Construction of the HighResMIP-ISST Ice and Sea Surface Temperature dataset
Malcolm Roberts, 26th June 2015
For HighResMIP Tier 1 experiments (1950-2014), we intend to use a ¼˚, daily SST
and sea-ice forcing dataset, based on the monthly 1˚ HadISST2 dataset (Rayner and
Kennedy, in prep). This essentially involves taking the monthly dataset and adding a
mask that gives the higher resolution, daily variability (based on more recent
observed climatology datasets).
We need this since many authors have found the influence of strong SST gradients
on the atmosphere is profound (e.g. Minobe et al 2008; Bryan et al. 2010; Chelton et
al. 2010 and many others).
This is straightforward, Nick and John have software to provide this historic dataset.
At the moment, I’m just using the monthly, 1 degree dataset to test below – the ¼
degree, daily can be added at the end.
For HighResMIP Tier 3 simulations (2014-2050), which are also required for
PRIMAVERA, we intend to extend this dataset forwards to 2050 to have a forcedatmosphere future simulation. This will be useful for a variety of studies, and enables
us to trace the continuous change into the future, rather than having a discontinuous
“timeslice” simulation.
It is probably worth taking this all the way to 2100 anyway – this could then be used
by any groups who can only do shorter runs – they can pick a period in the future
where the natural variability is repeated (see below) as their “timeslice” forcing – this
will then allow much more coordinated comparisons of simulations which are
continuous, and those which are time-slice discontinuous.
Going up to 2050, the RCP or strength of global warming is not such a big issue
(though there is a noticeable difference between RCP4.5 and RCP8.5 even here,
see IPCC AR5 WG1), beyond this is becomes even more important. Hence:
Question 1: should we consider which RCP our dataset is representative of, do we
want to calibrate the warming based on CMIP5 global mean changes?
The idea is to take a period in the past where the large-scale modes of variability are
similar to present day (e.g. 2010, the current end of the HadISST dataset), take that
section of the HadISST dataset (for example 1950-2010), and concatenate this on to
the end of the HadISST dataset, with a little adjustment of several years to get a
smooth continuous dataset.
HadISST: 1850------------------------------1950----------- 2010
Cut out a section
|------------------------|
Concatenate this section (twice) to the end of HadISST at 2010:
HighResMIP_ISST: 1850-------------1950----------2010|----------------|2070|----------|2130
We’d like this to use as simple a method as possible.
I did some work to look at how we might extrapolate the SST into the future, and
came across several problems (which should probably have been obvious at the
start):
Effort 1:
I wanted to remove the global trend between 1870-2010 from the HadISST dataset
at every point, so that the timeseries was reasonable flat. I could then take the 19502010 segment and:
a. At each x-y spatial point, add a 2 year timeseries to smoothly stitch together
the real 2010 data to the concatenated 1950 data. Since I have removed the
global trend, then this 2 year timeseries can be generated fairly simply at each
point by using the mean and stdev of the values at this x-y point.
You may be able to see the problem immediately – at points where the local trend in
the timeseries is completely different from the subtracted global trend, then you
introduce a different trend at the x-y point (particularly obvious near sea-ice, where
there are no trends). So this was probably naive.
Effort 2:
Rather than removing the global trend from each point, try removing the local trend
at each point but otherwise follow the method above.
This is not good because, when it comes time to extrapolate the trend into the future,
you need to do this at each spatial x-y point (since you subtracted the trend from
each point), and hence you are dependent on these trends being reasonable. Here I
think we test HadISST to destruction, because it tends to have strong warming
trends in the Southern Ocean over this period, and in general is not acceptable.
Effort 3:
So then consider not removing a trend at all.
A simple test of this is to take the global mean difference between 2010 and 1950,
and then concatenate the 1950-2010 chunk onto the end of 2010, adding this offset
so that the 2010-new 1950 boundary is relatively joined up. I can try a more
sophisticated joining later if we think this is a good method.
I then repeat, taking the (1950-2010) chunk and concatenating to the end of
(2010+(2010-1950))=2070, and hence having 3 repeats of the global variability in the
period 1950-2010.
Since the trend between 1950-2010 is rather small (and when you concatenate the
1950-2010 chunk onto the end of 2010, you are left with 2 lots of hiatus periods), I
have added a simple linear trend at each point at the moment, starting from 2010,
such that the SST increases by 1K over 60 years.
This is the dataset I’ve made available initially. There are clearly some issues to
address:
1. Adding the global mean offset of (2010-1950) to every point means there are
steps in the data, which are particularly obvious at points where the SST does
not change or have much variability, such as around sea-ice. I think I can deal
with this.
2. The problem with the linear trend is that the spatial structure of the warming
does not look like models, it essentially projects the past pattern. This means
that we don’t have:
a. Polar amplification
b. Enhanced warming in the tropics
c. Some areas may warm that are inconvenient, such as the Southern
Ocean where the trend is currently small, and probably will remain so
with ozone changes into the future
d. We probably lose the relatively cool region in the North Atlantic.
So here we are left with more questions:
1. Should we use some kind of spatial pattern to scale the future warming trend
– this would be relatively easy technically, but how do we choose a patter
a. do we warm like a Nino pattern, does this make the future SST too
complex, depending on too many choices?
b. Do we take the mean warming pattern from CMIP5 models for the next
century and use this – if so, has anyone already calculated this
pattern?
2. How strong should the warming be?
a. We need to ensure that changes in the radiative forcing (GHG,
aerosols etc) are consistent with our imposed SST warming.
3. We should remember that typical timeslice simulations are also quite simple:
a. tend to either add a spatially constant value to a past period of SST to
make the future SST, or add some spatial pattern from e.g. a coupled
simulation, and hence they are not sophisticated either.
At the moment I have not addressed how to project the sea-ice into the future. I think
that the methodology that KNMI have suggested should work fine:
The following was from Rein Haarsma:
Projecting the sea-ice into the future was based on the following procedure:
1. Using observed SST and sea ice concentration an empirical relationship is
constructed.
This is done by dividing the SST in bins of 0.1K. The SST of each data point
determines in which bin the sea ice concentration of each data point falls. After all
data points are handled in this way the mean sea-ice concentration for each bin is
computed. The relationship appeared to be different for the Arctic and Antarctic and
seasonal dependent.
2. Using this empirical relationship between SST and Sea-ice concentration the seaice concentrations for the constructed SST are computed.
Camiel Severijns still has the tables and the CDAT script for step 2.
I think that if we can get the SST to look reasonable then the sea-ice can follow.
As a test of my new dataset (1850-2130) constructed as above, I calculated AMO,
PDO, TPI (Henley et al 2015, A tripole index for the Interdecadal Pacific Oscillation),
and Nino3. They all look reasonable.
This dataset is available for download if you want to take a look – please let me
know and I can give you the details.
Fig 1: Global mean SST timeseries
Fig 2: HighResMIP_ISST SST difference between (2080-2100) – (1980-2005)
Fig 3: Timeseries of different global modes of variability in the HighResMIP-ISST
dataset
Fig 4: SST regressed onto PDO timeseries
Fig 5: AMO regressed onto SST