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EUMETSAT Satellite Application Facility on Climate Monitoring
Visiting Scientist Report
Evaluation of EC-Earth global water vapour field
simulations using CM SAF ATOVS operational
products and HOAPS climate data sets
CDOP VS Study No 11
Date
Reference
Author
15. March 2013
CLM_AS09_p04
Ulrika Willén
Objective
To evaluate the global water vapour distribution of the global climate model EC-EARTH
using CM-SAF time series of satellite data from the ATOVS sounder (13 years) and from
microwave imagers (HOAPS SSM/I datasets over ocean, 19 years).
Summary
We have compared monthly mean water vapour fields from the EUMETSAT Climate
Monitoring SAF project with simulated water vapour (IWV) from ERA-Interim and the
global climate model EC-Earth for the time period January 1979 to July 2011.
EC-Earth climatology pattern of water vapour is fairly well simulated compared to the
ERA-Interim and the CM-SAF IWV data. The satellite data sets agree well with ERAInterim as expected due to their inter-relations. However, there are some model biases
and problem regions also for the satellite data sets.
EC-Earth has a dry bias over all land masses, this bias is strongest for the summer
hemisphere when compared with re-analysis data. In July there is a wet bias over land
over Sahel and the Arabian peninsula. Over tropical ocean the model overestimate the
water vapour and cloud fraction. The cold and wet biases occur where the model is too
cold and warm respectively. The difference between EC-Earth model versions and ERAInterim is of the same order or somewhat larger than the difference between the ERAInterim and the satellite data.
ERA-interim and the satellite data are dependent since ERA-Interim data are used as
input to the satellite retrievals and conversely satellite data are used as input in the ERAInterim data assimilation. Still some significant differences were found for certain
regions. For these regions an evaluation with independent observations would be
beneficial
ATOVS overestimate IWV over tropical land regions especially in the summer
hemisphere compared to ERA-Interim. There is a very strong land sea contrast in this
bias which could be due to some problems with the retrieval assumptions over land.
SSM/I has more water vapour in the tropics than ERA-Interim, while poleward from
about 30° and especially in the summer hemisphere there is less IWV over mid-latitudes.
Further poleward at 50°-60° the bias turns positive again. The last difference can be
understood from problems detecting IWV over and near sea-ice edge. The tropical and
mid-latitude difference between SSM/I and ERA-Interim need further investigations.
1 Introduction
The aim of this study has been to evaluate the usefulness of the Satellite Application
Facility on Climate Monitoring (CM-SAF) datasets for climate modelling development
and validation. For model evaluation and improvement we need long-term homogeneous
and consistent data sets, with large spatial coverage as is being collected by the CM-SAF.
The interaction of water vapour, clouds and radiation are crucial for the climate and its
variability. Collocated measurements of these variables can help to reveal systematic
biases in global and regional climate models simulations for present day and increase the
confidence in climate predictions. We can compare the model uncertainty with the
observations and the observational uncertainties, by performing sensitivity studies with
the models and change details in the parametrisations.
A major research area in climate science is the Arctic region where the recent warming
has been much larger than the global average especially in autumn and winter and
substantial reductions in summer sea-ice has occurred. Current climate models have a
large spread in this Arctic amplification for future scenarios. Antarctic sea-ice has
decreased over certain regions and increase over others, an understanding of the different
trends over the poles is still lacking. Therefore observations from different sources can
help to identify model short comings and help in development.
In this report we use the CM-SAF SSM/I and ATOVS water vapour data for evaluation
of the EC-Earth model for the whole globe with special emphasis on the Arctic and
Antarctic. We also compare the satellite mean and variability of the water vapour data
with ERA-Interim data. The results are in accordance with the outcome from two studies
on the Arctic and Antarctic sea-ice variability and its connection to mid-latitude extreme
weather using ERA-Interim data and the EC-Earth model. These results will be submitted
in spring 2013.
2 Model
The EC-Earth global climate model (Hazeleger et al 2010) has been been developed from
the European Centre for Medium-Range Weather Forecasts (ECWMF) model
(http://ecearth.knmi.nl/). The atmospheric component is based on ECMWF's Integrated
Forecasting System (IFS), cycle 31r1 with some additional implementation, including a
new convection scheme and the new land surface scheme H-TESSEL. Small changes in
the gravity wave drag and shortwave radiation parametrisations have also been applied to
reproduce the observed climatology of the past 40 years as closely as possible and to
achieve a balanced radiation budget. The ocean component is based on version 2 of the
NEMO model, with a horizontal resolution of nominally 1 degree and 42 vertical levels.
The sea ice model is the LIM2 model. The ocean/ice model is coupled to the
atmosphere/land model through the OASIS 3 coupler.
In this study we have made atmosphere only AMIP type simulations (Gates et. al. 1998),
i.e. we have run the EC-Earth atmospheric part with prescribed observed sea-surface
temperatures and prescribed sea ice for the time period 1979-2008 in order to be able to
compare with observations and determine systematic model climate errors. The model
was run at T159 (125km) horizontal spectral resolution with 62 vertical levels.
We have run EC-Earth standard version and made a sensitivity experiment where we
changed the mixed-phase cloud parametrisation. A number of studies have shown that
models underestimate the amount of cloud water in Arctic and Antarctic mixed-phase
clouds (Beesley et al 2000 and Klein et. al. 2009). The ECMWF model was found to
have a large underestimate of LWP in a model inter-comparison study of a single layer
ARM mixed-phase cloud by Klein et. al. (2009). We have therefore run EC-Earth for the
standard mixed-phase formulation with mixed-phase clouds assumed to exist between 0
and -20°C (EC1) and one version where liquid phase is allowed down to -40°C (EC2) as
has been found to occur for observations (Hu et al 2010). Only results linking to water
vapour changes will be shown here.
3 Satellite data
We have used the CM-SAF monthly time series of satellite data from the ATOVS
sounder (1999- 2011, 13 years) and from the microwave imagers HOAPS SSM/I datasets
over ocean (1988-2006, 19 years, Latitude/longitude grid 0.5x0.5 degree). The satellite
data was obtained from the EUMETSAT Climate monitoring Satellite Applications
Facility database (CM-SAF http://www.cmsaf.eu) Monthly mean values of vertically
integrated water vapour (IWV) were interpolated to the EC-Earth grid of 125 km
horizontal resolution to enable the comparison. We also use ERA-Interim reanalysis data
(Dee et al 2011) in the comparisons.
4 Results
We have compared the vertically integrated water vapour from the models with the
ATOVS and HOPAS SSM/I HTW products. We present results for January and July to
show the seasonal variations in the global water vapour distribution. The monthly mean
and standard deviation (STD) climatologies for ERA-Interim for 1979-2008, EC-Earth
AMIP runs (1979-2008) and the two satellite datasets, ATOVS 1999-2011 and SSMI
1988-2008 will be presented. In the bias plots we compare EC-Earth and the satellite data
sets for the same time periods to avoid sampling issues due to the natural variability.
4.1 Global comparison of water vapour
The monthly mean of water vapour for January are shown in Figure 1 and as zonal mean
values in Figure 7. The mean patterns are very similar for all data sets with maximum
IWV values of 55 kg/m2 in the zonal mean peaking at the ITCZ in the southern
hemisphere down to the very small values at the poles of less than 2kg/m2 (Figure 7).
Computing the standard deviations of the monthly fields show the high variability of
ERA-Interim in the tropics related to ENSO and regions of convergence similar what was
found for ERA-40 and SSM/I by Trenberth et al. (2005). The standard deviation for
ERA-Interim is shown in Figure 2, the zonal mean maximum value is about 6kg/m 2 (Fig.
7). The variability is larger the larger the mean values of IWV similar to what was found
by Trenberth et al (2005) for ERA40 and SSM/I.
Despite the overall agreement seen in Figure 1 the differences plots in Figure 2 and 3
reveal regions where the satellites and model deviate from ERA-Interim. Water vapour is
underestimated in EC-Earth compared to ERA-Interim over most land masses by more
than 5kg/m2, except over Sahel and west Asia in boreal summer as shown in Figure 2 and
5. The largest dry biases occur in the summer hemispheres where too much SW is
absorbed which leads to too high temperatures. The wet bias over Sahel and Asia in
boreal summer and over equatorial ocean regions corresponds to regions with too much
cloud fraction and liquid water path (not shown) and too little absorbed SW and therefore
cold biases (not shown).
Over the Atlantic EC-Earth underestimates IWV by 1-2kg/m2 but it is closer to the SSM/I
values. Allan et. al. (2004) found that ERA40 overestimate moisture by up to 1-3kg/m2
compared to satellite (SSM/I and SMMR) measurements over mid-latitude oceans. We
also find ERA-Interim has more water vapour than SSM/I data over mid-latitude oceans
i.e. in this aspect the EC-Earth AMIP runs are closer to the the SSM/I data than the
reanalysis data. However, the ATOVS data has close to and larger IWV values than
ERA-Interim over the oceans. To understand the discrepancy between the two satellite
datasets would help the model evaluation. Since the satellite retrievals use ERA-Interim
as input and ERA-Interim uses satellite data one would expect the data sets to stay close
which they do for many regions where the difference is only about 1kg/m2. However the
difference is much larger for some regions that will be discussed below.
SSM/I has more IWV in the tropics than ERA-Interim but it changes abruptly to a
negative bias at about 30° away from the equator as one moves towards the poles, in each
summer hemisphere, most strongly noted for boreal summer (Figure 5 July).
The SSMI standard deviation zonal distribution is much more peaked than for ATOVS
and ERA-Interim and EC-Earth (Figures 7 and 8). Towards the poles the SSM/I values
are less reliable due to the occurrence of sea-ice, for the winter hemisphere the bias gets
larger at 50° while in the summer hemisphere the values are more reasonable up to 60°.
For ATOVS the largest IWV difference compared to ERA-Interim occur over the
continents with the largest deviations in the summer hemispheres, i.e. where the largest
IWV values are. This appears to be some satellite problem since the land-sea contrast is
very strong. It could possibly be due to differences in assumed surface emissivity or if
different assumptions are made in the retrievals in the weighting of the ERA-Interim data
at different vertical levels. According to ATOVS manual, larger errors can be expected in
the near surface values. Since most of the IWV is contained in the lower layers of the
troposphere they dominate the integrated amount and the absolute error will be larger for
regions with more IWV.
To investigate the effect of the sampling we also compared ATOVS and SSM/I with the
mean values of ERA-Interim for the whole period 1979-2008. Those biases are shown in
the lowest rows in Figures 2, 3, 5 and 6. The errors are larger which show the importance
of the climate decadal variability. The mean IWV differences are smaller than the
differences in STD as also can be seen comparing the ATOVS and SSM/I full and dashed
lines in Figure 7 and 8 (dashed lines are the values for the bias calculated for different
averaging time periods).
4.2 Northern hemisphere and polar region water vapour
The recent large warming over the Arctic and the Arctic sea-ice record melting surpasses
most IPCC model predictions. The reasons for this rapid change are not fully understood
and a lot of modelling and observational campaigns are concentrating on this region.
ATOVS data due to its high sampling over the poles can be very useful for this purpose.
To investigate the Arctic amplification we performed sensitivity experiments with ECEarth increasing the liquid amount in mixed phase clouds since recent observations shoe
how cloud droplets exit down to -40°C, with more liquid in the clouds there is more LW
re-emitted to the surface and thereby more warming.
The mean patterns of ATOVS and ERA-Interim water vapour agree well over the Arctic
(Figure 10 and 11). We do not know the extent of ERA-Interim model influence on the
ATOVS data and vice versa. ATOVS has higher values over the ocean and lower over
the continent than ERA-Interim.
EC-Earth deviates from ERA-Interim and CM-SAF data over the North pole. There is
less IWV over the Arctic ocean and somewhat too much over Northern America and
Siberia. This later bias is due to a too warm surface in EC-Earth (3rd row Figure 11). In
boreal winter EC-Earth is up to 4K too warm over northern Canada and Siberia (Wilco et
al 2011). This bias over continental regions might be associated with a too simplified
treatment of seasonal snow (Dutra et al. 2011), and also with the BL mixing
parametrisation. The warm northern hemisphere biases are much larger for the test
version of EC-Earth where the increased LWP give rise to more LW emitted to the
surface (not shown). Over Greenland there is an underestimation of IWV for the control
run which is reduced for the sensitivity run.
The IWV of the test version of EC-Earth is closer to the ERA-Interim and ATOVS IWV
data, the bias changed from too dry to to humid in some areas. Despite the biases for the
two EC-Earth AMIP runs the trends over the last 30 years are of similar magnitude and
the regional distributions are similar so we can have some trust also in EC-Earth
predictions for the future (not shown).
For July ATOVS has more IWV than ERA-Interim over the continents while EC-Earth
has a large negative bias. This bias is somewhat reduced for the sensitivity run albeit the
general bias pattern remains. Looking at the temperature bias there is small negative bias
turning positive further south as the SW increase.
5 Conclusions and feedback
The main features of the humidity and cloud features in EC-Earth are well simulated
compared to ERA-Interim and the CM-SAF data on a global scale.
EC-Earth has a large dry bias over the continents compared to Era-Interim and ATOVS
IWV data. Over the tropical ocean EC-Earth overestimates IWV compared to ERIM but
also the satellite data sets have more IWV there. Over the Atlantic EC-Earth and SSMI/I
has less IWV than ERA-Interim. The sensitivity run with EC-Earth show that EC-Earth
underestimate LWP over the Arctic and Antarctica but not in other regions. For the
winter hemispheres EC-Earth overestimate the temperature over Canada and Northern
Siberia, the bias is increased for the sensitivity run with more LWP and warming, but is
largely due to errors in the surface parametrisation. The new satellite datasets from CMSAF helped us to investigate these problem regions further.
The two satellites data sets agree fairly well with ERA-Interim as expected. However
some regional differences need further investigation. ATOVS has much more IWV over
tropical continents, the land/sea difference appears to be exaggerated. SSM/I and ERAInterim has a change in sign of bias around 30° from the equator especially strong for
July.
The inter-dependence of the of the satellite products and ERA-Interim could be further
investigated using another model or different weighting functions as well as perform
evaluation with independent observations of IWV from e.g. radiosondes and GPS.
References
Allan, R. P., M. A. Ringer, J. A. Pamment and A. Slingo 2004. Simulation of the Earth's radiation budget
by the European Centre for Medium Range Weather Forecasts 40-year Reanalysis
(ERA40) J. Geophys. Res., 109, D18107, doi:10.1029/2004JD004816.
Beesley, J. A., C. S. Bretherton, C. Jakob, E. L. Andreas, J. M. Intrieri, and T. A. Uttal (2000), A
comparison of cloud and boundary layer variables in the ECMWF forecast model with
observations at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp, J. Geophys.
Res., 105(D10), 12,337–12,349.
Dee D et al 2011: The ERA-interim reanalysis: configuration and performance of the data assimilation
system. ECMWF ERA Report Series 9, pp 1–71 (available online:
http://www.ecmwf.int/publications/library/do/references/list/782009)
Gates, W. L., J. Boyle, C. Covey, C. Dease, C. Doutriaux, R. Drach, M. Fiorino, P. Gleckler, J. Hnilo, S.
Marlais, T. Phillips, G. Potter, B. Santer, K. Sperber, K. Taylor and D. Williams, 1998:
An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I ),
Bulletin of the American Meterological Society, 73, 1962-1970.
Hu, Y., S. Rodier, K.-M. Xu, W. Sun, J. Huang, B. Lin, P. Zhai, and D. Josset (2010), Occurrence, liquid
water content, and fraction of super-cooled water clouds from combined
CALIOP/IIR/MODIS mea
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T., Sterl, A., Tapamo, H., Vancoppenolle, M., Viterbo, P., and Willén, U., 2010: EC-Earth:
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Klein, S.A., R.B. McCoy, H. Morrison, A.S. Ackerman, A. Avramov, G. de de Boer, M. Chen, J.N.S. Cole,
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Figures
Figure 1. January mean IWV (kg/m2) for ERA-Interim (1979-2008), EC-Earth AMIP run
(1979-2008), ATOVS (1999-2011) and HOAPS SSM/I (1988-2006).
Figure 2. January mean IWV (kg/m2) for ERA-Interim and the differences of the ECEarth, ATOVS and HOAPS SSM/I compared to ERA-Interim (kg/m2) for same
respective time periods as in Fig1. The third row show the differences of ATOVS and
SSMI compared to ERA-Interim for the period 1979-2008 .
Figure 3. January standard deviation of IWV for ERA-Interim and the differences in STD
between EC-Earth, ATOVS and SSM/I compared to ERA-Interim (kg/m2).
Figure 4. Same as Figure 1 for July.
Figure 5. Same as Figure 2 for July.
Figure 6. Same as Figure 3 for July.
Figure 7. January zonal mean and standard deviation of IWV (kg/m2) for ERA-Interim,
EC-Earth (EC1:control run, EC2 sensitivity run), ATOVS and SSMI data. The
differences in mean and STD of IWV are calculated compared to ERA-Interim for the
respective time period of each dataset. Left column is for all grid points and right column
for grid points over sea only. The dashed lines for ATOVS and SSM/I are the differences
in mean and STD comparing with the entire period 1979-2008.
Figure 8. Same as in Figure 7 but for July.
Figure 9. Northern hemisphere January mean of IWV (kg/m2) for ERA-Interim, ECEarth (EC1 and EC2) and for ATOVS. The 3rd row show the mean T2M temperature of
the two EC-Earth runs.
Figure 10. Northern hemisphere January mean of IWV (kg/m2) for ERA-Interim and the
differences between EC-Earth (EC1 and EC2) and ATOVS compared to ERA-Interim.
The 3rd row show the T2M temperature bias of the two EC-Earth runs compared to ERAinterim.
Figure 11. Same as Figure 9 for July.
Figure 12. Same as Figure 10 for July.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12