This post is a continuation of the May 31, 2010 post titled GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. Refer also to the comments on the thread of the cross post at WattsUpWithThat. Between publishing that post and now, on its Climate Explorer, KNMI has added land and ocean masking to its features for the GISS Land-Ocean Temperature Index (LOTI) data. (GISS LOTI is listed on their Select a monthly field – Observations webpage under the TEMPERATURE field as “1880-now anomalies: GISS”, using the 1200km button.) That masking allows us to present the impacts differently. We’ll be looking at the period starting in January 1982 because GISS transitions from HADISST to Reynolds OI.v2 sea surface temperature data during 1981. The GISS LOTI data, as of this writing, only extends to October 2011 at the KNMI Climate Explorer, so that’s why the data ends then.
The topic of discussion is the impact of GISS deleting sea surface temperature data in areas of the Arctic and Southern Oceans with seasonal and permanent sea ice and their replacing that sea surface temperature data with land surface temperature data, which naturally warms at a much higher rate. Figure 1 is a graph that illustrates the linear trends of the two relevant sea surface temperature datasets for the period of January 1982 to October 2011 on a zonal mean basis. It compares Reynolds OI.v2 sea surface temperature data, which is the source sea surface temperature data for the GISS LOTI product during that period, and the GISS LOTI data with the land surface temperatures masked. That is, it shows the source sea surface temperature data (Reynolds OI.v2 – in blue) and the sea surface temperature data after GISS has deleted the sea surface temperature data in areas of sea ice and replaced it with land surface temperature data (in purple). The methods used by GISS significantly alter the sea surface temperature data north of 50N and south of 50S.
The Goddard Institute of Space Studies (GISS) land-ocean temperature index (LOTI) data is a global temperature product that includes a mix of land and sea surface temperature data. GISS infills land surface areas that are missing data by extrapolating and averaging data over distances of 1200 km. In the 2010 paper Global Surface Temperature Change, Hansen et al explain:
The GISS analysis specifies the temperature anomaly at a given location as the weighted average of the anomalies for all stations located within 1200 km of that point, with the weight decreasing linearly from unity for a station located at that point to zero for stations located 1200 km or farther from the point in question.
The sea surface temperature datasets used by GISS in their LOTI product are spatially complete. From January 1880 to November 1981, GISS uses HADISST, which is an infilled dataset based on temperature measurements from buoys and ships during that period, and from December 1981 to present, GISS uses the satellite-based Reynolds OI.v2 data.
The process developed by GISS is further detailed in the above-linked paper and at their GISS Surface Temperature Analysis webpage. GISS takes a step that was the subject of the earlier post. GISS masks sea surface temperature data where there has been sea ice, including seasonal sea ice. They write on their webpage:
Areas covered occasionally by sea ice are masked using a time-independent mask. The Reynolds climatology is included, since it also may be used to find that mask.
In other words, sea surface temperature data are excluded permanently from areas of the open ocean that are covered (permanently or seasonally) by sea ice. That is, GISS deletes sea surface temperature data from the Arctic and Southern Oceans. GISS then replaces the deleted sea surface temperature data with land surface temperature data.
WHY DOES GISS DELETE SEA SURFACE TEMPERATURE DATA AND REPLACE IT WITH LAND SURFACE TEMPERATURE DATA?
GISS is using the difference in the warming rates of sea and land surface temperatures to inflate surface temperatures locally and globally. They are doing it for a number of reasons. They explain in Hansen et al (2010), paragraph 28:
We use ocean temperature change only in regions that are ice free all year (a map of this area is included in Appendix A) because our data set is intended to be temperature change of surface air.
Unfortunately for Hansen et al, that logic does not ring true. 70% of the globe is covered by water, and excluding areas with sea ice only addresses about 7% of the surface area of the global oceans. The other 93% of the global ocean data is still presented as sea surface temperature in their global temperature product, not surface air temperature.
Hansen et al (2010) continues:
Surface air temperature (SAT), measured at heights of 1.25–2 m at meteorological stations, is of most practical significance to humans, and it is usually SAT change that is reported in climate model studies. Change of sea surface temperature (SST) should be a good approximation to change of SAT in ice‐free ocean areas; climate model simulations [Hansen et al., 2007] suggest that long‐term SAT change over ice‐free ocean is only slightly larger than SST change.
Marine air temperature and sea surface temperature observations since 1950 contradict the climate models. That is, observed sea surface temperatures warm faster than marine air temperatures. This is another error with climate models. Refer to the discussion under the heading of A NOTE ABOUT MARINE AIR VERSUS SEA SURFACE TEMPERATURES in the post here.
And Hansen et al (2010) conclude that discussion of sea surface temperature with:
However, ocean water temperature does not go below the freezing point of water, while surface air temperature over sea ice can be much colder. As a result, SST change underestimates SAT change when sea ice cover changes. Indeed, most climate models find that the largest SAT changes with global warming occur in regions of sea ice [IPCC, 2007]. Thus, we estimate SAT changes in sea ice regions by extrapolating actual SAT measurements on nearby land or islands; if there are no stations within 1200 km, we leave the temperature change undefined.
This part of their discussion pertains only to seasons when there is sea ice in given grids. That is, the logic appears sensible during seasons when sea ice exists, and when the albedo of the snow-covered land surface is more similar to sea ice. Unfortunately, GISS applies the extrapolation year round, regardless of the season. As we are all aware, there is a seasonal component to polar temperatures, land surface albedo and sea ice cover at the poles. Polar temperatures during summer months at some of the land surface stations rise well above freezing. Snow melts as a result and exposes land surfaces, roads, etc., all of which would have albedos that are different than sea ice and would warm at faster rates. At the same time, sea ice melts, exposing the sea surface, and the sea surface temperature warms at a much slower rate than land surface temperature. That open ocean in many places, especially in the Arctic, lies between the land surface temperature measurement stations and the remaining sea ice, placing an open area, a barrier against extrapolation of sorts, between the surface stations and the sea ice. For those reasons, during periods of seasonal sea ice melt, it does not seem logical to exclude the sea surface temperature data and then extrapolate land surface temperature across open ocean to the remaining sea ice. In short, for part of the year, the method GISS uses to approximate unmeasured polar temperatures makes sense, but for the other, it appears like an excuse to indiscriminately adjust temperature data upwards.
THE IMPACT OF GISS MODIFYING POLAR SEA SURFACE TEMPERATURE DATA
As noted in the opening, the KNMI Climate Explorer includes a feature for the GISS LOTI data that allows users to mask land and sea surface portions of the data, leaving only the sea surface and land surface temperature data, respectively. Figures 1 and 2 present the Reynolds OI.v2 sea surface temperature data, which is the source data for GISS LOTI during the period, and the GISS LOTI data with the land surface temperature data masked, leaving only the GISS-modified sea surface temperature data.
Figure 1 compares the linear trends from January 1982 to October 2011 on a zonal mean basis. That is, the y-axis is the trend in deg C per decade, and the x-axis is the latitude, from the South Pole (90S) to the North Pole (90N). The two datasets are basically the same from 50S-50N, as they should be. I suspect the minor differences in between those latitudes are caused by the land mask missing the land surface temperature readings of islands that GISS uses in its data. But, then, south of 50S and north of 50N, the GISS-adjusted sea surface temperature data warms at much faster rates than the source data. In fact, south of 50S, the Southern Ocean is actually cooling, but the GISS modifications show it warming.
Figure 2 compares the two global sea surface temperature anomaly datasets on a time-series basis. As shown, by modifying the polar sea surface temperature data in areas with permanent and seasonal sea ice, GISS has raised the rate at which GLOBAL sea surface temperature anomalies have warmed over the past 30 years. They increased the trend of the global sea surface temperature anomalies from 0.088 deg C per decade to 0.125 deg C per decade or about 42%. That’s a significant rise in anyone’s book.
As a reference, Figure 3 compares the GISS LOTI data and its land and sea surface temperature components. The land and sea surface temperature data were determined using the corresponding masks. Matching the linear trend of the combined product with the components requires a weighted average of 31.54% land and 68.46% ocean.
So if we replace the GISS-adjusted sea surface temperature data with the source (Reynolds OI.v2) data in the weighted average, we can approximate the impact, on global combined land plus sea surface temperature, of the GISS adjustments to polar sea surface temperatures. See Figure 4. During the period of January 1982 to October 2011, with those adjustments, GISS raised the global surface temperature anomaly trend from 0.157 deg C per decade to 0.188 deg C per decade, or about 20%.
Arctic sea surface and land surface temperatures have warmed over the past three decades. But the difference in the rates at which they have warmed are considerable. At the other end of the world, the measured sea surface temperature of the Southern Ocean has cooled significantly over the last 30 years, while land surface temperature measuring stations show surface air temperature is warming. The actual warming rate of the surface air temperature of Arctic sea ice is an unknown, as is the warming or cooling rate of the sea ice surrounding Antarctica. As shown in the post, making a year-round adjustment to polar sea surface temperature does have a noticeable impact on global surface temperatures. Extending land surface temperature data out over the Arctic sea ice appears logical during periods of the year when the sea ice extends to the coast. However, during periods of seasonal sea ice melt, where open ocean is exposed, the adjustments to sea surface temperature do not make sense. Sea surface temperature exists then. Why not use it?
If GISS were to use sea surface temperature in the Arctic and Southern Oceans when the data exists there, global temperature anomalies would fall somewhere between the two curves shown in Figure 4. Or the GISS temperature anomalies would be in the neighborhood of the HadCRUT3 and NCDC data shown in Figure 5.
MY FIRST BOOK
The IPCC claims that the warming over the past 30 years can only be explained by the rise in anthropogenic greenhouse gases. Satellite-based sea surface temperature data disagrees with the IPCC’s claims. Most, if not all, of the rise in global sea surface temperature is shown to be the result of a natural process called the El Niño-Southern Oscillation, or ENSO. This is discussed in detail in my first book, If the IPCC was Selling Manmade Global Warming as a Product, Would the FTC Stop their deceptive Ads?, which is available in pdf and Kindle editions. A copy of the introduction, table of contents, and closing can be found here. Of course, donations are also welcome and gratefully accepted, because the rumor that bloggers skeptical of anthropogenic climate change are supported by big oil is simply that, a rumor.
The surface temperature data presented in this post are available through the KNMI Climate Explorer: