Forgetting to perform intended actions can have major consequences, including loss of life in some situations. Laboratory research on prospective memory—remembering (and sometimes forgetting) to ...perform deferred intentions—is growing rapidly, thanks to new laboratory paradigms that are being used to uncover underlying cognitive mechanisms. Everyday situations and workplace situations in fields such as aviation and medicine, which have been studied less extensively, reveal aspects of prospective remembering that have both practical and theoretical implications, which are discussed here. Several types of situations in which individuals are vulnerable to forgetting intentions, but which have not been studied extensively in laboratory research, are described, and ways to reduce vulnerability to forgetting are suggested.
Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses ...vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
During the 1990s, ocean sampling expeditions were carried out as part of the World Ocean Circulation Experiment (WOCE), the Joint Global Ocean Flux Study (JGOFS), and the Ocean Atmosphere Carbon ...Exchange Study (OACES). Subsequently, a group of U.S. scientists synthesized the data into easily usable and readily available products. This collaboration is known as the Global Ocean Data Analysis Project (GLODAP). Results were merged into a common format data set, segregated by ocean. For comparison purposes, each ocean data set includes a small number of high‐quality historical cruises. The data were subjected to rigorous quality control procedures to eliminate systematic data measurement biases. The calibrated 1990s data were used to estimate anthropogenic CO2, potential alkalinity, CFC watermass ages, CFC partial pressure, bomb‐produced radiocarbon, and natural radiocarbon. These quantities were merged into the measured data files. The data were used to produce objectively gridded property maps at a 1° resolution on 33 depth surfaces chosen to match existing climatologies for temperature, salinity, oxygen, and nutrients. The mapped fields are interpreted as an annual mean distribution in spite of the inaccuracy in that assumption. Both the calibrated data and the gridded products are available from the Carbon Dioxide Information Analysis Center. Here we describe the important details of the data treatment and the mapping procedure, and present summary quantities and integrals for the various parameters.
Sea ice in the Arctic is one of the most rapidly changing components of the global climate system. Over the past few decades, summer areal extent has declined over 30%, and all months show ...statistically significant declining trends. New satellite missions and techniques have greatly expanded information on sea ice thickness, but many uncertainties remain in the satellite data and long‐term records are sparse. However, thickness observations and other satellite‐derived data indicate a 40% decline in thickness, due in large part to the loss of thicker, older ice cover. The changes in sea ice are happening faster than models have projected. With continued increasing temperatures, summer ice‐free conditions are likely sometime in the coming decades, though there are substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases. The changes in Arctic sea ice are already having an impact on flora and fauna in the Arctic. Some species will face increasing challenges in the future, while new habitat will open up for other species. The changes are also affecting people living and working in the Arctic. Native communities are facing challenges to their traditional ways of life, while new opportunities open for shipping, fishing, and natural resource extraction. Significant progress has been made in recent years in understanding of Arctic sea ice and its role in climate, the ecosystem, and human activities. However, significant challenges remain in furthering the knowledge of the processes, impacts, and future evolution of the system.
Key Points
Arctic sea ice is rapidly changing; thinning and summer extents are decreasingChanges are faster than model forecasts; feedbacks play a key roleChanging sea ice is impacting biology and human activity in the Arctic
In September 2012, Arctic sea ice cover reached a record minimum for the satellite era. The following winter the sea ice quickly returned, carrying through to the summer when ice extent was 48% ...greater than the same time in 2012. Most of this rebound in the ice cover was in the Chukchi and Beaufort Seas, areas experiencing the greatest decline in sea ice over the last three decades. A variety of factors, including ice dynamics, oceanic and atmospheric heat transport, wind, and solar insolation anomalies, may have contributed to the rebound. Here we show that another factor, below-average Arctic cloud cover in January-February 2013, resulted in a more strongly negative surface radiation budget, cooling the surface and allowing for greater ice growth. More thick ice was observed in March 2013 relative to March 2012 in the western Arctic Ocean, and the areas of ice growth estimated from the negative cloud cover anomaly and advected from winter to summer with ice drift data, correspond well with the September ice concentration anomaly pattern. Therefore, decreased wintertime cloud cover appears to have played an important role in the return of the sea ice cover the following summer, providing a partial explanation for large year-to-year variations in an otherwise decreasing Arctic sea ice cover.
The injection of radiocarbon (14C) into the atmosphere by nuclear weapons testing in the 1950s and 1960s has provided a powerful tracer to investigate ocean physical and chemical processes. While the ...oceanic uptake of bomb‐derived 14C was primarily controlled by air‐sea exchange in the early decades after the bomb spike, we demonstrate that changes in oceanic 14C are now primarily controlled by shallow‐to‐deep ocean exchange, i.e., the same mechanism that governs anthropogenic CO2 uptake. This is a result of accumulated bomb 14C uptake that has rapidly decreased the air‐sea gradient of 14C/C (Δ14C) and shifted the main reservoir of bomb 14C from the atmosphere to the upper ocean. The air‐sea Δ14C gradient, reduced further by fossil fuel dilution, is now weaker than before weapons testing in most regions. Oceanic 14C, and particularly its temporal change, can now be used to study the oceanic uptake of anthropogenic CO2. We examine observed changes in oceanic Δ14C between the WOCE/SAVE (1988–1995) and the CLIVAR (2001–2007) eras and simulations with two ocean general circulation models, the Community Climate System Model (CCSM) and the Estimating the Circulation and Climate of the Ocean Model (ECCO). Observed oceanic Δ14C and its changes between the 1980s–90s and 2000s indicate that shallow‐to‐deep exchange is too efficient in ECCO and too sluggish in CCSM. These findings suggest that mean global oceanic uptake of anthropogenic CO2 between 1990 and 2007 is bounded by the ECCO‐based estimate of 2.3 Pg C yr−1 and the CCSM‐based estimate of 1.7 Pg C yr−1.
Key Points
Interior ocean processes are now the main influence on oceanic radiocarbon
Oceanic radiocarbon can now be used to evaluate ocean models at global scales
14C data suggest the ocean CO2 sink is between 1.7 and 2.3 PgC/yr for 1990‐2007
Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and ...accurate projections of future sea ice conditions. Here, we present a monthly, Arctic-basin-wide, and 25 km resolution Eulerian estimation of thermodynamic and dynamic effects on wintertime sea ice thickness from 2010–2021. Estimates of thermodynamic growth rate are determined by coupling passive microwave-retrieved snow–ice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from a weekly Alfred Wegener Institute (AWI) European Space Agency (ESA) CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) combination product (CS2SMOS), and dynamic effects are calculated as their difference. The dynamic effects are further separated into advection and residual effects using a sea ice motion dataset. Our results show new detail in these fields and, when summed to a basin-wide or regional scale, are in line with previous studies. Across the Arctic, dynamic effects are negative and about one-fourth the magnitude of thermodynamic growth. Thermodynamic growth varies from less than 0.1 m per month in the central Arctic to greater than 0.3 m per month in the seasonal ice zones. High positive dynamic effects of greater than 0.1 m per month, twice that of thermodynamic growth or more in some areas, are found north of the Canadian Arctic Archipelago, where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of less than −0.2 m per month are found where the Transpolar Drift originates, nearly equal to and opposite the thermodynamic effects in these regions. Monthly results compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019–2020. Couplets of deformation and advection effects with opposite signs are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows residual deformation effects and overall dynamic effects increasing as the winter season progresses.
We introduce a composite tracer for the marine system, Alk*, that has a global distribution primarily determined by CaCO3 precipitation and dissolution. Alk* is also affected by riverine alkalinity ...from dissolved terrestrial carbonate minerals. We estimate that the Arctic receives approximately twice the riverine alkalinity per unit area as the Atlantic, and 8 times that of the other oceans. Riverine inputs broadly elevate Alk* in the Arctic surface and particularly near river mouths. Strong net carbonate precipitation results in low Alk* in subtropical gyres, especially in the Indian and Atlantic oceans. Upwelling of dissolved CaCO3-rich deep water elevates North Pacific and Southern Ocean Alk*. We use the Alk* distribution to estimate the variability of the calcite saturation state resulting from CaCO3 cycling and other processes. We show that regional differences in surface calcite saturation state are due primarily to the effect of temperature differences on CO2 solubility and, to a lesser extent, differences in freshwater content and air-sea disequilibria. The variations in net calcium carbonate cycling revealed by Alk* play a comparatively minor role in determining the calcium carbonate saturation state.
Sea ice is a key component of the Arctic climate system,
and has impacts on global climate. Ice concentration, thickness, and volume
are among the most important Arctic sea ice parameters. This study ...presents
a new record of Arctic sea ice thickness and volume from 1984 to 2018 based
on an existing satellite-derived ice age product. The relationship between
ice age and ice thickness is first established for every month based on
collocated ice age and ice thickness from submarine sonar data (1984–2000)
and ICESat (2003–2008) and an empirical ice growth model. Based on this
relationship, ice thickness is derived for the entire time period from the
weekly ice age product, and the Arctic monthly sea ice volume is then
calculated. The ice-age-based thickness and volume show good agreement in
terms of bias and root-mean-square error with submarine, ICESat, and
CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in
February–March and October–November. More detailed comparisons with
independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI,
and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The
ratios of the ice volume uncertainties to the mean range from 21 % to
29 %. Analysis of the derived data shows that the ice-age-based sea ice
volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to
2018, stronger than the trends from other datasets. Of the factors affecting
the sea ice volume trends, changes in sea ice thickness contribute more than
changes in sea ice area, with a contribution of at least 80 % from changes
in sea ice thickness from November to May and nearly 50 % in August and
September, while less than 30 % is from changes in sea ice area in all
months.
As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is ...clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.
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