SST CCI data used to assess long-term records

A paper by Hausfather et al used the experimental v1.2 SST CCI / NCEO dataset to assess the representation of the recent decades of ocean warming. The work confirms in useful detail the comment in a previous post that the version change of NOAA's long-term blended SST product last year does seem to reconcile their results with what we are getting in the satellite record.

The v2.0 SST CCI data, which we will release this year (2017), will be even more relevant to this sort of assessment, being more independent of in situ data than v1.2 and covering a longer time interval.

Global Drifter Program(me)

Over the last two days, I have been in a technical workshop with a range of contributors to the Global Drifter Program. Drifters are deployed around the ocean, and most measure sea surface temperature (SST), surface atmospheric pressure and (via their changing position) the speed of ocean currents. The data are transmitted back, mostly hourly, and shared around the meteorological and oceanographic centres as inputs to forecasts of the weather and ocean conditions. There are around 1400 drifters working in this way around the world as I write (see below).

Drifter status on 10/102016. For update to most recent image from the GDP, click here.

This in situ system is highly complementary to the satellite system, and both are needed to meet the needs of meteorology and understanding of climate. One of the presentations at the workshop was by chief of a group in NOAA that generates near-real time SST products, by empirically correlating radiances from meteorological satellites at drifter SST locations with the drifter SST measurements. This is a very effective means of using satellite data effectively and rapidly to interpolate the drifter SSTs spatially, capturing the fronts, eddies and structures across the global oceans that can't be deduced from ~1400 point measurements.

We had the chance to see the Scripps Institute of Oceanography's lab for assembling their drifters, and learned a lot about the detailed thinking and experimentation that goes into creating drifters that can operate in the harsh environment of the ocean. Tiny design decisions are crucial to the success of their deployment.

Picture of drifter from our visit, showing where the SST sensor sits. In (rare!) calm conditions at sea, the water line is by design around the transition between blue anti-fouling paint and the white paint (to minimise solar heating) on the hull. So the drifter measures SST at a depth of 17 cm in a dead calm, or a little deeper once biomass starts to accumulate on the perennially submerged components, weighing the drifter down.

In SST CCI, we don't try to blend satellite and drifter data. Instead, we treat the satellite and drifter array as two independent systems that can tell us about marine climate change. If the measurements made by two completely different technologies (remote sensing and in-water thermistors) tell us the same story of change then this greatly increases our confidence that we have accurately quantified the changing marine environment.

That is the importance of our figure in the last assessment report of IPCC, below.

Change in global mean SST temperature in satellite data only (red lines, derived from Along Track Scanning Radiometers) and in in situ measurements only (ensemble of black lines, mostly from drifters). From Chapter 2 of the fifth IPCC assessment report of the physical basis of climate change.

Perfect agreement is not expected, since all measurement systems have some level of uncertainty. Nonetheless, it is clear that these independent datasets agree closely about a lot of the year-to-year changes in the overall temperature of the oceans, and about the general rate of change.

The satellite-based curve has been updated within the current SST CCI project, and we are still working on further extension back in time. A previous blog discussed how, last year, some of the US datasets blending various data sources were revised in a manner that brought them into closer agreement with these results, obtained a few years earlier.

The discussions in the workshop over the past two days have been very informative. We 'satellite folks' learned a lot about nature of the drifter array from the manufacturers and deployers of the drifters.  This will greatly help us when we use drifters to validate the uncertainties we attach to satellite SST data, for example. Turning the raw data from either system into a curve describing global change is tricky task. We all now appreciate better the challenges faced when extracting useful information, needed by society, from both satellite and in situ systems.

Thanks to Luca Centurioni and Lance Braash for hosting us all and showing us around -- and to all the manufacturers of drifters who gave their time to explain their work and listen to how the satellite community use their data.

Sea surface temperatures in 2015

For SST folks, 2015 was an interesting year. There was the flurry around a certain paper in Science and of course the emergence of a major El Nino event. In SST CCI, the focus is to do a careful job on historical records. But, with some support from the National Centre for Earth Observation, Owen Embury has done a quick and rough (for us!) look at the SST record up to 2015. This builds on our recent experimental reprocessing, but brings the record more up to date by using some short cuts. (These will be superseded by the culmination of the SST CCI project, so these results are provisional, and just posted for interest.)

First, the global average sea surface temperature change over time since 1991, as shown below:

We see 2015 as the warmest year for SST, with a marked increase since 2012. A similar sharp rise was seen in 1997/8, which was also the result of a major El Nino event. As then, it is likely that the global SST will drop down again during 2016 to temperatures more typical of recent years. 

The warm water associated with the El Nino event is obvious right across the equatorial Pacific in the map for December 2015, corresponding to the last point on the time series above:

We have the recent El Nino as being the record event in the classic El Nino "3.4 region" -- although the margin compared to the 1997/8 event is close enough that we won't be confident of its record-holding status until we have done the proper job on SST CCI reprocessing:

In contrast, the anomaly in the "4 region" is clearly stronger in our data than for the earlier event, indicating that the hot action was located further west than in 1997/8. 

On a final note, it is a pity that the next dual-view instrument, Sea and Land Surface Temperature Radiometer (SLSTR), missed the action of 2015, but in the project we are all very pleased that it has successfully been launched. Looking forward to working on the data.

Paid internship looking at industrial thermal plumes in coastal waters

Elevated temperatures from use of seawater as coolant by large coastal installations can be seen in high resolution thermal imagery.

Note that the SSTs in this image are not quantitatively correct. The image is based on LandSat8 and the thermal channels unfortunately have calibration issues that prevent quantitative water surface temperature determination. However, it is interesting to look qualitatively at the data, since it is at ~100 m resolution the in infrared bands, and resolves features like this that are not seen in 1 km data typically used for SST.

EDF UK are offering a student summer paid internship for a project working with me to scope the potential in principal for thermal remote sensing at high resolution to help in monitoring and understanding the physical and ecological impacts of coolant plumes from power stations around the UK. 

It would be excellent to have well calibrated multi-channel thermal resolution observations at ~100 m scale resolution for applications like this and also remote sensing of lakes. It is good that there is interest around Europe in extending the capabilities of future Sentinel 2 missions in this direction. 

Closing date for internship applications: 8th February. Don't delay!

Image credit: Shaun Lee, University of Reading. (Thanks, Shaun.)

Interview and MOOC

ESA have released this short interview about sea surface temperature and why it is a variable that it is essential to measured in order to understand and monitor climate.

There is also a re-run of the Massive Online Open Course on "Climate from Space" in which I feature, again talking about SST and climate.

Experimental Reprocessing v1.2 results

The current phase of the SST CCI project involves two strands:

• migration of all components of a previously distributed system to one location where all data are held and significant processing power is available
• improvements to the way we derive sea surface temperature to achieve the stringent requirements of our users

Making improvements is an iterative process, where we introduce innovations progressively and validate they have positive impact. The team is currently assessing our most recent large-scale reprocessing, for which the new smooth-atmosphere algorithms have been used, along with other minor changes (e.g., to the prior assumptions used in optimal estimation).

The summary is that the stability of the AVHRR data streams is clearly improved, both with respect to the calibration anomalies on some instruments and sensitivity to desert dust outbreaks over the ocean.

Being experimental, we are not promoting use of this version of the data, but it can be shared with any interested researchers.

The next experiment reprocessing run will attempt to derive SSTs for the 1980s using our system, for the first time. It is due around the end of this year.

Why worry about all sources of errors?

There are many effects that act as sources of errors in a climate data record. No measured value is perfect, whether taken in the laboratory or inferred from radiances measured in low Earth orbit.

The most obvious characteristic to establish about a source of error is the magnitude of its effect. The (standard) uncertainty is a measure of the typical size of errors, and is generally what is represented by "error bars" on a plot.

Less obvious is the need to know whether the error is correlated between different measured values. This becomes very important for climate data records: looking at climate change, highly correlated errors are the ones we need to worry about.

To illustrate this, consider a climate data record (CDR) for sea surface temperature (SST). Full resolution satellite data typically measures an instantaneous SST across a pixel of about 1 km. For a typical case, we might have three categories of effect, causing uncertainties of different size. 

• Noise, which is uncorrelated between different pixels. 
• Ambiguity in obtaining the SST from the measured radiances, which tends to be correlated "locally", where the state of the atmosphere is similar.
• Systematic errors ("biases") including sensor calibration degradation over time, which tend to affect measured values in a highly correlated (non-random) way.

Data producers tend to put effort into correcting any "biases", but nonetheless, residual uncertainty remains after such corrections are applied.

For a single instantaneously measured SST from a well-designed instrument, the first two effects are the biggest, and may be comparable. 

However, for climate change analyses, we may be more interested in how SST over one area and period of time compares with SST for that area at a different time. The larger the spatial scale and longer the period of time considered, the less important noise becomes (the SST errors tend to average down when data are aggregated) and the relatively more important calibration effects become. 

This figure illustrates this effect for a reasonable set of assumptions for the case of SST. In a single 1 km SST retrieval, the random effects may dominate the uncertainty, followed by the locally systematic effects associated with retrieval ambiguity. However, as the scale of analysis of the SST data becomes larger in space and longer in time, first noise and then locally systematic effects become less important. If we are using the CDR across several years, more than one sensor is involved, and not only systematic calibration effects from a single sensor matter, but uncertainty in the (corrected) systematic differences between instruments in the sensor series also begin to matter.

Analyses of uncertainty budgets in SST products and for instrument design often focus on the regime at the left side of this diagram, where calibration effects (after bias correction) may be thought negligible. But CDRs also get used for applications at the right side of this diagram, where the systematic effects matter most. 

When it comes to creating climate data records, all categories of effect causing uncertainty need to be considered and characterised, as far as practicable. All types of error source are relevant to the applications of at least some CDR users.

Very easy download of basic SST CCI data

If you want some of our data in the simplest possible form, head to our new 'easy download' page. You can get the monthly SST time series for a particular latitude and longitude in a format readable into a spreadsheet.

For some applications, that is all someone needs, and deriving such data from the full archive could be too much work to be justified for a modest application.  For this reason, Guy Griffiths has created this simplest-possible interface.

More difficult is to make sure the right people find this page when they hunt for simple SST data. You could help by linking there from any SST-relevant web pages you control: promote the page up the search-engine rankings!

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.

Learning Python

After years of relying on IDL for interacting with data, I am taking the plunge and switching to Python. The tipping point was deciding that iPython notebooks are a good way of maintaining the links between results, figures and the code used to generate them. My first plot is based on SST CCI data, of course!

Geoscience Data Journal paper

An open-access journal article describing the SST CCI phase 1 datasets was published today.

It is published in Geoscience Data Journal. I think the advent of 'data journals' over the past few years is a good development. The traditional recourse of trying to shoe-horn a detailed data description into a paper with science results was not ideal, particularly for large complex datasets such as those created by reprocessing EO archives for climate.

The new paper is:

Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20

1. European Space Agency, ESRIN/Contract No. 4000101570/10/I-AM ‘Phase 1 of the ESA Climate Change Initiative SST_cci’

What differences to use in validation?

Validation is the comparison of (in this case) our satellite SSTs with temperature measured in situ, from buoys, ships, etc. Validation gives assurance that the satellite SSTs are, in a general sense, accurate. However, the comparison is complicated by the fact that different SSTs are genuinely different (geophysical differences), so that the difference between any two data points is a mix of error contributions and true differences. In addition, the SST CCI products include a number of SST estimates, each of which requires validation.

We therefore need to be clear about which in-situ/satellite comparisons will be made, and why. This post records the results of a review of our options, following discussions between myself and Gary Corlett (Leicester).

"Raw differences"

Here, we will compare the skin SST from the satellite to the nearest-in-time depth SST of the in situ measurement. In this case we expect certain systematic differences. (1) There is a geophysical difference based on the ocean thermal skin effect, which is typically of order -0.2 K, but also has a wind-speed dependence which should be clear in night-time differences. (2) There should be a trend in the raw difference with respect to the time separation of in situ and satellite: for example, in mid morning the ocean is typically warming, so in situ measurements after the satellite time will tend to be warmer. However, with respect to things that might affect the satellite retrieval adversely (but not directly the skin-depth SST difference) the systematic dependencies should be small; for example, a systematic effect in the raw differences with latitude should be no larger than we might be able to account for by the fact that mean wind speed (and therefore skin effect) varies with latitude.

"Skin-skin differences"

The idea here is to estimate the skin and depth effects at the time of the satellite observation and add these to the in situ observation. The in situ history is first interpolated to the satellite observation time, so giving an estimate of SST-20cm at the location and time of a satellite SST. Any 20-cm-to-subskin stratification is estimated using a model (usually small, and only ever large for day-time cases) and the skin effect is also estimated using a skin-effect model. There should ideally be no systematic effects with respect to latitude, wind, satellite-buoy-time-difference, etc, because the models are meant to account correctly (on average) for all the geophysical differences (other than those from comparing a point to a pixel, which are assumed to add zero-mean noise). This measure therefore tests the combination of "retrieval + adjustment for skin effect + adjustment in depth".

"Depth-depth differences"

In the SST products there is an adjustment provided that can be added to the fundamental satellite SST retrieval (of skin SST at the time of the satellite) to give an estimate of SST at typical drifting buoy depth (~20 cm) at standard local times of day (10.30 h or 22.30 h). To explore this, the satellite SST-20cm estimate for a standard local time will be differenced with the spatially-matched in situ SST history interpolated in time to the same local time. There should ideally be no systematic effects with respect to latitude, wind, satellite-buoy-time-difference, etc, because the adjustment is meant to account correctly (on average) for all the geophysical effects (other than those from comparing a point to a pixel, which are assumed to be zero-mean noise). This measure therefore tests the combination of "retrieval + adjustment for skin effect + adjustment in depth + adjustment in time". Compared to the skin-skin difference, this tests in addition the time-adjustment of the SST for the diurnal cycle.

"Daily mean depth-depth differences"

Although not generated in existing datasets, there has been a requirement which we are considering to estimate an adjustment to be added to the skin SST from the satellite which would give an estimate of the daily-mean SST at the location of the satellite observation. The day over which the mean is to be estimated is the UTC (i.e., GMT, not local) day which includes the time of the satellite observation. The in situ data would therefore consist of the average of the history of the in situ measurements over a 24 hour period. This comparison therefore tests "retrieval + adjustment for skin effect + adjustment in depth + adjustment to daily mean", and the spread of the results will include the uncertainty effect of estimation of the daily mean SST from a single observation. Systematic effects in the differences should be small.

"System Maturity" CORE-CLIMAX style

The SST CCI project is to create both new SST data and to prototype a system for how this SST climate data record can continue to be routinely provided in the future. The "system" is a processing chain that takes inputs (satellite radiance data, auxiliary data etc) and transforms these into SST products. It consists of something like 100000 lines of code, installed at the facility for Climate and Environmental Modelling from Space (CEMS) in Harwell.

CORE-CLIMAX [no kidding] is a European project. Within its scope is development of a means of encapsulating how "mature" systems for delivering climate data records are. It is quite instructive for a team like ours to evaluate itself against the various criteria in the "System Maturity Matrix" they propose. We just did a self evaluation, for AVHRR and analysis products, and find that in its current state, the project straddles a "research capability" and an "initial operations capability" in most areas. That seems right -- it is exactly where we would expect to be at this stage, working on science and also towards a functioning, sustainable system.

Here are our self assessment results. The green shaded boxes show the range of "maturity" of different aspects of the project within each of the metric categories (software readiness, metadata, user documentation, uncertainty characterisation, feedback/access and usage).

Heat Content vs Surface Temperature

The Guardian have (after inputs from ESA) updated the article discussed here to say that the "lousy indicator of climate change" comment applied to surface air temperature rather than sea surface temperature. Well, I suppose that is slightly better from my point of view, but I still don't agree with the point!

"Global surface temperature" time series are generally made up of surface air temperature over land and sea surface temperature over the oceans. Together, surface temperature, precipitation, wind and solar radiation are the principal elements that define our experience of weather and climate. These matter to people, and we need to present any climate changes (anthropogenic and natural) in terms of these elements. Surface temperature is not a simple indicator of the accumulated heat in the climate system, that is true. But it is an indicator of great relevance to society and the environment.