Archive for the ‘Climatology’ Category

Papers based on CMIP5 data

March 14, 2012

I happened to ask over on Ed Hawkin’s blog whether he knew of any list of publications based on CMIP5 data (i.e. the suite of climate projections being produced for the IPCC AR5). He pointed me to the PCMDI page, which at the time was blank. So, here is my list of papers based on CMIP5 that I’ll try and keep up to date:

UPDATE: There are now papers being added to the official PCMDI list. It seems as though anyone can add papers to that list so there are lots of “Submitted” papers. The list below is of papers that have been published.

Individual papers

Ahlström et al (2012) “Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections” Environ. Res. Lett., 7, 044008.

Andrews et al. (2012) “Forcing, Feedbacks and Climate Sensitivity in CMIP5 Coupled Atmosphere-Ocean Climate Models” Geophys. Res. Lett., doi:10.1029/2012GL051607

Arora et al. (2011) “Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases” Geophys. Res. Lett., 38, L05805.

Biasutti (2013) “Forced Sahel rainfall trends in the CMIP5 archive” J. Geophys. Res., DOI: 10.1002/jgrd.50206, in press.

Bellouin et al. (2011) “Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate” J. Geophys. Res., 116, D20206.

Branstator and Teng (2012) “Potential Impact of Initialization on Decadal Predictions as Assessed for CMIP5” Geophys. Res. Lett., doi:10.1029/2012GL051974, in press.

Cai et al. (2012) “More extreme swings of the South Pacific convergence zone due to greenhouse warming” Nature, 488, 365–369.

Chang et al (2012) “CMIP5 multi-model ensemble projection of storm track change under global warming” J. Geophys. Res., doi:10.1029/2012JD018578, in press.

Christensen and Boberg (2012) “Temperature dependent climate projection deficiencies in CMIP5 models” Geophys. Res. Lett., 39, doi:10.1029/2012GL053650, in press.

Dai et al. (2012) “Increasing drought under global warming in observations and models” Nature Clim. Change, doi:10.1038/nclimate1633.

Dobrynin et al. (2012) “Evolution of the global wind wave climate in CMIP5 experiments” Geophys. Res. Lett., doi:10.1029/2012GL052843, in press.

Driscoll et al. (2012) “Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions”  J. Geophys. Res., doi:10.1029/2012JD017607, in press.

Dunn-Sigouin and Son (2013) “Northern Hemisphere blocking frequency and duration in the CMIP5 models” J. Geophys. Res., DOI: 10.1002/jgrd.50143, in press.

Gillett and Fyfe (2013) “Annular mode changes in the CMIP5 simulations” Geophys. Res. Lett., DOI: 10.1002/grl.50249, in press.

Good et al. (2011) “A step-response simple climate model to reconstruct and interpret AOGCM projections” Geophys. Res. Lett., 38, L01703.

Guilyardi et al. (2012) “A first look at ENSO in CMIP5” CLIVAR Exchanges, 17, 29-32.

Haywood et al. (2011) “The roles of aerosol, water vapor and cloud in future global dimming/brightening” J. Geophys. Res., 116, D20203.

Heuzé et al. (2013) “Southern Ocean bottom water characteristics in CMIP5 models” Geophys. Res. Lett., DOI: 10.1002/grl.50287.

Jiang et al. (2012) “Evaluation of Cloud and Water Vapor Simulations in CMIP5 Climate Models Using NASA “A-Train” Satellite Observations” J. Geophys. Res., doi:10.1029/2011JD017237. GFDL summary.

Jones et al. (2011) “The HadGEM2-ES implementation of CMIP5 centennial simulations” Geosci. Model Dev., 4, 543–570.

Kamae and Watanabe (2012) “On the robustness of tropospheric adjustment in CMIP5 models” Geophys. Res. Lett., doi:10.1029/2012GL054275, in press.

Kawase et al. (2011) “Future changes in tropospheric ozone under Representative Concentration Pathways (RCPs)” Geophys. Res. Lett., 38, L05801.

Kelley et al. (2012) “Mediterranean precipitation climatology, seasonal cycle, and trend as simulated by CMIP5” Geophys. Res. Lett., doi:10.1029/2012GL053416, in press.

Kim and Yu (2012) “The Two Types of ENSO in CMIP5 Models” Geophys. Res. Lett., doi:10.1029/2012GL052006. [paper pdf here]

Kim et al. (2012) “Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts” Geophys. Res. Lett., doi:10.1029/2012GL051644.

Knutti and Sedlácek (2012) “Robustness and uncertainties in the new CMIP5 climate model projections” Nature Climate Change, doi:10.1038/nclimate1716, in press.

Knutti et al. (2013) “Climate model genealogy: Generation CMIP5 and how we got there” Geophys. Res. Lett., 40, doi:10.1002/grl.50256.

Kug et al. (2012) “Improved simulation of two types of El Niño in CMIP5 models” Environ. Res. Lett., 7, 034002, doi:10.1088/1748-9326/7/3/034002.

Lau et al. (2013) “A canonical response of precipitation characteristics to Global Warming from CMIP5 models” Geophys. Res. Lett., DOI: 10.1002/grl.50420

Liu et al. (2012) “Co-variation of temperature and precipitation in CMIP5 models and satellite observations” Geophys. Res. Lett., doi:10.1029/2012GL052093, in press.

Massonnet et al. (2012) “Constraining projections of summer Arctic sea ice” The Cryosphere Discuss., 6, 2931-2959.

Meijers et al. (2012) “Representation of the Antarctic Circumpolar Current in the CMIP5 climate models and future changes under warming scenarios” J. Geophys. Res., doi:10.1029/2012JC008412, in press.

Mizuta (2012) “Intensification of extratropical cyclones associated with the polar jet change in the CMIP5 global warming projections” Geophys. Res. Lett., doi:10.1029/2012GL053032, in press.

Monerie et al. (2012) “Expected future changes in the African monsoon between 2030 and 2070 using some CMIP3 and CMIP5 models under a medium-low RCP scenario” J. Geophys. Res., doi:10.1029/2012JD017510, in press.

Nam et al. (2012) “The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models” Geophys. Res. Lett., doi:10.1029/2012GL053421, in press.

Oleson (2012) “Contrasts between Urban and Rural Climate in CCSM4 CMIP5 Climate Change Scenarios” J. Climate, 25, 1390–1412.

Osprey et al. (2013) “Stratospheric Variability in Twentieth-Century CMIP5 Simulations of the Met Office Climate Model: High Top versus Low Top” J. Climate, 26, 1595–1606.

Reichler et al. (2012) “A stratospheric connection to Atlantic climate variability” Nature Geoscience, doi:10.1038/ngeo1586.

Rotstayn et al. (2012) “Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations” Atmos. Chem. Phys., 12, 6377-6404.

Sabeerali et al. (2013) “Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs” J. Geophys. Res., doi: : 10.1002/jgrd.50403, in press.

Sarojini et al. (2012) “Fingerprints of changes in annual and seasonal precipitation from CMIP5 models over land and ocean” Geophys. Res. Lett., doi:10.1029/2012GL053373, in press.

Seager et al. (2012) “Projections of declining surface-water availability for the southwestern United States” Nature Climate Change, doi:10.1038/nclimate1787

Seth et al. (2013) “CMIP5 Projected Changes in the Annual Cycle of Precipitation in Monsoon Regions”  Journal of Climate 2013, doi: http://dx.doi.org/10.1175/JCLI-D-12-00726.1

Sillmann et al. (2013) “Climate extremes indices in the CMIP5 multi-model ensemble. Part 1: Model evaluation in the present climate” J. Geophys. Res., doi: 10.1002/jgrd.50203, in press.

Stevenson et al. (2012) “Will There Be a Significant Change to El Niño in the Twenty-First Century?” J. Climate, 25, 2129–2145.

Stroeve et al. (2012) “Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations” Geophys. Res. Lett., 39, L16502, doi:10.1029/2012GL052676.

Su et al. (2012) “Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using “A-Train” satellite observations and reanalysis data” J. Geophys. Res., doi:10.1029/2012JD018575, in press.

Taylor et al. (2011) “An Overview of CMIP5 and the Experiment Design” Bull. Am. Meteorol. Soc.

Tian et al. (2013) “Evaluating CMIP5 Models using AIRS Tropospheric Air Temperature and Specific Humidity Climatology” J. Geophys. Res., 118, in press.

Todd-Brown et al. (2012) “Causes of variation in soil carbon predictions from CMIP5 Earth system models and comparison with observations, Biogeosciences Discuss., 9, 14437-14473.

Turner et al. (2013) “An Initial Assessment of Antarctic Sea Ice Extent in the CMIP5 Models” J. Climate, 26, 1473–1484.

Villarini and Vecchi (2012) “Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models” Nature Climate Change, doi:10.1038/nclimate1530.

Villarini and Vecchi (2013) “Projected Increases in North Atlantic Tropical Cyclone Intensity from CMIP5 Models” J. Climate, 26, 3231–3240.

Wang  and Overland (2012) “A sea ice free summer Arctic within 30 years-an update from CMIP5 models” Geophys. Res. Lett., doi:10.1029/2012GL052868, in press.

Watanabe et al. (2011) “MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments” Geosci. Model Dev., 4, 845-872.

Williams et al (2012) “Diagnosing atmosphere–land feedbacks in CMIP5 climate models” Environ. Res. Lett., 7, 044003

Xu and Powell Jr. (2012) “Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models” Geosci. Model Dev. Discuss., 5, 3621-3645

Yang and Christensen (2012) “Arctic sea ice reduction and European cold winters in CMIP5 climate change experiments” Geophys. Res. Lett., doi:10.1029/2012GL053338, in press.

Yeh et al. (2012) “Changes in the Tropical Pacific SST Trend from CMIP3 to CMIP5 and Its Implication of ENSO” J. Climate, 25, 7764–7771.

Yin (2012) “Century to multi-century sea level rise projections from CMIP5 models” Geophys. Res. Lett., doi:10.1029/2012GL052947, in press.

Ying and Chong-Hai (2012) “Preliminary Assessment of Simulations of Climate Changes over China by CMIP5 Multi-Models” Atmospheric and Oceanic Science Letters, in press.

Zhang and Jin (2012) “Improvements in the CMIP5 simulations of ENSO-SSTA meridional width” Geophys. Res. Lett., doi:10.1029/2012GL053588, in press.

Zunz et al. (2012) “How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?” The Cryosphere Discuss., 6, 3539-3573.

Special Issues/Collections (some of the above papers may be in these Special Issues)

Climatic Change special issue on the Representative Concentration Pathways

Climate Dynamics IPSL and CNRM CMIP5 Special Issue (mostly submitted papers as of 15/03/2012)

Geoscientific Model Development Special Issue on Community software to support the delivery of CMIP5 (no papers linked to it as of 15/03/2012)

Journal of Climate Special Collection on CCSM4 and CESM1

Journal of Climate Special Collection on C4MIP

Books

Dong et al. (2012) The Atlas of Climate Change — Based on SEAP-CMIP5. Springer. 200pp.

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More BEST (but still not peer reviewed)

October 21, 2011

The BEST story rumbles on but still no peer reviewed results.

Instead, they’ve made 4 manuscripts available that have been submitted on their methods, the influence of the urban heat island, temperature records and stations quality for the US and global temperature variations (all 4 links go to pdfs).

This seems to be the key figure:

"Comparison of the Berkeley Average to existing land-only averages reported by the three major temperature groups." The differences in the late 20th Century arise from different definitions of "land" used by the four groups - the paper says that global averages match better in this period but I couldn't see that figure in the papers - if it's not there, it'd be nice to add it.

Here are a few interesting quotes from the papers after a very quick read. From the first paper:

This change [in global land mean temperature] is consistent with global land-surface warming results previously reported, but with reduced uncertainty.

I’d read this as GISS, NOAA and HadCRU being pretty much on the money but they’ve found the same thing using more data (the BEST record goes back to 1800, which is nice) and a different method.

From the second paper:

The small size, and its negative sign, [of the urban heat island effect] supports the key conclusion of prior groups that urban warming does not unduly bias estimates of recent global temperature change.

This pretty much confirms the recent work of Menne et al. (2010) and the Watts paper and will hopefully put this issue to bed. Indeed, the third paper, which specifically mentions Watts in the abstract in relation to SurfaceStations, then goes on to show that US station quality makes little impact on the recorded trend.

Paper four looks at the role of ENSO and the AMO in controlling decadal variability in the temperature data. This looks like the most interesting paper to me so I’ll have a closer look at that soon.

Overall, though, there doesn’t quite seem like enough material for 4 papers here. Maybe they’re trying to make it look like they’ve done more than re-re-re-confirm the results of other groups.

Useful climate tools and data sites

January 11, 2011

Here are some links to useful climate data/tools. There’re my favourite places to get simple data.

If anyone uses anything different, please leave let me know!

KMNI’s Climate Explorer – excellent tool to get loads of data and basic plots. I’ve always found the inface friendly too.

NASA’s GISS Surface Temperature Analysis (GISTEMP) – raw station data and nice plotting tool.

UEA’s Climatic Research Unit have lots of data but no plotting tools.

Daily Earth Temperatures from Satellites – if you want satellite derived temperatures from various levels, this is the place to go.

NOAA’s National Climatic Data Centre – looks like there’s lots there but I’ve never really looked through it.

The University of Wyoming’s weather balloon data – probably a bit niche for this list but this is an amazing archive of balloon data from all over the world! Surely you need to check just how strong the Antarctic inversion is today? No?

Snow again.

November 26, 2010

I wrote a post last winter about how the snow doesn’t mean that climate change is over.

Well, it’s snowing again.

And the warming on a global scale still hasn’t stopped:

October 2010 temperature anomalies relative to the period 1951-1980 from the NASA GISS webpage.

Skepticism or denial?

February 3, 2010

Whilst I would describe myself as a scientific skeptic, in that I will try to investigate claims before coming to a judgement, I would not say I was a “climate change skeptic”. This term is often used to label those that are irrationally dismissive of the scientific evidence (or worse). Several commentators on climate issues, notably George Monbiot of the Guardian, have now started referring to many within this group as “climate change deniers” as it appears that any amount of evidence counter to their stance will alter their belief in that position. One prominent blogger, though, found the use of the denial tag unhelpful and has set himself the challenge, as a layperson, “to make sense of the global warming and climate change debates” via a new blog.

Now, though, we have an opportunity to test the scientific integrity of one of these skeptics. Anthony Watts, an American weather presenter, blogger and self proclaimed climate change skeptic, was instrumental in setting up a web campaign to survey the United States climatological surface station records – SurfaceStations.org. This is a laudable scientific aim, regardless of the fact that it was done in the belief that it would show that the surface temperature recording method was flawed and that the warming trend observed in the US was an artefact of the local micro-conditions.

The analysis on the website consists of quite a lot of not-very-scientific comments about photographs on how poorly sited some of these stations are. Watts has also published a report with some of the photographs alongside their temperature records. However, Matthew Menne (a scientist at the American National Climatic Data Center) and co-authors have published a peer reviewed, systematic analysis of the US surface station temperature records. The results show that the poorly located stations, as determined by SurfaceStations.org, actually show a negative bias relative to the well located sites. This means that the poorly located sites introduce an artificial cooling in the temperature record, not a warming as Watts predicted. Clearly, the uncovering of such a bias in the surface station network in the US means that the infrastructure requires tighter regulation as it is not, at certain locations, doing its job properly.

In this situation, I suspect that a true skeptic would be proud that their effort had highlighted a real issue and contributed to the scientific understanding. However, as SurfaceStations.org approached their investigation with the hypothesis that the network would introduce artificial warming, how will they react?

Reference:

ResearchBlogging.orgM. J. Menne, C. N. Williams, & M. A. Palecki (2010). On the reliability of the U.S. Surface Temperature Record Journal of Geophysical Research : doi:10.1029/2009JD013094

Links:

The paper can be found here
There is a more thorough analysis of the paper by the Skeptical Science blog
There is some comment in The Guardian’s Environment blog

Britain’s snow and climate change

January 8, 2010

NOTE: This post is from January 2010. I put a temperature anomaly plot from October 2010 here and I’ll do one for November 2010 as soon as the data is available.

I’m sure most of the Brits out there have seen this amazing NASA image of Britain covered in snow.  I love satellite images and use them a lot in my research – they really help me get a grasp of the big picture.

But what does this cold weather tell us about climate change?  Well, if we examine the whole northern hemisphere and look at how the temperatures for December compared to those from the last 30 years, then we get an interesting picture:

So, northern Europe and North America were colder than usual.  But southern Europe, Greenland, the Arctic and north Africa were all warmer than usual.  The situation for January will probably be quite similar.  So, looking at the bigger picture, the recent cold conditions in the UK don’t really tell us much about climate change – we need to look on big scales in both time and area.