Abstract

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial system is responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992–2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinder all-sky temperatures (APP); 4) National Centers for Environmental Prediction– National Center for Atmospheric Research (NCEP–NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effect on the output from hydrologic models, and several different datasets should be used for a robust hydrologic assessment.

Abstract

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska.

Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.

Abstract

Analysis of weekly sea ice charts produced by the U.S. National Ice Center from 1976 to 2007 indicates large interannual variations in the averaged winter landfast ice extent around the Arctic Basin. During the 32-yr period of the record, landfast ice cover was relatively extensive from the early to mid-1980s but since then has declined in many coastal regions of the Arctic, particularly after the early 1990s. While the Barents, Baltic, and Bering Seas show increases in landfast ice area, the overall change for the Northern Hemisphere is negative, about −12.27 (±2.8) × 10³ km² yr⁻¹, or −7 (±1.5)% decade⁻¹ relative to the long-term mean. Except in a few coastal regions, the seasonal duration of landfast ice is shorter overall, particularly in the Laptev, East Siberian, and Chukchi Seas. The decreased winter landfast ice extent is associated with some notable changes in ice growth and melt patterns, in particular the slowed landfast ice expansion during fall and early winter since 1990. The observed changes in Arctic landfast ice could have profound impacts on the Arctic coasts. The challenge is to understand and project the responses of the whole coastal ecosystem to changing ice cover and Arctic warming.

Abstract

Continuous observation of sea ice using a small robotic aircraft called the Aerosonde was made over the Arctic Ocean from Barrow, Alaska, on 20–21 July 2003. Over a region located 350 km off the coast of Barrow, images obtained from the aircraft were used to characterize the sea ice and to determine the fraction of melt ponds on both multiyear and first-year ice. Analysis of the data indicates that melt-pond fraction increased northward from 20% to 30% as the ice fraction increased. However, the fraction of ponded ice was over 30% in the multiyear ice zone while it was about 25% in the first-year ice zone. A comparison with a satellite microwave product showed that the ice concentration derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) has a negative bias of 7% due to melt ponds. These analyses demonstrate the utility of recent advances in unmanned aerial vehicle (UAV) technology for monitoring and interpreting the spatial variations in the sea ice with melt ponds.

Abstract

Generation and sample applications of an integrated set of remotely sensed products for investigations of Arctic climate are described. Cloud fraction, ice surface temperature, surface albedo, downwelling radiative fluxes, ice motion vectors, and cloud properties such as optical depth, phase, and droplet effective radius are estimated from calibrated and navigated AVHRR 1.1-km imagery of the Arctic Beaufort Sea region for June 1992 through July 1993. The processing strategy and characteristics of the products are reviewed. The utility of this type of multiparameter dataset for modeling applications and process studies is illustrated using simple examples of an albedo parameterization, sensible heat flux calculation, and sea ice advection.

Abstract

Accurate estimates of the spatial and temporal variation in terrestrial water and energy fluxes and mean states are important for simulating regional hydrology and biogeochemistry in high-latitude regions. Furthermore, it is necessary to develop high-resolution hydroclimatological datasets at finer spatial resolutions than are currently available from global analyses. This study uses a regional climate model (RCM) to develop a hydroclimatological dataset for hydrologic and ecological application in the Western Arctic. The fifth-generation Penn State–NCAR Mesoscale Model (MM5) forced by global reanalysis products at the boundaries is used to perform 12 yr of simulation (1990 through 2001) over the Western Arctic. An analysis that compares the RCM simulations with independent observationally derived data sources is conducted to evaluate the temporal and spatial distribution of the mean states, variability, and trends during the period of simulation. The RCM simulation of sea level pressure agrees well with the reanalysis in terms of mean states, seasonality, and interannual variability. The RCM also simulates major spatial patterns of the observed climatology of surface air temperature (SAT), but RCM SAT is generally colder in the summertime and warmer in the wintertime in comparison with other datasets. Although there are biases in the mean state of SAT, the RCM simulations of the seasonal and interannual variability of SAT are similar to variability in observationally derived datasets. The RCM also simulates general spatial patterns of observed rainfall, but the modeled mean state of precipitation is characterized by large biases relative to observationally derived datasets. In particular, the RCM tends to overestimate coastal region precipitation but underestimates precipitation in the interior of the Western Arctic. The Arctic terrestrial surface climate trends for the period of 1992 to 2001 of the RCM are similar to those derived from observations, with sea level pressure decreasing 0.15 hPa decade⁻¹, SAT increasing 0.10°C decade⁻¹, and precipitation decreasing slightly in the RCM simulations. In summary, the RCM dataset produced in this study represents an improvement over data currently available from large-scale global reanalysis and provides a consistent meteorological forcing dataset for hydrologic and ecological applications.

Abstract

The Climate System Model (CSM) consists of atmosphere, ocean, land, and sea-ice components linked by a flux coupler, which computes fluxes of energy and momentum between components. The sea-ice component consists of a thermodynamic formulation for ice, snow, and leads within the ice pack, and ice dynamics using the cavitating-fluid ice rheology, which allows for the compressive strength of ice but ignores shear viscosity.

The results of a 300-yr climate simulation are presented, with the focus on sea ice and the atmospheric forcing over sea ice in the polar regions. The atmospheric model results are compared to analyses from the European Centre for Medium-Range Weather Forecasts and other observational sources. The sea-ice concentrations and velocities are compared to satellite observational data.

The atmospheric sea level pressure (SLP) in CSM exhibits a high in the central Arctic displaced poleward from the observed Beaufort high. The Southern Hemisphere SLP over sea ice is generally 5 mb lower than observed. Air temperatures over sea ice in both hemispheres exhibit cold biases of 2–4 K. The precipitation-minus-evaporation fields in both hemispheres are greatly improved over those from earlier versions of the atmospheric GCM.

The simulated ice-covered area is close to observations in the Southern Hemisphere but too large in the Northern Hemisphere. The ice concentration fields show that the ice cover is too extensive in the North Pacific and subarctic North Atlantic Oceans. The interannual variability of the ice area is similar to observations in both hemispheres. The ice thickness pattern in the Antarctic is realistic but generally too thin away from the continent. The maximum thickness in the Arctic occurs against the Bering Strait, not against the Canadian Archipelago as observed. The ice velocities are stronger than observed in both hemispheres, with a consistently greater turning angle (to the left) in the Southern Hemisphere. They produce a northward ice transport in the Southern Hemisphere that is 3–4 times the satellite-derived value. Sensitivity tests with the sea-ice component show that both the pattern of wind forcing in CSM and the air-ice drag parameter used contribute to the biases in thickness, drift speeds, and transport. Plans for further development of the ice model to incorporate a viscous-plastic ice rheology are presented.

In spite of the biases of the sea-ice simulation, the 300-yr climate simulation exhibits only a small degree of drift in the surface climate without the use of flux adjustment. This suggests a robust stability in the simulated climate in the presence of significant variability.

Abstract

Measurements from the Russian “North Pole” series of drifting stations, the United States drifting stations“T-3” and “Arlis II,” land stations, and, where necessary, over the northern North Atlantic and coastal Greenland, empirically derived values from earlier Russian studies are used to compile a new gridded monthly climatology of global (downwelling shortwave) radiation for the region north of 65°N. Spatio-temporal patterns of fluxes and effective cloud transmittance are examined and comparisons are made with fields from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and those derived from the International Satellite Cloud Climatology Project (ISCCP) C2 (monthly) cloud product.

All months examined (March–October) show peak fluxes over the Greenland ice sheet. March, September, and October feature a strong zonal component. Other months exhibit an asymmetric pattern related to cloud fraction and optical depth, manifested by an Atlantic side flux minimum. For June, the month of maximum insolation, fluxes increase from less than 200 W m⁻² in the Norwegian and Barents seas to more than 300 W m⁻² over the Pacific side of central Arctic Ocean extending into the Beaufort Sea. June fluxes of more than 340 W m⁻² are found over the Greenland ice sheet. Effective cloud transmittance, taken as the ratio of the observed flux to the modeled clear sky flux, is examined for April–September. Values for the Atlantic sector range from 0.50–0.60, contrasting with the central Arctic Ocean where values peak in April at 0.75–0.80, falling to 0.60–0.65 during late summer and early autumn. A relative Beaufort Sea maximum is well expressed during June. The NCEP–NCAR and ISCCP products capture 50%–60% of the observed spatial variance in global radiation during most months. However, the NCEP–NCAR fluxes are consistently high, with Arctic Ocean errors in excess of 60 W m⁻² during summer, reflecting problems in modeled cloud cover. ISCCP fluxes compare better in terms of magnitude.

Abstract

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

A summary is presented of the Surface Heat Budget of the Arctic Ocean (SHEBA) project, with a focus on the field experiment that was conducted from October 1997 to October 1998. The primary objective of the field work was to collect ocean, ice, and atmospheric datasets over a full annual cycle that could be used to understand the processes controlling surface heat exchanges—in particular, the ice–albedo feedback and cloud–radiation feedback. This information is being used to improve formulations of arctic ice–ocean–atmosphere processes in climate models and thereby improve simulations of present and future arctic climate. The experiment was deployed from an ice breaker that was frozen into the ice pack and allowed to drift for the duration of the experiment. This research platform allowed the use of an extensive suite of instruments that directly measured ocean, atmosphere, and ice properties from both the ship and the ice pack in the immediate vicinity of the ship. This summary describes the project goals, experimental design, instrumentation, and the resulting datasets. Examples of various data products available from the SHEBA project are presented.