ACSYS Polar Products from Reanalysis

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ACSYS Polar Products from Reanalysis Polar
Products from Reanalysis (PPOR) Working Group
Mark C. Serreze Cooperative Institute for
Research in Environmental Sciences University of
Colorado Boulder CO
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What is an Atmospheric
Reanalysis? Reanalysis is a form of numerical
weather prediction (NWP). Reanalysis provides
long time series of analysed atmospheric fields
(e.g., tropospheric pressure heights,
temperatures, winds, humidity) and forecasted
surface variables (e.g., precipitation,
evaporation, radiation) using a frozen data
assimilation/forecast system. Archives from
operational NWP systems used for routine
forecasting contain temporal inhomogeneities due
to frequent model changes. Using frozen model
results in better temporal consistency, with the
caveat that the assimilation data base (e.g.,
rawinsondes, satellite retrievals) changes with
time. Reanalysis efforts include
NCEP/NCAR (NCEP-1) 1948-present NCEP-DO
E AMIP-II (NCEP-2) 1979-present ERA-15
1979-1993 ERA-40 1957-2002 (recently
completed) JMA-25 NASA DAO NCEP NARR
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Reanalysis in the Arctic Why
the Interest? We clearly want reliable fields of
analyzed atmospheric variables (e.g., 500 hPa
heights, SLP) for climate studies. But some of
the strongest interest by the Arctic community
regards forecasted surface variables.
Observed time series of even basic variables
such as precipitation and temperature tend to
be of relatively short duration with
insufficient spatial coverage. Direct
measurements of fluxes of latent heat, sensible
heat and radiation are even more scanty.
Reanalysis provides an alternative data source.
Even with biases, surface fields from
reanalysis have and will continue to find wide
use in climate studies. With removal of biases
and blending with avialable observations,
surface fields from reanalysis can be used be
used to drive other models (e.g., stand alone
LSMs, sea ice and coupled ice/ocean models)
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The ACSYS Working Group on Polar Products from
Reanalysis (PPOR) was established in June 1997.
It was tasked with the following 1) Promote
the evaluation and assessment of reanalysis
products for polar regions, working with the
reanalysis centers as appropriate. 2) Promote
the development and implementation of
techniques for blending modeled and in-situ data
sources to provide optimal gridded products. 3)
Give particular attention to precipitation
fields, with the aim of providing data sets at
spatial and temporal resolutions suitable for
input to hydrologic models. 4) Maintain
awareness of the work of reanalysis centers,
liasing with them as appropriate on the
representation of polar processes in future
reanalysis efforts.
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There are many examples of the use of surface
variables from reanalysis in Arctic climate
studies Relationships between Eurasian snow
cover variability and 2-m temperature fields.
Use of reanalysis data to drive sea ice and
coupled ice-ocean models. Assessments of
large-large-scale variability in Arctic
precipitation. Assessments of Arctic airmass
formation. But the general conclusion from
evaluations has been somewhat sobering There is
useful skill in reanalysis depictions of many
surface variables, such as precipitation and
temperature. But there are often large biases,
and sometimes, gross misrepresentations.
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Some Results from Past Evaluations of Surface
Variables NCEP-1 monthly averaged fluxes of
downwelling solar radiation in the Arctic are
much too high (up to 80 Watts per square meter
in June), apparently due to problems in modeled
cloud cover. Summer precipitation rates in
NCEP-1 are much too high over Arctic land areas,
mostly from excessive convective precipitation
and evaporation. The extent to which these
problems are conflated with the excessive
shortwave radiation, the convective scheme and
updating soil moisture by the forecasted
precipitation is unclear. NCEP-2 suffers from
the same summer precipitation biases. NCEP-1
also shows a blotchy pattern of precipitation
associated with orography, apparently due to a
problem in horizontal moisture diffusion. The
problem has been addressed in NCEP-2.
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Surface Evaluations
Continued Overall, ERA-15 seems to perform
better than NCEP-1, and the extent to which it
has been examined, NCEP-2. But ERA-15 has a
cold bias in 2-m temperatures over boreal
forest Viterbo and Betts, 1999 Walsh and
Chapman 1998 looked at radiation/surface tempera
ture/cloud cover associations over the Arctic
Ocean NCEP-1 slightly exaggerates observed
associations observed associations between
cloudiness and surface air temperature
while ERA-15 shows a much weaker association.
Oddly, surface radiation fluxes in ERA-15 seem
to be insensitive to the presence of clouds.
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Aerological P-E A Success Story Aerological
estimates of precipitation minus evaporation
(P-E, computed from wind and moisture profiles)
from both NCEP-1 and ERA-15 look pretty good
Cullather et al., 2000 Rogers et al., 2001.
This is a strong point of reanalysis. The plots
at right show mean January and July P-E (mm)
from NCEP-1.
However, P-E computed from the aerological
method and from the surface forecasts of P-E are
not in hydrologic balance. Both models have lower
P-E in the forecasts (but with both P and E being
much too high in NCEP-1).
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The New ERA-40 Model Much of the effort by the
working group focused on ERA-40 development.
Fruitful collaborations with Pedro Viterbo
(ECMWF) and Nick Raynor (Hadley Center) 1)
Use of improved sea ice boundary conditions that
include realistic sea ice concentrations. ERA-15
essentially used a slab depiction. 2) Use of a
multi-layer formulation to improve depiction of
2-m air temperature over sea ice. 3) Improved
treatments of the land surface (albedo, frozen
soil hydrology) Over half of improved surface
treatments in ERA-40 were driven by high-latitude
concerns!
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Biases in ERA-40 2-m Temperatures (Model minus
Observed) Biases in 2-m daily temperature with
respect to the UW POLES data set are overall
smaller than in ERA-15. There are still some
problems over open ocean areas, where ERA-40
seems warm, and over central Greenland, where it
appears cold. But evaluation is difficult
over these regions due to sparse observations.
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ERA-40 2-m Temperatures Temporal Correlations
Temporal correlations between observed (POLES)
and ERA-40 daily temperatures look pretty good
(largely above 0.8). Correlations are low over
the melting sea ice cover in July. This is not
surprising as the observed temperature has
little temporal variability in this month.
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Precipitation A Key Variable Understanding the
hydrology of the Arctic regions and monitoring
emerging changes requires (among other things)
gridded fields of precipitation of sufficient
quality for climate analysis and for driving
hydrologic and land surface models.While the
station data base has always been sparse, it has
seriously degraded since about 1990. For
example, station coverage over Siberia is is now
about half of what it was in the 1980s. The
network is now insufficient to compile gridded
fields. Blending precipitation forecasts from
reanalysis with available gauge data may be the
best approach.
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ERA-40 Mean Precipitation The spatial patterns
of mean monthly precipitation from ERA-40 are in
accord with available observations.
Precipitation is greatest in the north
Atlantic and Pacific, best expressed in winter.
Land areas and the Arctic Ocean exhibit winter
minima and summer maxima.Winter month totals in
parts of Siberia and northern Canada are less
than 15 mm.
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Mean Annual Cycles for the Major Eurasian
Watersheds Observed, NCEP-1, ERA-15 and
ERA-40 Observations show July maxima and cold
season minima. This basic pattern is reproduced
by each model, but with warm-season precipitation
too high in NCEP-1. ERA-40 and ERA-15 look quite
good, and closely track each other.
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Squared Correlations, Observed vs. ERA-40
Precipitation There are large areas,
especially over Eurasia, where ERA-40 performs
quite well (squared correlations exceed 0.50).
But for many areas, perfomance appears to be
poor. But in data-sparse areas, the observed
gridded time series are of poor quality.
ERA-40 may be perfoming better than is indicated.
Performance is much better than NCEP-1, but no
better than ERA-15. Basic conclusion ERA-40
fields are good enough to be blended with gauge
observations.
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Squared Correlations Observed vs GPCP The
better performance of ERA-40 (and
ERA-15) relative to the GPCP satellite product is
abundantly clear. Satellite retrievals have a
hard time dealing with the heterogeneous
emissivity of land surfaces, and work best
over open ocean regions. Over land, reanalysis is
the way to go. The satellite retrievals are
considered to be improved after 1987 (the SSM/I
era).
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ASR Development Efforts over the next few years
will focus on the planned Arctic System
Reanalysis (ASR), to be based on the NCEP
WRF system. Will (hopefully!) be a flagship
of the SEARCH program. NOAA has provided seed
funding for ASR development. A recent meeting in
Fairbanks, AK, focused on ASR development.
There are many issues to be resolved What data
are out there (e.g., Russian rawinsondes) that
can be recovered to improve the assimilation data
base? Assimililation of MODIS winds (TERRA,
AQUA). TOVS retrievals over the cloudy Arctic
Ocean. Better representation of sea
ice. Better cloud microphysics