East Australian Current in TV documentary

German TV company ZDF will screen a documentary on Sunday, 21st of June, 7:30 pm Berlin time, which features an nice animation of the East Australian Current done by Guy Griffiths using the SST CCI analysis data. 

Here is the trailer (although the animation doesn't feature in it).

UPDATE: Here is a clip showing the (brief) appearance of the data, in the context of a discussion about coral's sensitivity to water temperatures. The colours represent the SST relative to the annual average at a given latitude, and the warming off Australia is seasonal.

No slowdown in global temperature rise?

There has been much recently published about why, compared to the 1980s and 1990s, there has been a slowdown (or even a "pause" or "hiatus") in global warming. Burial of heat below the surface of the ocean has been fruitfully investigated. At the same time, commentary has pointed out

• that the perception of a hiatus in part results from considering a time interval that starts with a year that was (back then) unusually warm (1998)
• decade-to-decade variations in short-term (~10 year) climate warming rates are to be expected

Now Karl et al. in Science have declared the non-existence of a slowdown, after reassessing observational data.

They are right to make scrutiny of the observations part of the search to understand any (apparent) hiatus. Global warming is important for us in the long run, but is a subtle change to estimate over a single decade (~0.1°C global change). Part of the challenge is the variability of nature year to year, region to region. The other aspect is ensuring consistency across the changing mix of measurements that go into constructing a long-term dataset.

The major component of their re-appraisal is a revised estimate of the global sea surface temperature (SST) trend upwards to about 0.08°C between 1998 and 2012 (from about 0.01°C in IPCC). This revision comes from changing the assumptions about the relative biases between and the relative weight it is appropriate to give to drifting buoys and ships. This revision amounts to ¹/₂₀₀^th °C/year.

When talking about a revision of temperatures that is so subtle, it is prudent to be cautious. Karl et al. recognise this in the title of their paper "Possible artifacts of data biases in the recent global surface warming hiatus" (my emphasis).

How can we gain more confidence about the recent global sea surface temperature changes? SST CCI's contribution is to develop a high stability, independent dataset of SST. We attempt this using satellite-derived SSTs, based on physics (not tuning to drifting buoys or other in situ observations).  If we see compatible behaviour in independent datasets, that behaviour is unlikely to be driven by data artefacts.

Compared to an in situ data set, we in SST CCI have fewer instruments whose long-term calibration we need to worry about. On the other hand, the satellite measurement, being from space, is indirect, through the atmosphere; we go to considerable lengths to remove any residual atmospheric artefact in the SST time series.

Looking at our ATSR-based obs4MIPS dataset, the global mean SST trend (not including sea ice areas) over 1998 to 2012 ^[1] is 0.085°C, which is 0.06°C/decade ^[2].

Karl et al.'s "new" value of SST change over this interval therefore fits pretty well with our independent ^[3] satellite data. These data featured in IPCC AR5. (They also agree well with the Hadley Centre in situ ensemble in the same figure.)

So, how new Karl et al.'s result is depends on what data you previously paid attention to.

And I would still describe 0.06°C/decade as a slowdown compared to the 1980s and 1990s.

In SST CCI we also have higher resolution data from blending in more types of satellite data, which, over 1998 to 2010 ^[4], has the same global mean SST trend of 0.06°C/decade. Patterns of warming tend to be more interesting that global mean numbers, and are related to the dynamics of heat uptake by the ocean. We need to understand how energy gets distributed around the climate system in order to understand decade-to-decade variations in global warming rates.

Notes:

1. The last ATSR failed in March 2012, so the period is not quite the full 1998 to 2012 interval.

2. The best uncertainty estimate that we can provide is that we have 95% confidence that the trend is between 0.04 to 0.085°C/decade. This is the trend uncertainty from the stability of the observations. We can only be confident that this trend uncertainty is valid in tropical regions, because the excellent global tropical moored buoy array is the only in situ reference we can compare with over that period where the thermometers are accurately pre- and post- calibrated. The fall-behind in maintenance of the array was very unhelpful, as it undermines this sort of long-term stability assessment for the whole satellite SST constellation, which has implications for knowledge of climate globally.

3. I think it is important that in situ and satellite observationalists try to preserve this independence by not letting too much understanding of each other's biases leak between the in-situ-only versus satellite-only datasets. But we also need to work together on getting the best possible reconstructions of SST using blends of in situ and satellite SST. Tricky when we are the same people!

4. The ongoing phase of SST CCI aims to extend this SST analysis to cover 1981 to 2016, over the next 2 years. But for now, it ends with 2010.

5. Thanks to Owen Embury and Simone Morak for help in putting this post together.

New method gives less noisy view of ocean temperatures

New sea surface temperature (SST) products were generated experimentally by SST CCI in March 2015. These included full resolution data from the Advanced Along Track Scanning Radiometers using a new retrieval method that reduces the noisiness of full resolution SSTs. Although ATSRs are generally relatively low noise sensors, the process of low-bias SST determination amplifies the sensor noise and creates images that look noisy. Noise filtering of the SST could address this cosmetically, but this would degrade the feature resolution and real data quality, and is not satisfactory. Instead, a new theoretical development by SST CCI has been tested: namely, multi-channel smoothing of the atmospheric influence on observed AATSR brightness temperatures. The conditions of the atmosphere vary more slowly with horizontal distance than the SST. The process of SST retrieval can be viewed as estimating the atmospheric influences on brightness temperatures and removing them to reveal the SST that underlies the atmosphere. The new method estimates atmospheric influences across a wider area around a given image pixel, but treats the SST estimation at full resolution. Compared to older methods (left panel of figure), the new SSTs are less noisy (middle panel) — NOT because the SST has been smoothed (it hasn’t), but because the new retrieval method amplifies noise less and gives a more faithful rendering of the true SST variation. 

Left: full resolution (1 km) AATSR SST image from previous project. Clouds over the ocean are rendered on a grey scale, and the green feature is land. The rendering of temperature is such that the coolest temperatures are pale blue and warmer temperatures are deep blue. Middle: new result, using new technique to reduce noise amplification in the process of retrieval. Right: noise removed.

The noise removed by the new method compared to old is shown in the right panel. The noise should look completely random, but in fact there are some streaks where there is a tendency to blue or red, indicating non-random effects. This appears to be a result of imperfect co-registration of the forward and nadir views in the older imagery (we have improved the offset correction between the views in the latest experimental products as well).

Ensembles of satellite SST

Last week was the previously advertised user consultation meeting on uncertainty information in the SST climate data record (with some sharing of experience with other variables). It was a lively meeting, and my thanks to all participants.

There will be a meeting report, which is in preparation, and outcomes will be assimilated into an update of the SST CCI user requirements document.

An interesting outcome was the appetite among some users for ensembles of sea surface temperature (SST) datasets. This might seem surprising, since satellite-derived datasets are already large, and the prospect of a meaningful ensemble (100 to 1000 realisations was mentioned) would seem to be an obstacle. However, a number of counter-balancing points were raised:

• creating an ensemble can be the only practical way to represent some correlated error structures (particularly errors that evolve on the long term through a multi-decadal dataset, such as those associated with imperfect harmonisation across series of sensors)
• other forms of more detailed uncertainty information, including error covariance matrices, can also represent and large additional data volume
• once a user has an analysis or model run set up for a particular experiment using an SST dataset, it can be much easier for them to explore uncertainty by re-running the same analysis for variants of the SST dataset, than to analyse the dataset uncertainty information and assess its impact (which involves more thinking and work!
• the ensemble generation can involve runs that explore structural uncertainty (effects of data processing choices where there is some element of judgement about parameters, etc) -- and there is no other way to obtain the resulting effects (these choices/parameters generally can't be analytically propagated, and sensitivity to them has to be found by tweaking the choices and re-running)

We can't feasibly produce a full ensemble version of our climate data records within SST CCI phase 2, but we will stay alert to opportunities to progress towards that capability as we are doing the project.

OSTIA water/land mask and new inland water bodies dataset

Laura Carrea and I have been looking at the OSTIA water/land mask in comparison with the new higher resolution dataset from the Landcover CCI project. The OSTIA mask is at 0.05 deg lat-lon resolution, and is used for the SST CCI Analysis product (gap filled daily SSTs); its precise heritage (since being created several years ago) hasn't been re-traceable, so it is useful to check its nature.

The plot below shows for a small area (Baltic Sea) the prevalence of water (according to the LC CCI product) within each 0.05 deg cell which is labelled in OSTIA as land. Red colours indicate the presence of a small fraction of water in an OSTIA "land" cell, and rivers and the many lakes on land are obvious. Dark blues indicate total or near-total water in an OSTIA "land" cell. Some of these are inland lakes no resolved in OSTIA, which is no surprise. There are a few cells around the Baltic coasts that are apparently water-filled (dark blue) yet are "land" cells in the OSTIA mask. Globally, however, such cases are exceptions. In general the coasts don't have a fringe, which suggests that the OSTIA mask is designed such that a cell tends to be labelled "water" if there is any significant fraction of sea within the cell. (If the design were such that a cell was labelled "water" if it contains >50% of sea, then there would be a fringe of intermediate values of %water-in-land-cell all coastlines.) So OSTIA must use a "fat" water mask, rather than a mask that is neutral with respect to land and water.

This next plot shows the prevalence of land in cells labelled water in OSTIA. Here, blue colours indicate a small amount of land, and red a lot of land in the cell. The fact that the whole coastline tends to be fringed with colour confirms that the OSTIA mask is "fat" with respect to water. (That is, truly mixed cells tend to be labelled as sea, so there is a fringe of land-in-water-cell cases along coasts.) However, it is also clear that there are many "water" cells that are in LC CCI completely land (dark red in this picture). These appear preferentially on northern coasts. This is consistent globally, and not just around the UK. This suggests that there is an offset in the N-S direction, roughly half an OSTIA cell in size, in the OSTIA mask relative to the Landcover CCI data set.

Where an OSTIA cell is flagged as sea and is in fact filled with land, there will be (or should be) no satellite SST retrievals ever available for that cell -- the data will always be provided by the gap-filling procedures associated with creating the L4 SST CCI analysis.

How to represent different SSTs in the products

As previously discussed we aim in future products to include not only the skin SST (the primary geophysical retrieval) and, as before, a 20 cm estimate at a fixed local time (to minimise aliasing of the diurnal cycle in the long term trends), but also a UTC-day mean estimate. This combination hits a good fraction of the diverse range of user requirements we collected for depths and time.

We also need to provide an adjustment to the most consistent possible retrieval (default in product is the best available type of retrieval, but one might also want to analyse one type of retrieval through the whole record).

To deliver this information in a GHRSST-compatible form requires some thought, since the retrieval and the various model-derived adjustments all have up to three components of uncertainty as well as their values.

Project team discussions have concluded on the following: to store the best available skin SST as the primary variable, and give a set of adjustments that can be added to this, each with N (1 <= N <= 3) uncertainty components. This is a much smaller data volume than adding all the different SSTs each with three uncertainty components. Data volume is a concern for a significant set of users.

For the convenience of users faced with the complexity of adding adjustments to the primary data, we will need to provide a reader programme that configures the calculation of a desired SST type and its uncertainty. Even nicer would be configuration of the desired fields on the fly on download -- that is a technological solution we aim to discuss with those who will do the CCI programme data portal (invitation to tender currently published).

Uncertainty information in Climate Data Records

We are well into our planning for a User Consultation workshop on representing uncertainty information in our SST CCI products in the most useful way. (Registration is still open.)

Venue: Met Office Hadley Centre, Exeter, UK
Date: 18-20th November 2014.

This workshop will be a two-way discussion between data providers and users. We aim to create a common understanding of: where uncertainty comes from (in this case, uncertainty in satellite sea surface temperature); how to talk about uncertainty unambiguously; how well the uncertainty information that is provided addresses users´ needs; and how to practically use such uncertainty information. It will achieve this through a mixture of oral and poster presentations, activities and group discussions.

We as data producers need to provide uncertainty information that users have confidence in, and have confidence in using. That is, they need to know it is realistic information, and what they can validly do with the information. Achieving this definitely involves increased mutual understanding, so it should be a very stimulating and lively meeting.