Arctic clouds and radiative fluxes in large-scale atmospheric reanalyses
John E. Walsh, William L. Chapman, Diane H. Portis
1

University of Illinois, Urbana, Illinois USA
Correspondence: walsh@atmos.uiuc.edu

INTRODUCTION
Despite their potentially broad impact on the global climate, the evolution and formation of Arctic clouds and theirassociated radiative interactions are complex and poorly understood. This is due to several factors, e.g. high albedo
of the snow/ice surface, the lack of solar radiation during the cold season, the extremely cold and dry conditions, andthe presence of temperature and humidity inversions. In summer, there is often a multi-layered cloud regime in thelowest kilometer of the atmosphere, with an apparent decoupling of the upper and lower layers (Stamnes et al. 1999).
The physical processes involved in forming these ubiquitous and persistent multi-layered clouds are not clear.
Recently a major research activity, the Atmospheric Radiation Measurement (ARM) program, was undertaken toprovide comprehensive observational datasets to document the physical processes in the Arctic involving clouds,
radiation, and the surface energy budget. ARM is an ongoing multi-year atmospheric measurement and modelingproject directed toward improved understanding of the processes that affect atmospheric radiation with a particularfocus on cloud radiative feedback. The North Slope of Alaska (NSA) Cloud and Radiation Testbed site at Barrow,
Alaska is one of several intensive sites for the ARM project. A primary objective of the NSA site is to provide highspatial- and temporal-density measurements of Arctic clouds and radiation designed to elucidate high-latitude
processes and effectively incorporate these processes into Global Climate Models (GCMs).
While GCMs are the primary tool for projecting global climate change, validations with observed data, such as
those produced by the ARM program, are only possible in a climatological sense. That is, direct day-by-day andhour-by-hour comparisions between GCM output and direct observations are meaningless. Atmospheric reanalyses,
however, use many of the same cloud and radiative formulations as GCMs in their cloud and radiation representations,and they provide time-specific output. In the following analysis, we use the ARM/NSA data from Barrow asguidance in evaluating the arctic cloud-radiative interactions for four currently available reanalyses: National Centers
for Environmental Prediction (NCEP), European Center for Medium Range Forecasting (ERA40), North American
Regional Reanalysis (NARR) and the Japanese 25-year Reanalysis Project (JRA25). The archived reanalysis
variables we are comparing to ARM/NSA archived variables include (1) total cloud cover, (2) downwelling shortwave
solar radiative flux, (3) downwelling longwave flux, and (4) net surface radiative flux defined as the net surface
shortwave flux plus the net surface longwave flux. While radiative fluxes are archived in the reanalyses regardless ofcloud coverage, the accompanying cloud information permits the stratification of the fluxes according to cloud
coverage, thereby permitting evaluation of the cloud-radiative forcing in the subsequent results. Our assessment
seeks to identify systematic biases in cloud-radiative fields of the reanalyses across seasonal to diurnal timescales,
capitalizing upon the time-specific reference frame common to the reanalyses and the ARM measurements.
RESULTS
In order to illustrate the biases of the reanalysis-derived cloud coverage, Figure 1 shows the observed monthly
mean cloud fraction (left-panel) at the Barrow ARM site, together with corresponding cloud fractions simulated by thefour atmospheric reanalyses for the ARM-observation period of record (1999-2006). ERA40 monthly means arelimited to 1999-2002. Monthly mean values for each reanalysis were linearly interpolated from the four nearest grid
points to the ARM-Barrow location. The shapes of the seasonal cycles in the JRA25 and NCEP reanalyses arewell-simulated, but their amplitudes are muted when compared to the observed climatology. The biases in thesereanalyses throughout the year, however, are very large, ranging from -10% (winter) to -19% (summer) for the JRA25and -24% (winter) to -29% (summer) for the NCEP reanalysis. ERA40 summer cloud fractions are very wellsimulated but the smallest cloud fractions observed in winter are not simulated by the ERA40, as illustrated by a biasof +11% in winter. Winter cloud fraction biases for the NARR are similar to ERA40 (+11%) and the NARR is tooclear in summer with a bias of -16% relative to the ARM/NSA measurements.

Figure 1

Monthly mean cloud fraction (left-panel) at the Barrow-NSA site from ARM-observations(black), the ERA40 reanalysis (red), the JRA25 reanalysis (green), the NARR (blue), and theNCEP/NCAR reanalysis (violet); Monthly mean surface downwelling shortwave flux(right-panel) at the Barrow-NSA site from ARM-observations (black), the ERA40 reanalysis(red), the JRA25 reanalysis (green), the NARR (blue), and the NCEP/NCAR reanalysis (violet).

The relatively large seasonal cloud biases have significant impacts on the reanalyses’ surface energy budgets,given the unique seasonal radiative flux characteristics of the Arctic. For example, the negative summer cloud
fraction biases seen in all the reanalyses contribute to positive biases of downwelling shortwave radiation flux at thesurface in the reanalyses (Figure 1, right-panel). Shortwave flux biases averaged over June-August for the ERA40
are +6 Wm-2 but are much more significant for the JRA25 (+16 Wm-2), NARR (+25 Wm-2), and NCEP (+43 W/m-2)
reanalyses corresponding directly to the under-simulated summertime cloud fraction biases.
Comparisons of cloud fraction and longwave flux biases are presented in detail in the 6-hour averages of thedownwelling longwave flux during June of 1999-2006 from the Barrow ARM site measurements and from the NCEP
reanalysis (Figure 2). Also shown are corresponding 6-hour cloud fractions from the same two sources. The
characteristics of summertime observed Barrow cloudiness (black) are clearly missed in the NCEP reanalysis (blue).
The observed six-hour average cloud fraction distribution appears bimodal with a majority of the six-hour periods
characterized as 100% cloud covered. Less frequently, clear or nearly clear skies are recorded and, only rarely are
partly cloudy conditions are observed. In contrast, the 6-hour average cloud fractions simulated by the NCEP
reanalysis are very rarely 100% and are most often in the partly cloudy range. The impacts of simulated cloud biases
on the surface downwelling longwave flux are substantial. Periods when the NCEP reanalysis cloud fractions
closely match the observed cloud fractions (e.g., first half of June, 2006) show NCEP-simulated downwelling
longwave flux values that closely match the ARM observations. More often, however, the simulated cloud fractions
are much too low and corresponding simulated downwelling longwave fluxes are more than 50 W/m2 too low (e.g.,
first half of June 2001). The impacts on downwelling solar radiation (not shown) are even greater when the sun iswell above the horizon, i.e., during mid-day in the summer.

Figure 2

Six-hour mean June 1999-2006 surface downwelling longwave flux at the Barrow-NSA locationfrom ARM observations (black, thin) and the NCEP reanalysis (red) plotted with corresponding
six-hour mean cloud fractions for ARM observations (black, thick) and the NCEP reanalysis (blue).
Cloud fractions in lower portion of each panel range from 0 to 1.

The impact of clouds on the Arctic surface energy budget can be summarized by the total surface cloud
forcing, defined as the net radiation at the surface (shortwave + longwave) for all cloud conditions relative to the net
surface radiation under clear sky conditions for corresponding dates and times. We define clear sky conditions asless than 10% cloud fraction to ensure enough clear sky reference values throughout the annual cycle. Figure 3shows the total surface cloud radiative forcing at Barrow, AK derived from ARM observations and the four reanalyses.
The observed cloud forcing (black) illustrates that clouds at Barrow cool the surface for 3.5 months of the year (Maythrough mid August). The ERA40 simulates a similar seasonal pattern of cloud radiative forcing with the length andtiming of the cooling period nearly identical to observed. The NARR also shows a net cooling effect of clouds insummer but the period of cooling is only two months (June – July). The impact of clouds in the JRA25 and NCEPmodels is to warm the surface for the entire year, including the summer months. Clearly these two models are notadequately capturing the first order effects of clouds on the surface energy budget throughout the year. During
non-summer months, the ERA40 and NARR are also notably good performers. The exception would be a positive
net radiation bias of +10 to +15 Wm-2 for the NARR in late winter and spring. The maximum in the observed net
cloud radiative forcing occurs in autumn when the sun has set for the winter and the local air temperatures are stillrelatively warm. Again, the ERA40 and NARR are closest to capturing this subtle feature, although the magnitudes
are muted, while the NCEP and JRA25 models show nearly opposite cloud forcing signals.

Figure 3

Total surface cloud radiative forcing derived from ARM observations (black), ERA40 (red),
NCEP (blue), JRA25 (green), and NARR (orange) reanalyses at the Barrow-ARM grid cell.

In an attempt to isolate the cloud conditions responsible for contributing to the errors in total net cloud radiative
forcing above, we include a variable cloud radiative forcing (VCRF) defined as the net surface radiative flux minus
the corresponding clear sky net radiative flux as a function of cloud fraction and calendar month (Figure 4).

Figure 4

Variable cloud radiative forcing derived from ARM observations (left) and four atmosphericreanalyses at the Barrow-ARM grid cell plotted as a function of cloud fraction (ordinate) andmonth (abscissa).

For the four reanalyses, the VCRF is determined as an area-weighted mean over the 70-90°N domain, while theobserved VCRF (left panel in Figure 4) is valid for the ARM-Barrow location only. In both the VCRF derived fromARM-observations and those from the reanalyses (right-most four panels in Figure 4), VCRF is positive throughout
most of the year indicating that clouds have a net warming influence on the Arctic surface during the cold months.
For two-three months during the summer, the sign of the VCRF changes to negative, (i.e., clouds have a net cooling
effect on the surface energy budget). These first-order characterizations of the ARM-observed VCRF are capturedby the reanalyses. However, the details of the VCRF profiles vary among observations and the four reanalyses. For
example, the negative summertime VCRF values approximate the ARM values in the ERA40 and NCEP reanalyses
for 100% cloudy skies, but the negative VCRF values do not persist at cloud fractions less than 100%, indicating thatthe models’ radiative flux and/or cloud optical depth parameterizations may need refinement, at least for an Arcticdomain. The same cannot be said for wintertime partly cloudy conditions in which some reanalyses (NCEP andNARR) respond with net radiative fluxes that are too high while JRA-25 and ERA40 simulate too little net surface
radiative flux for partly cloudy conditions.
CONCLUSION
The ARM/NSA Barrow site is characterized by large cloud fractions, especially in summer when there is
persistent low-level cloud cover that often obscures an otherwise clear sky. Cloud fraction climatologies illustratethat all of the reanalyses, with the exception of ERA40, have a difficult time capturing this cloud cover distribution
and undersimulate cloud fractions in summer. However, the radiative flux climatologies indicate that when thereanalysis models correctly simulate the clouds at Barrow, the radiative fluxes are generally well-simulated. When
clouds are undersimulated, positive biases in monthly surface downwelling shortwave flux range from +4 Wm-2 to+43 Wm-2 and the negative downwelling longwave flux biases range from -15 Wm-2 to -21 Wm-2. Ourintercomparison of 6-hourly time series of cloud fractions and radiative fluxes during June at Barrow for NCEP/ARM
and ERA40/ARM confirms that radiative flux biases in the reanalyses can be traced to the inability of the underlying
cloud models to accurately simulate the summertime cloudiness. Although not shown here, the impacts of these
6-hourly cloud fraction biases can range from +200 to +400 Wm-2 in the surface downwelling shortwave flux at
mid-day when insolation is greatest, and more than -50 Wm-2 in the surface downwelling longwave flux.
Intercomparison of the 6-hourly data also indicates that the ERA40, relative to the NCEP and other reanalyses,simulates more accurately the frequency distribution of summertime cloud fractions and downwelling radiative fluxes.
For the winter months, cloud fraction climatologies show that the NARR and the ERA40 oversimulate cloud
fractions at Barrow, while the JRA25 and NCEP reanalyses undersimulate cloud fractions. This is reflected inpositive longwave biases of +22 Wm-2 for the NARR and negative longwave biases of -9 Wm-2 to -18 Wm-2 for theJRA25 and NCEP, respectively. Cloud fraction biases in the ERA40 are not apparent in the longwave or theshortwave fluxes in either winter or summer.
While the results presented here are limited to the ARM-NSA site at Barrow, Arctic-wide (70-90°N) cloud
fraction climatologies were found to be similar to those at the Barrow location, indicating that the ARM observations
at Barrow are representative of the broader Arctic domain. For this broader domain, the results show that

when the cloud fraction is well simulated there are minimal biases in the radiative fluxes;
when summer cloud fractions are more than 50-100% less than observed, monthly mean net surface
shortwave flux biases can exceed +160 Wm-2;
when cloud fractions are undersimulated (oversimulated), the monthly mean net surface longwave flux arenegatively (positively) biased by 50 to 80 Wm-2 .
Cloud radiative forcing calculations made for the ARM data at Barrow show that clouds have a net warmingeffect throughout the year except for 2-3 months in the summer. This is consistent with the findings of Walsh andChapman (1998) based on measurements from Russian drifting stations during 1950-1990. Curry et al.’s (1993)
computational approach produced a slightly shorter (~weeks) period of positive cloud radiative forcing, although theirseasonality is consistent with that obtained here. This commonality is another indication that the ARM data at
Barrow captures at least the first order characteristics of the cloud-radiation feedback in the Arctic region. TheERA40 and NARR reanalyses correctly represent this seasonal cloud forcing, while the NCEP and JRA25 reanalysis
models misrepresent the cloud’s impacts as a net warming influence on the Arctic surface for the entire year.
Cloud radiative forcing displayed as a function of cloud fraction and season illustrate that major differences
between the ARM- and reanalyses-derived radiation fields were for partly cloudy conditions. In summer all of thereanalyses, to varying degrees, underestimate the net cooling effect of clouds under partly cloudy conditions. Inwinter, the NCEP and NARR reanalyses overestimate the net warming effect of clouds while the ERA40 and JRA25
underestimate it under partly cloudy conditions. Accordingly, partly cloudy conditions are at the core of twopriorities identified by this study: (1) the need to reduce the bias toward too-frequent occurrences of partly cloudy
conditions in the reanalyses, and (2) the need to simulate more realistically the radiative impacts of a partial cloudcover.
ACKNOWLEDGEMENTS
This work was supported by the U. S. Department of Energy’s ARM program through Grant
DESG-0206-ER64251 and by NOAA’s Cooperative Institute for Arctic Research through Grant NA17RJ1224.
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