisons. We find good agreement in the along-track estimates (spatial correlation coefficient >0.8 and mean differences <0.03 m) as well as in the gridded monthly SSHA estimates (temporal correlation coefficient of 0.76 and a mean difference of 0.01 m) from the two altimeters, suggesting ICESat-2 adds to the CryoSat-2 SSHA estimates.
Arctic oscillation (AO), which is the most dominant atmospheric variability in the Northern Hemisphere (NH) during the boreal winter, significantly affects the weather and climate at mid-to-high latitudes in the NH. Although a climate community has focused on a negative trend of AO in recent decades, the significant positive trend of AO over the last 60 years has not yet been thoroughly discussed. By analyzing reanalysis and Atmospheric Model Inter-comparison Project (AMIP) datasets with novel pacemaker experiments, we found that sea surface temperature warming in the Indian Ocean is conducive to the positive trend of AO from the late 1950s. The momentum flux convergence by stationary waves due to the Indian Ocean warming plays an important role in the positive trend of AO, which is characterized by a poleward shift of zonal-mean zonal winds. In addition, the reduced upward propagating wave activity flux over the North Pacific due to Indian Ocean warming also plays a role to strengthen the polar vortex, subsequently, it contributes to the positive trend of AO. Our results imply that the respective warming trend of tropical ocean basins including Indian Ocean, which is either anthropogenic forcing or natural variability or their combined effect, should be considered to correctly project the future AO’s trend.
Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.
The Arctic Ocean is changing rapidly as the planet warms, but studying it presents challenges: it’s a remote area that is often covered by sea ice. As CHELLE GENTEMANN, MARISOL GARCÍA-REYES and JORGE VAZQUEZ-CUERVO explain, using autonomous solar-powered sailing robots is a safe, reliable and climate-friendly way to collect the data we need.
The Arctic Ocean is surrounded by land that feeds highly seasonal rivers with water enriched in high concentrations of dissolved and particulate organic carbon (DOC and POC). Explicit estimates of the flux of organic carbon across the land-ocean interface are difficult to quantify and many interdependent processes makes source attribution difficult. A high-resolution 3-D biogeochemical model was built for the lower Yukon River and coastal ocean to estimate biogeochemical cycling across the land-ocean continuum. The model solves for complex reactions related to organic carbon transformation, including mechanistic photodegradation and multi-reactivity microbial processing, DOC to POC flocculation, and phytoplankton dynamics. The baseline DOC and POC flux out of the delta from April to September 2019, was 977 and 536 Gg C (~80% of the annual total), but only 50% of the DOC and 25% of the POC exited the plume across the 10 m isobath. Microbial breakdown of DOC accounted for a net loss of 168 Gg C (17% of delta export) within the plume and photodegradation accounted for a net loss of 46.6 Gg C DOC (5% of delta export) in 2019. Flocculation decreased the total organic carbon flux by only 6.4 Gg C (~1%), while POC sinking accounted for 63.3 Gg C (10%) settling in the plume. The loss of chromophoric dissolved organic matter (CDOM) due to photodegradation increased the light available for phytoplankton growth throughout the coastal ocean, demonstrating the secondary effects that organic carbon reactions can have on biological processes and the net coastal carbon flux.
Here, we present a compilation of 95 ice temperature profiles from 85 boreholes from the Greenland ice sheet and peripheral ice caps, as well as local ice caps in the Canadian Arctic. Profiles from only 31 boreholes (36 %) were previously available in open-access data repositories. The remaining 54 borehole profiles (64 %) are being made digitally available here for the first time. These newly available profiles, which are associated with pre-2010 boreholes, have been submitted by community members or digitized from published graphics and/or data tables. All 95 profiles are now made available in both absolute (meters) and normalized (0 to 1 ice thickness) depth scales and are accompanied by extensive metadata. These metadata include a transparent description of data provenance. The ice temperature profiles span 70 years, with the earliest profile being from 1950 at Camp VI, West Greenland. To highlight the value of this database in evaluating ice flow simulations, we compare the ice temperature profiles from the Greenland ice sheet with an ice flow simulation by the Parallel Ice Sheet Model (PISM). We find a cold bias in modeled near-surface ice temperatures within the ablation area, a warm bias in modeled basal ice temperatures at inland cold-bedded sites, and an apparent underestimation of deformational heating in high-strain settings. These biases provide process level insight on simulated ice temperatures.
To better understand how model resolution affects the formation of Arctic boundary layer clouds, we investigated the influence of grid spacing on simulating cloud streets that occurred near Utqiagvik (formerly Barrow), Alaska on 2 May 2013 and were observed by the Moderate Resolution Imaging Spectroradiometer (MODIS). The Weather Research and Forecasting model was used to simulate the clouds using nested domains with increasingly fine resolution ranging from a horizontal grid spacing of 27 km in the boundary-layer-parameterized mesoscale domain to a grid spacing of 0.111 km in the large-eddy-permitting domain. We investigated the model-simulated mesoscale environment, horizontal and vertical cloud structures, boundary layer stability, and cloud properties, all of which were subsequently used to interpret the observed roll-cloud case. Increasing model resolution led to a transition from a more buoyant boundary layer to a more shear-driven turbulent boundary layer. The clouds were stratiform-like in the mesoscale domain, but as the model resolution increased, roll-like structures, aligned along the wind field, appeared with ever smaller wavelengths. A stronger vertical water vapor gradient occurred above the cloud layers with decreasing grid spacing. With fixed model grid spacing at 0.333 km, changing the model configuration from a boundary layer parameterization to a large-eddy-permitting scheme produced a more shear-driven and less unstable environment, a stronger vertical water vapor gradient below the cloud layers, and the wavelengths of the rolls decreased slightly. In this study, only the large-eddy-permitting simulation with gird spacing of 0.111 km was sufficient to model the observed roll clouds.
The Arctic Alaska petroleum province is geologically and geochemically complex. Mixed hydrocarbon charge from multiple source rocks and/or levels of thermal maturity is common within an individual oil pool. Biomarker and chemometric statistical analyses were used to correlate twenty-nine oils to five oil families derived from: (1) Triassic Shublik Formation (calcareous organofacies), (2) Triassic Shublik Formation (shaly organofacies), (3) Jurassic Kingak Shale, (4) Cretaceous shale (pebble shale unit and Hue Shale), and (5) Paleogene shale (Canning Formation). Age-diagnostic and source-related oil biomarker parameters establish clear genetic relationships between the normal oil-window components and their putative source designations. However, application of diamondoid analyses reveals mixed-oil accumulations with postmature charge contributions (diamondoid-rich and biomarker-poor) in many oils. Most sampled reservoirs contain a predominant charge derived from a single oil-window source plus a minor contribution from one or more higher maturity source(s). Variations in source organofacies also are recognized in the Shublik, Kingak, and Cretaceous oil families. In some cases, oils from multiple pools within a single field display relatively homogeneous geochemical profiles, suggesting a common source and migration pathway. For example, oil from the significant Pikka discovery is inferred to originate mainly from the calcareous Shublik Formation. In other cases, variability among oils from multiple pools within a single field (e.g., Milne Point, Colville River, and Northstar) likely indicates a more complex source, migration, and charge history. The results of this study may be useful for anticipating the composition of oil charge in stratigraphic traps with low-permeability sandstone reservoirs, where oil gravity and other chemical parameters may influence economic viability.
Arctic delta channel dynamics and coastal deposition patterns are uniquely affected by ice and permafrost. Building on previous experiments that documented the individual effects of ice and permafrost, we conducted new numerical modeling experiments to examine the combined influences of ice thickness and permafrost erodibility. We quantified the effects of ice, permafrost, and their combination on channel migration, avulsion, and persistence; onshore and nearshore deposition; and large-scale delta morphology. Both ice and permafrost limited channel mobility, enhanced overbank deposition, and roughened shorelines. Thick ice increased offshore channel incision and sediment delivery. Permafrost stabilized channels and focused deposition that led to enhanced channel levee development. The increased levee deposition limited widespread channel infilling leading to the preservation of abandoned channels. Levee growth was enhanced by overbank flooding due to ice-driven changes in water surface elevations. Furthermore, this flooding led to more extensive levees on deltas with both ice and permafrost compared to deltas with only permafrost. Ice also preserved small channels, leading to size-dependent channel persistence: the presence of ice in the channels reduced the fraction of time small channels remained active relative to larger channels. Resistant permafrost damped this effect, as smaller and more distal channels were active earlier in the flood on deltas with resistant permafrost likely due to the smaller number of channels on permafrost-laden deltas. Finally, our results suggest that the loss of both ice and permafrost will increase channel mobility, decrease overbank flooding, increase shoreline progradation rates, and decrease offshore transport of riverine fluxes.
Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.
Here, the ‘surface scattering layer’ (SSL) is the highly-scattering, coarse-grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ~0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the MOSAiC expedition showed that the SSL re-forms in approximately one day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice-albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain.
The Arctic is marked by deep intrusions of warm, moist air, alternating with outbreaks of cold air down to lower latitudes. The typical vertical structure of clouds and precipitation during these two synoptic weather extremes is examined at a coastal site at 69°N in Norway. The Norwegian Sea is a corridor for warm-air intrusions (WAIs) and frequently witnesses cold-air outbreaks (CAOs). This study uses data from profiling radar, lidar, and microwave radiometer, radiosondes and other probes that were collected during the CAOs in the Marine Boundary Layer Experiment (COMBLE) between 1 December 2019 and 31 May 2020. Marine CAOs are defined in terms of thermal instability relative to the sea surface temperature, and WAIs in terms of equivalent potential temperature stratification between the surface and 850 hPa. Cloud structures in CAOs are convective, driven by strong surface heat fluxes over a long fetch of open water, with cloud tops rarely exceeding 6 km above sea level. The mostly open-cellular convection produces intermittent moderately-heavy precipitation at the observational site, notwithstanding the low precipitable water vapor (PWV). In contrast, WAIs are marked by high values of PWV and integrated vapor transport. Here, WAI clouds are synoptically driven, stratiform, with cloud tops often exceeding 5 km, sometimes layered, and generally producing persistent precipitation that can be heavier than in CAOs.
The planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year-round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLH MOSAiC ), and further evaluate the PBLH from the ERA5 reanalysis (PBLH ERA5 ). Comparisons between PBLH MOSAiC and PBLH ERA5 from different perspectives reveal that: (a) The overestimation of PBLH ERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLH ERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLH ERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLH ERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.
Abstract Marine cold‐air outbreaks (MCAOs) are crucial for Arctic Ocean heat loss, featuring convective cloud rolls that transition into convection cells downstream. Understanding factors controlling this transformation is the key for improving MCAO cloud representation in climate models. This study employs large‐eddy simulations to investigate how cloud ice number concentrations () affect cloud evolution using a case from the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. The simulations, performed in a Lagrangian framework following an air mass trajectory, are driven by ERA5 reanalysis data. Initially, all simulations produce similar cloud patterns, but higher leads to earlier breakup of cloud rolls. Between 4 and 10 hr, surface precipitation rates are similar across simulations, but precipitation initiates earlier, and the cloud‐base precipitation rates are higher when is higher. The stronger precipitation evaporation leads to increased stability of the boundary layer and reduced intensity of vertical mixing between the surface and cloud layer. An increased sink of cloud layer moisture via precipitation and decreased source through diminished vertical transport result in earlier cloud breakup in higher conditions. Simulations with different sea surface temperatures (SST) indicate that this cloud breakup mechanism remains valid for MCAOs of different strengths, although the cloud organization is more sensitive to SST changes in low environments. This work highlights the importance of accurate representations of ice processes in simulating MCAO clouds and suggests the need for observational constraints of ice nucleating particles and over the mixed‐phase cloud regimes.