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William L. Chapman
and
John E. Walsh

Abstract

Simulations of Arctic surface air temperature and sea level pressure by 14 global climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are synthesized in an analysis of biases and trends. Simulated composite GCM surface air temperatures for 1981–2000 are generally 1°–2°C colder than corresponding observations with the exception of a cold bias maximum of 6°–8°C in the Barents Sea. The Barents Sea bias, most prominent in winter and spring, occurs in 12 of the 14 GCMs and corresponds to a region of oversimulated sea ice. All models project a twenty-first-century warming that is largest in the autumn and winter, although the rates of the projected warming vary considerably among the models. The across-model and across-scenario uncertainties in the projected temperatures are comparable through the first half of the twenty-first century, but increases in variability associated with the choice of scenario begin to outpace increases in across-model variability by about the year 2070. By the end of the twenty-first century, the cross-scenario variability is about 50% greater than the across-model variability. The biases of sea level pressure are smaller than in the previous generation of global climate models, although the models still show a positive bias of sea level pressure in the Eurasian sector of the Arctic Ocean, surrounded by an area of negative pressure biases. This bias is consistent with an inability of the North Atlantic storm track to penetrate the Eurasian portion of the Arctic Ocean. The changes of sea level pressure projected for the twenty-first century are negative over essentially the entire Arctic. The most significant decreases of pressure are projected for the Bering Strait region, primarily in autumn and winter.

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John E. Walsh
and
William L. Chapman

Abstract

Associations between cloudiness, radiative fluxes, and surface air temperature in the central Arctic are evaluated from 1) measurements made at Russian drifting ice stations, and 2) atmospheric reanalyses of the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF). In the ice station data, cloudiness is associated with an increase of downward longwave radiation in all months and an increase of net (downward minus upward) total radiation from September through March. The surface air temperatures under overcast skies are 6°–9°C higher than under clear skies during September–March, and the differences are even larger when the observations are stratified by wind as well as cloudiness. The warming by the radiative flux enhancement after a transition from clear skies to overcast has a 1–2-day timescale, while the cooling after the transition to clear skies has a somewhat shorter timescale. The NCEP reanalysis exaggerates slightly the association between cloudiness and surface air temperature, while the ECMWF reanalysis shows a considerably weaker association.

The maximum cloud-radiative forcing (MCRF), defined as the difference between the ice station measurements of net surface radiation under cloudy and clear skies, ranges from −59 W m−2 in June to positive values of 20–30 W m−2 in September–March. The annual mean is small but positive, 3 W m−2, despite the approximately three-month summer period of substantially negative MCRF. These findings are consistent with the conventional cloud-radiative forcing obtained in earlier studies using satellite data and one-dimensional models of the Arctic atmosphere and sea ice. Neither reanalysis captures the seasonality of the observationally deduced effects of clouds on surface radiation. The NCEP reanalysis does not capture the seasonality of the actual cloudiness (as defined by the reported cloud fractions), while the ECMWF reanalysis does not show an impact of clouds on the surface solar flux.

Issues needing further attention in the model–data comparison are the effects of surface heterogeneities, the characterization of Arctic clouds, the formulational reasons for the discrepancies between the model-derived reanalyses and the observational data, and the implications for model-derived projections of climate change in the Arctic.

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William L. Chapman
and
John E. Walsh

Abstract

Monthly surface air temperatures from land surface stations, automatic weather stations, and ship/buoy observations from the high-latitude Southern Hemisphere are synthesized into gridded analyses at a resolution appropriate for applications ranging from spatial trend analyses to climate change impact assessments. Correlation length scales are used to enhance information content while limiting the spatial extent of influence of the sparse data in the Antarctic region. The correlation length scales are generally largest in summer and over the Antarctic continent, while they are shortest over the winter sea ice. Gridded analyses of temperature anomalies, limited to regions within a correlation length scale of at least one observation, are constructed and validated against observed temperature anomalies in single-station-out experiments. Trends calculated for the 1958–2002 period suggest modest warming over much of the 60°–90°S domain. All seasons show warming, with winter trends being the largest at +0.172°C decade−1 while summer warming rates are only +0.045°C decade−1. The 45-yr temperature trend for the annual means is +0.082°C decade−1 corresponding to a +0.371°C temperature change over the 1958–2002 period of record. Trends computed using these analyses show considerable sensitivity to start and end dates, with trends calculated using start dates prior to 1965 showing overall warming, while those using start dates from 1966 to 1982 show net cooling over the region. Because of the large interannual variability of temperatures over the continental Antarctic, most of the continental trends are not statistically significant. However, the statistically significant warming over the Antarctic Peninsula is the strongest and most seasonally robust in the spatial patterns of temperature change.

Composite (11-model) global climate model (GCM) simulations for 1958–2002 with forcing from historic aerosol and greenhouse gas concentrations show warming patterns and magnitudes similar to the corresponding observed trends for the 45-yr period. GCM projections for the rest of the twenty-first century, however, discontinue the pattern of strongest warming over the Antarctic Peninsula, but instead show the strongest warming over the Antarctic continent.

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John E. Walsh
and
William L. Chapman

Abstract

Because much of the deep water of the world's oceans forms in the high-latitude North Atlantic, the potential climatic leverage of salinity and temperature anomalies in this region is large. Substantial variations of sea ice have accompanied North Atlantic salinity and temperature anomalies, especially the extreme and long-lived “Great Salinity Anomaly” of the late 1960s and early 1970s. Atmospheric pressure data are used hem to show that the local forcing of high-latitude North Atlantic Ocean fluctuations is augmented by antecedent atmospheric circulation anomalies over the central Arctic. These circulation anomalies are consistent with enhanced wind-forcing of thicker, older ice into the Transpolar Drift Stream and an enhanced export of sea ice (fresh water) from the Arctic into the Greenland Sea prior to major episodes of ice severity in the Greenland and Iceland seas. An index of the pressure difference between southern Greenland and the Arctic-Asian coast reached its highest value of the twentieth century during the middle-to-late 1960s, the approximate time of the earliest observation documentation of the Great Salinity Anomaly.

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John E. Walsh
and
William L. Chapman

Abstract

The circulation of the Arctic atmosphere undergoes large fluctuations about its monthly and annual means. The statistics of Arctic sea level pressure and temperature are evaluated in order to place Arctic atmospheric variability into the context of fluctuations elsewhere. The persistence of monthly sea level pressure anomalies in the Arctic is smaller than in the subtropics but greater than in middle latitudes. This persistence is strongest in winter. Air temperature anomalies are less persistent in the Arctic than in lower latitudes, except during the autumn freeze-up season. Monthly Arctic pressure anomalies show a relatively strong association with concurrent anomalies in the North Atlantic, especially during the winter half of the year. Associations with North Pacific anomalies are weak. During the past twenty years, the greatest warming has occurred over Alaska, the North Atlantic marginal ice zone, and north central Asia. Cooling has occurred over much of Europe, especially Scandinavia. The COADS sea surface temperature changes support the pattern of temperature change derived from land station data. The pattern of recent high-latitude temperature change is consistent with and at least partially attributable to corresponding changes in the sea level pressure (gradient wind) field.

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John E. Walsh
,
William L. Chapman
, and
Diane H. Portis

Abstract

Arctic radiative fluxes, cloud fraction, and cloud radiative forcing are evaluated from four currently available reanalysis models using data from the North Slope of Alaska (NSA) Barrow site of the Atmospheric Radiation Measurement Program (ARM). A primary objective of the ARM–NSA program is to provide a high-resolution dataset of direct measurements of Arctic clouds and radiation so that global climate models can better parameterize high-latitude cloud radiative processes. The four reanalysis models used in this study are the 1) NCEP–NCAR global reanalysis, 2) 40-yr ECMWF Re-Analysis (ERA-40), 3) NCEP–NCAR North American Regional Reanalysis (NARR), and 4) Japan Meteorological Agency and Central Research Institute of Electric Power Industry 25-yr Reanalysis (JRA25). The reanalysis models simulate the radiative fluxes well if/when the cloud fraction is simulated correctly. However, the systematic errors of climatological reanalysis cloud fractions are substantial. Cloud fraction and radiation biases show considerable scatter, both in the annual mean and over a seasonal cycle, when compared to those observed at the ARM–NSA. Large seasonal cloud fraction biases have significant impacts on the surface energy budget. Detailed comparisons of ARM and reanalysis products reveal that the persistent low-level cloud fraction in summer is particularly difficult for the reanalysis models to capture creating biases in the shortwave radiation flux that can exceed 160 W m−2. ERA-40 is the best performer in both shortwave and longwave flux seasonal representations at Barrow, largely because its simulation of the cloud coverage is the most realistic of the four reanalyses. Only two reanalyses (ERA-40 and NARR) capture the observed transition from positive to negative surface net cloud radiative forcing during a 2–3-month period in summer, while the remaining reanalyses indicate a net warming impact of Arctic clouds on the surface energy budget throughout the entire year. The authors present a variable cloud radiative forcing metric to diagnose the erroneous impact of reanalysis cloud fraction on the surface energy balance. The misrepresentations of cloud radiative forcing in some of the reanalyses are attributable to errors in both simulated cloud amounts and the models’ radiative response to partly cloudy conditions.

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John E. Walsh
,
William L. Chapman
, and
Timothy L. Shy

Abstract

Arctic sea level pressure data from the period of the Arctic Ocean Buoy Program show a significant decrease in the annual mean. In every calendar month, the annual mean is lower in the second half of the 1979–1994 period than in the first. The changes of the annual means are larger in the central Arctic than anywhere else in the Northern Hemisphere. The decreases are largest and statistically significant in the autumn and winter. The annual anomalies became negative relative to the 16-yr mean in the 1980s and have been negative in every year since 1988. Correspondingly, the mean anticyclone in the Arctic pressure field has weakened and the vorticity of the gradient wind field over the central Arctic Ocean has become more positive than at any time in the past several decades. The pressure decrease, which has been compensated by pressure increases over the subpolar oceans, implies that the wind forcing of sea ice contains an enhanced cyclonic component relative to earlier decades.

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Xin Tao
,
John E. Walsh
, and
William L. Chapman

Abstract

Simulations of Arctic temperatures by 19 general circulation models are examined as part of a diagnostic subproject of the Atmospheric Model Intercomparison Project (AMIP). The forcing of all the models by observed sea surface temperatures and sea ice from a 10-yr period (1979–1988) permits comparative evaluations of the model biases as well as the models’ simulations of the interannual variations contained in the observational data. The models capture the latitudinal and seasonal variability of surface air temperatures in the Arctic, although a cold bias of −3.3°C (std dev = 3.4°C) is apparent over northern Eurasia during spring, especially in the models that do not include vegetative masking of the high-albedo snow. The 19-model mean bias over northern North America is less than 2°C in all seasons. Over the Arctic Ocean, the spring temperatures generally have a warm bias that averages 3.0 (std dev = 2.9°C), although the bias is smaller in the models in which the prescribed albedo of sea ice is highest. For the summer season, correlations between simulated cloudiness and surface air temperatures are negative and statistically significant, but the corresponding correlations for the winter months are small and statistically insignificant The models without gravity wave drag are generally colder than the other models at the Arctic surface, especially during autumn.

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MyeongHee Han
,
Igor Kamenkovich
,
Timour Radko
, and
William E. Johns

Abstract

This study aims to explore the relationship between air–sea density flux and isopycnal meridional overturning circulation (MOC), using the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) model projections of the twenty-first-century climate. The focus is on the semiadiabatic component of MOC beneath the mixed layer; this component is described using the concept of the push–pull mode, which represents the combined effects of the adiabatic push into the deep ocean in the Northern Hemisphere and the pull out of the deep ocean in the Southern Hemisphere. The analysis based on the GFDL Climate Model version 2.1 (CM2.1) simulation demonstrates that the push–pull mode and the actual isopycnal MOC at the equator evolve similarly in the deep layers, with their maximum transports decreasing by 4–5 Sv (1 Sv ≡ 106 m3 s−1) during years 2001–2100. In particular, the push–pull mode and actual isopycnal MOC are within approximately 10% of each other at the density layers heavier than 27.55 kg m−3, where the reduction in the MOC strength is the strongest. The decrease in the push–pull mode is caused by the direct contribution of the anomalous heat, rather than freshwater, surface fluxes. The agreement between the deep push–pull mode and MOC in the values of linear trend and variability on time scales longer than a decade suggests a largely adiabatic pole-to-pole mechanism for these changes. The robustness of the main conclusions is further explored in additional model simulations.

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Amanda H. Lynch
,
William L. Chapman
,
John E. Walsh
, and
Gunter Weller

Abstract

An Arctic region climate system model has been developed to simulate coupled interactions among the atmosphere, sea ice, ocean, and land surface of the western Arctic. The atmospheric formulation is based upon the NCAR regional climate model RegCM2, and includes the NCAR Community Climate Model Version 2 radiation scheme and the Biosphere–Atmosphere Transfer Scheme. The dynamic–thermodynamic sea ice model includes the Hibler–Flato cavitating fluid formulation and the Parkinson–Washington thermodynamic scheme linked to a mixed-layer ocean.

Arctic winter and summer simulations have been performed at a 63 km resolution, driven at the boundaries by analyses compiled at the European Centre for Medium-Range Weather Forecasts. While the general spatial patterns are consistent with observations, the model shows biases when the results are examined in detail. These biases appear to be consequences in part of the lack of parameterizations of ice dynamics and the ice phase in atmospheric moist processes in winter, but appear to have other causes in summer.

The inclusion of sea ice dynamics has substantial impacts on the model results for winter. Locally, the fluxes of sensible and latent heat increase by over 100 W m−2 in regions where offshore winds evacuate sea ice. Averaged over the entire domain, these effects result in root-mean-square differences of sensible heat flux and temperatures of 15 W m−2 and 2°C. Other monthly simulations have addressed the model sensitivity to the subgrid-scale moisture treatment, to ice-phase physics in the explicit moisture parameterization, and to changes in the relative humidity threshold for the autoconversion of cloud water to rainwater. The results suggest that the winter simulation is most sensitive to the inclusion of ice phase physics, which results in an increase of precipitation of approximately 50% and in a cooling of several degrees over large portions of the domain. The summer simulation shows little sensitivity to the ice phase and much stronger sensitivity to the convective parameterization, as expected.

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