Saturday, 26 February 2011

Distributions and ranges for climate sensitivity from different lines of evidence

Distributions and ranges for climate sensitivity from different lines of evidence. The circle indicates the most likely value. The thin colored bars indicate very likely value (more than 90% probability). The thicker colored bars indicate likely values (more than 66% probability). Dashed lines indicate no robust constraint on an upper bound. The IPCC likely range (2 to 4.5°C) and most likely value (3°C) are indicated by the vertical grey bar and black line, respectively (Knutti and Hegerl 2008)

Melting Snow and Ice Warm Northern Hemisphere

The left image shows how much energy the NorthernHemisphere’s snow and ice—called the cryosphere—reflected on average between 1979 and 2008. Dark blue indicates more reflected energy, in Watts per square meter, and thus more cooling. The Greenland ice sheet reflects more energy than any other single location in the Northern Hemisphere. The second-largest contributor to cooling is the cap of sea ice over the Arctic Ocean.

The right image shows how the energy being reflected from the cryosphere has changed between 1979 and 2008. When snow and ice disappear, they are replaced by dark land or ocean, both of which absorb energy. The image shows that the Northern Hemisphere is absorbing more energy, particularly along the outer edges of the Arctic Ocean, where sea ice has disappeared, and in the mountains of Central Asia.

Climate Tipping Points

Probability distribution for the committed warming by GHGs between 1750 and 2005 from Ramanathan and Feng (2008). The normalized distribution is calculated from the probability density function given by Roe and Baker (2007), and the mean and standard deviation of the uncertainties associated with feedback processes are fitted for Sanderson et al. (2007). Shown are the climate-tipping elements and the temperature threshold range that initiates the tipping. 

Monday, 21 February 2011

Global Temperature Relative to 1800-1900

Northern Hemisphere proxy temperature reconstruction (purple - Mann et al. 2008) vs. the instrumental temperature record (black) and projected 21st Century surface temperature changes in various IPCC emissions scenarios (red, yellow, green).  Source: Copenhagen Diagnosis.

Global Temperature Model

Global surface temperature record (black - NASA GISS) with one-sigma error bars (blue) and model runs for the IPCC A1B emissions scenario (red, yellow). 

Friday, 18 February 2011

GHCN Monthly / Australia's high quality climate change dataset.

A great deal of effort went into the homogeneity adjustments. Yet the effects of the homogeneity adjustments on global average temperature trends are minor (Easterling and Peterson 1995b). However, on scales of half a continent or smaller, the homogeneity adjustments can have an impact. On an individual time series, the effects of the adjustments can be enormous. These adjustments are the best we could do given the paucity of historical station history metadata on a global scale. But using an approach based on a reference series created from surrounding stations means that the adjusted station's data is more indicative of regional climate change and less representative of local microclimatic change than an individual station not needing adjustments.

A change in the type of thermometer shelter used at many Australian observation sites in the early 20th century resulted in a sudden drop in recorded temperatures which is entirely spurious. It is for this reason that these early data are currently not used for monitoring climate change. Other common changes at Australian sites over time include location moves, construction of buildings or growth of vegetation around the observation site and, more recently, the introduction of Automatic Weather Stations.
The impacts of these changes on the data are often comparable in size to real climate variations, so they need to be removed before long-term trends are investigated. Procedures to identify and adjust for non-climatic changes in historical climate data generally involve a combination of:
Ø       investigating historical information (metadata) about the observation site,
Ø       using statistical tests to compare records from nearby locations, and
Ø       using comparison data recorded simultaneously at old and new locations, or with old and new instrument types.

 Full details of the procedure are described here 

Thursday, 10 February 2011

Hydrological Changes from IPCC Fourth Assessment Report: Climate Change 2007: Working Group I: The Physical Science Basis

Some of the more significant hydrological changes, with the two panels corresponding to DJF and JJA. The backdrop to these figures is the fraction of the AOGCMs (out of the 21 considered for this purpose) that predict an increase in mean precipitation in that grid cell (using the A1B scenario and comparing the period 2080 to 2099 with the control period 1980 to 1999)

The World's Biggest Cherrypick

Tuesday, 8 February 2011

NOAA State of the Climate 2010

  • For 2010, the combined global land and ocean surface temperature tied with 2005 as the warmest such period on record, at 0.62°C (1.12°F) above the 20th century average of 13.9°C (57.0°F). 1998 is the third warmest year-to-date on record, at 0.60°C (1.08°F) above the 20th century average.
  • The 2010 Northern Hemisphere combined global land and ocean surface temperature was the warmest year on record, at 0.73°C (1.31°F) above the 20th century average. The 2010 Southern Hemisphere combined global land and ocean surface temperature was the sixth warmest year on record, at 0.51°C (0.92°F) above the 20th century average.
  • The global land surface temperature for 2010 tied with 2005 as the second warmest on record, at 0.96°C (1.73°F) above the 20th century average. The warmest such period on record occurred in 2007, at 0.99°C (1.78°F) above the 20th century average.    
  • The global ocean surface temperature for 2010 tied with 2005 as the third warmest on record, at 0.49°C (0.88°F) above the 20th century average.
  • In 2010 there was a dramatic shift in the El Niño–Southern Oscillation, which influences temperature and precipitation patterns around the world. A moderate-to-strong El Niño at the beginning of the year transitioned to La Niña conditions by July. At the end of November, La Niña was moderate-to-strong.

Significant Climate Anomalies and Events in 2010

Monthly Food Price Index