Climatological Characteristics of Arctic and Antarctic Surface-Based Inversions

Surface-based inversions (SBIs) are frequent features of the Arctic and Antarctic atmospheric boundary layer. They influence vertical mixing of energy, moisture and pollutants, cloud formation, and surface ozone destruction. Their climatic variability is related to that of sea ice and planetary albedo, important factors in climate feedback mechanisms. However, climatological polar SBI properties have not been fully characterized nor have climate model simulations of SBIs been compared comprehensively to observations. Using 20 years of twice-daily observations from 39 Arctic and 6 Antarctic radiosonde stations, this study examines the spatial and temporal variability of three SBI characteristic—frequency of occurrence, depth (from the surface to the inversion top), and intensity (temperature difference over the SBI depth)—and relationships among them. In both polar regions, SBIs are more frequent, deeper, and stronger in winter and autumn than in summer and spring. In the Arctic, these tendencies increase from the Norwegian Sea eastward toward the East Siberian Sea, associated both with (seasonal and diurnal) variations in solar elevation angle at the standard radiosonde observation times and with differences between continental and maritime climates. Two state-of-the-art climate models and one reanalysis dataset show similar seasonal patterns and spatial distributions of SBI properties as the radiosonde observations, but with biases in their magnitudes that differ among the models and that are smaller in winter and autumn than in spring and summer. SBI frequency, depth, and intensity are positively correlated, both spatially and temporally, and all three are anticorrelated with surface temperature.

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Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽

10.3390/atmos12020174 ◽

2021 ◽

Vol 12 (2) ◽

pp. 174

Author(s):

Günther Heinemann ◽

Sascha Willmes ◽

Lukas Schefczyk ◽

Alexander Makshtas ◽

Vasilii Kustov ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Open Water ◽

The Arctic ◽

Late Winter ◽

Good Representation ◽

Hourly Temperature ◽

Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

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Antarctic sea ice decline delayed well into the 21st century in a high-resolution climate projection

10.5194/egusphere-egu2020-20837 ◽

2020 ◽

Author(s):

Thomas Rackow ◽

Sergey Danilov ◽

Helge F. Goessling ◽

Hartmut H. Hellmer ◽

Dmitry V. Sein ◽

...

Keyword(s):

Global Warming ◽

High Resolution ◽

Sea Ice ◽

Climate Models ◽

Climate Model ◽

The Arctic ◽

Alternative Mechanism ◽

Sea Ice Extent ◽

Antarctic Sea Ice ◽

Sea Ice Decline

Despite ongoing global warming and strong sea ice decline in the Arctic, the sea ice extent around the Antarctic continent has not declined during the satellite era since 1979. This is in stark contrast to existing climate models that tend to show a strong negative sea ice trend for the same period; hence the confidence in projected Antarctic sea-ice changes is considered to be low. In the years since 2016, there has been significantly lower Antarctic sea ice extent, which some consider a sign of imminent change; however, others have argued that sea ice extent is expected to regress to the weak decadal trend in the near future.In this presentation, we show results from climate change projections with a new climate model that allows the simulation of mesoscale eddies in dynamically active ocean regions in a computationally efficient way. We find that the high-resolution configuration (HR) favours periods of stable Antarctic sea ice extent in September as observed over the satellite era. Sea ice is not projected to decline well into the 21st century in the HR simulations, which is similar to the delaying effect of, e.g., added glacial melt water in recent studies. The HR ocean configurations simulate an ocean heat transport that responds differently to global warming and is more efficient at moderating the anthropogenic warming of the Southern Ocean. As a consequence, decrease of Antarctic sea ice extent is significantly delayed, in contrast to what existing coarser-resolution climate models predict.Other explanations why current models simulate a non-observed decline of Antarctic sea-ice have been put forward, including the choice of included sea ice physics and underestimated simulated trends in westerly winds. Our results provide an alternative mechanism that might be strong enough to explain the gap between modeled and observed trends alone.

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Intercomparison of Arctic Regional Climate Models: Modeling Clouds and Radiation for SHEBA in May 1998

Journal of Climate ◽

10.1175/jcli3854.1 ◽

2006 ◽

Vol 19 (17) ◽

pp. 4167-4178 ◽

Cited By ~ 47

Author(s):

Jun Inoue ◽

Jiping Liu ◽

James O. Pinto ◽

Judith A. Curry

Keyword(s):

Radiative Transfer ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Cloud Microphysics ◽

Surface Heat ◽

The Arctic ◽

Surface Boundary ◽

Simulated Surface

Abstract To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.

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Present-Day Arctic Sea Ice Variability in the Coupled ECHAM5/MPI-OM Model

Journal of Climate ◽

10.1175/2009jcli3065.1 ◽

2010 ◽

Vol 23 (10) ◽

pp. 2520-2543 ◽

Cited By ~ 16

Author(s):

Nikolay V. Koldunov ◽

Detlef Stammer ◽

Jochem Marotzke

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Ocean Circulation ◽

Climate Models ◽

Climate Model ◽

Heat Content ◽

Arctic Sea Ice ◽

The Arctic ◽

Max Planck ◽

Arctic Sea

Abstract As a contribution to a detailed evaluation of Intergovernmental Panel on Climate Change (IPCC)-type coupled climate models against observations, this study analyzes Arctic sea ice parameters simulated by the Max-Planck-Institute for Meteorology (MPI-M) fully coupled climate model ECHAM5/Max-Planck-Institute for Meteorology Hamburg Primitive Equation Ocean Model (MPI-OM) for the period from 1980 to 1999 and compares them with observations collected during field programs and by satellites. Results of the coupled run forced by twentieth-century CO2 concentrations show significant discrepancies during summer months with respect to observations of the spatial distribution of the ice concentration and ice thickness. Equally important, the coupled run lacks interannual variability in all ice and Arctic Ocean parameters. Causes for such big discrepancies arise from errors in the ECHAM5/MPI-OM atmosphere and associated errors in surface forcing fields (especially wind stress). This includes mean bias pattern caused by an artificial circulation around the geometric North Pole in its atmosphere, as well as insufficient atmospheric variability in the ECHAM5/MPI-OM model, for example, associated with Arctic Oscillation/North Atlantic Oscillation (AO/NAO). In contrast, the identical coupled ocean–ice model, when driven by NCEP–NCAR reanalysis fields, shows much increased skill in its ice and ocean circulation parameters. However, common to both model runs is too strong an ice export through the Fram Strait and a substantially biased heat content in the interior of the Arctic Ocean, both of which may affect sea ice budgets in centennial projections of the Arctic climate system.

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Secondary ice production in NorESM2 climate model: quantifying the impact on Arctic clouds

10.5194/egusphere-egu21-10241 ◽

2021 ◽

Author(s):

Georgia Sotiropoulou ◽

Anna Lewinschal ◽

Annica Ekman ◽

Athanasios Nenes

Keyword(s):

Climate Models ◽

Climate Model ◽

The Arctic ◽

Liquid Water Path ◽

Arctic Clouds ◽

Radiative Balance ◽

Microphysical Processes ◽

Ice Production ◽

Mixed Phase Clouds ◽

The Impact

Arctic clouds are among the largest sources of uncertainty in predictions of Arctic weather and climate. This is mainly due to errors in the representation of the cloud thermodynamic phase and the associated radiative impacts, which largely depends on the parameterization of cloud microphysical processes. Secondary ice processes (SIP) are among the microphysical processes that are poorly represented, or completely absent, in climate models. In most models, including the Norwegian Earth System Model -version 2 (NorESM2), Hallet-Mossop (H-M) is the only SIP mechanism available. In this study we further improve the description of H-M and include two additional SIP mechanisms (collisional break-up and drop-shattering) in NorESM2. Our results indicate that these additions improve the agreement between observed and modeled ice crystal number concentrations and liquid water path in mixed-phase clouds observed at Ny-Alesund in 2016-2017. We then conclude by quantifying the impact of these overlooked SIP mechanisms for cloud microphysical characteristics, properties and the radiative balance throughout the Arctic.&#160;

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Formulation of an ocean model for global climate simulations

Ocean Science Discussions ◽

10.5194/osd-2-165-2005 ◽

2005 ◽

Vol 2 (3) ◽

pp. 165-246 ◽

Cited By ~ 4

Author(s):

S. M. Griffies ◽

A. Gnanadesikan ◽

K. W. Dixon ◽

J. P. Dunne ◽

R. Gerdes ◽

...

Keyword(s):

Free Surface ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Three Dimensional ◽

Coupled Model ◽

Ocean Model ◽

Tidal Mixing ◽

The Arctic ◽

Tracer Transport

Abstract. This paper summarizes the formulation of the ocean component to the Geophysical Fluid Dynamics Laboratory's (GFDL) coupled climate model used for the 4th IPCC Assessment (AR4) of global climate change. In particular, it reviews elements of ocean climate models and how they are pieced together for use in a state-of-the-art coupled model. Novel issues are also highlighted, with particular attention given to sensitivity of the coupled simulation to physical parameterizations and numerical methods. Features of the model described here include the following: (1) tripolar grid to resolve the Arctic Ocean without polar filtering, (2) partial bottom step representation of topography to better represent topographically influenced advective and wave processes, (3) more accurate equation of state, (4) three-dimensional flux limited tracer advection to reduce overshoots and undershoots, (5) incorporation of regional climatological variability in shortwave penetration, (6) neutral physics parameterization for representation of the pathways of tracer transport, (7) staggered time stepping for tracer conservation and numerical efficiency, (8) anisotropic horizontal viscosities for representation of equatorial currents, (9) parameterization of exchange with marginal seas, (10) incorporation of a free surface that accomodates a dynamic ice model and wave propagation, (11) transport of water across the ocean free surface to eliminate unphysical "virtual tracer flux" methods, (12) parameterization of tidal mixing on continental shelves.

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The effect of aerosol on surface cloud radiative forcing in the Arctic

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-9039-2005 ◽

2005 ◽

Vol 5 (5) ◽

pp. 9039-9063 ◽

Cited By ~ 3

Author(s):

R.-M. Hu ◽

J.-P. Blanchet ◽

E. Girard

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

High Surface ◽

The Arctic ◽

Cloud Radiative Forcing ◽

Cloud Forcing ◽

Aerosol Forcing ◽

Albedo Effect

Abstract. Cloud radiative forcing is a very important concept to understand what kind of role the clouds play in climate change with thermal effect or albedo effect. In spite of that much progress has been achieved, the clouds are still poorly described in the climate models. Due to the complex aerosol-cloud-radiation interactions, high surface albedo of snow and ice cover, and without solar radiation in long period of the year, the Arctic strong warming caused by increasing greenhouse gases (as most GCMs suggested) has not been verified by the observations. In this study, we were dedicated to quantify the aerosol effect on the Arctic cloud radiative forcing by Northern Aerosol Regional Climate Model (NARCM). Major aerosol species such as Arctic haze sulphate, black carbon, sea salt, organics and dust have been included during our simulations. By inter-comparisons with the Atmospheric Radiation Measurement (ARM) data, we find surface cloud radiative forcing (SCRF) is −22 W/m2 for shortwave and 36 W/m2 for longwave. Total cloud forcing is 14 W/m2 with minimum of −35 W/m2 in early July. If aerosols are taken into account, the SCRF has been increased during winter while negative SCRF has been enhanced during summer. Our estimate of aerosol forcing is about −6 W/m2 in the Arctic.

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Spatial and temporal variability of iodine in aerosol

10.5194/egusphere-egu21-2077 ◽

2021 ◽

Author(s):

Juan Carlos Gomez Martin ◽

Alfonso Saiz-Lopez ◽

Carlos Cuevas ◽

Rafael Fernandez ◽

Benjamin Gilfedder ◽

...

Keyword(s):

Sea Water ◽

Surface Ozone ◽

Temporal Trends ◽

The Arctic ◽

Spatial And Temporal Variability ◽

Total Iodine ◽

Middle Latitudes ◽

Air Interface ◽

Latitudinal Dependence ◽

The Pacific

In this work we describe the compilation and homogenization of an extensive dataset of aerosol total iodine field observations in the period between 1963 and 2018 and we discuss its spatial and temporal trends. Total iodine in aerosol shows a distinct latitudinal dependence, with an enhancement towards the northern hemisphere (NH) tropics and lower values towards the poles. Longitudinally, there is some indication of a wave-one profile in the Tropics, which peaks in the Atlantic and shows a minimum in the Pacific, following the well-known wave-one longitudinal variation of tropical tropospheric ozone. These spatial trends result from the global distribution of the main oceanic iodine source to the atmosphere (the reaction of surface ozone with aqueous iodide on the sea water-air interface). New data from Antarctica show that the south polar seasonal variation of iodine in aerosol mirrors that observed previously in the Arctic, with two equinoctial maxima and the dominant maximum occurring in spring. While no clear seasonal variability is observed in NH middle latitudes, there is an indication of different seasonal cycles in the NH tropical Atlantic and Pacific. A weak positive long-term trend is observed in the tropical annual averages, which is consistent with an enhancement of the anthropogenic ozone-driven global oceanic source of iodine over the last 50 years.

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Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0180.1 ◽

2021 ◽

Vol 22 (4) ◽

pp. 971-995

Author(s):

Abhishekh Kumar Srivastava ◽

Richard Grotjahn ◽

Paul Aaron Ullrich ◽

Mojtaba Sadegh

Keyword(s):

Climate Models ◽

Climate Model ◽

Synthetic Data ◽

Extreme Value Distribution ◽

Precipitation Intensity ◽

Spatial And Temporal Variability ◽

Return Periods ◽

Model Data ◽

Estimation Uncertainty ◽

Almost All

AbstractTraditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.

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The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming

10.5194/egusphere-egu21-16310 ◽

2021 ◽

Author(s):

Stephanie Hay ◽

Paul Kusnher

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

Climate Model Simulations ◽

Arctic Sea Ice Loss

Antarctic sea ice has gradually increased in extent over the forty-year-long satellite record, in contrast with the clear decrease in sea-ice extent seen in the Arctic over the same time period. However, state-of-the-art climate models ubiquitously project Antarctic sea-ice to decrease over the coming century, much as they do for Arctic sea-ice. Several recent years have also seen record low Antarctic sea-ice. It is therefore of interest to understand what the climate response to Antarctic sea-ice loss will be.&#160;We have carried out new fully coupled climate model simulations to assess the response to sea-ice loss in either hemisphere separately or coincidentally under different albedo parameter settings to determine the relative importance of each. By perturbing the albedo of the snow overlying the sea ice and the albedo of the bare sea ice, we obtain a suite of simulations to assess the linearity and additivity of sea-ice loss. We find the response to sea-ice loss in each hemisphere exhibits a high degree of additivity, and can simply be decomposed into responses due to loss in each hemisphere separately.&#160;We find that the response to Antarctic sea-ice loss exceeds that of Arctic sea-ice loss in the tropics, and that Antarctic sea-ice loss leads to statistically significant Arctic warming, while the opposite is not true.With these new simulations and one in which CO2 is instantaneously doubled , we can further characterize the response to sea-ice loss from each hemisphere using an extension to classical pattern scaling that includes three controlling parameters. This allows us to simultaneously compute the sensitivity patterns to Arctic sea-ice loss, Antarctic sea-ice loss, and to tropical warming. The statistically significant response to Antarctic sea-ice loss in the Northern Hemisphere extratropics is found to be mediated by tropical warming and small amounts of Arctic sea-ice loss.

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