|
|
| (One intermediate revision by one other user not shown) |
| Line 1: |
Line 1: |
| {{Redirect|AGCM|the Italian competition regulator|Autorità Garante della Concorrenza e del Mercato}}
| | My name is Ronny Simmonds. I life in Luyksgestel (Netherlands).<br><br>My site [http://safedietsthatwork.bloghi.com/ diet plans] |
| {{Use dmy dates|date=July 2012}}
| |
| {{bots|deny=Citation bot}}
| |
| | |
| [[File:AtmosphericModelSchematic.png|thumb|right|350px|Climate models are systems of [[differential equations]] based on the basic laws of [[physics]], [[Fluid dynamics|fluid motion]], and [[chemistry]]. To “run” a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate [[winds]], [[heat transfer]], [[radiation]], [[relative humidity]], and surface [[hydrology]] within each grid and evaluate interactions with neighboring points.<ref>{{cite web|title=The First Climate Model|url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html|publisher=NOAA|accessdate=12 January 2014}}</ref>]]
| |
| | |
| A '''general circulation model (GCM)''' is a [[mathematical model]] of the general circulation of a planetary [[atmosphere]] or ocean and based on the [[Navier–Stokes equations]] on a rotating sphere with [[thermodynamics|thermodynamic]] terms for various energy sources ([[radiation]], [[latent heat]]). These equations are the basis for complex computer programs commonly used for [[simulation|simulating]] the atmosphere or ocean of the Earth. Atmospheric and oceanic GCMs (AGCM and [[Ocean general circulation model|OGCM]]) are key components of '''global climate models''' along with [[sea ice]] and land-surface components. GCMs and global climate models are widely applied for [[weather forecasting]], understanding the [[climate]], and projecting [[climate change]]. Versions designed for decade to century time scale climate applications were originally created by [[Syukuro Manabe]] and [[Kirk Bryan (oceanographer)|Kirk Bryan]] at the [[Geophysical Fluid Dynamics Laboratory]] in [[Princeton, New Jersey]].<ref name="noaa200">{{cite web |url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html |title=: The First Climate Model |publisher=NOAA 200th Celebration |year=2007 |accessdate=20 April 2010 }}</ref> These computationally intensive numerical models are based on the integration of a variety of fluid dynamical, chemical, and sometimes biological equations.
| |
| | |
| ==Note on nomenclature==
| |
| The [[initialism]] ''GCM'' stands originally for ''general circulation model''. Recently, a second meaning has come into use, namely ''global climate model''. While these do not refer to the same thing, General Circulation Models are typically the tools used for [[climate model|modelling climate]], and hence the two terms are sometimes used as if they were interchangeable. However, the term "global climate model" is ambiguous, and may refer to an integrated framework incorporating multiple components which may include a general circulation model, or may refer to the general class of [[Climate model|climate models]] that use a variety of means to represent the climate mathematically with differing levels of detail.
| |
| | |
| ==History: general circulation models==
| |
| {{See also|Numerical weather prediction}}
| |
| In 1956, Norman Phillips developed a mathematical model which could realistically depict monthly and seasonal patterns in the troposphere, which became the first successful [[climate model]].<ref>{{cite journal
| |
| |last=Phillips|first=Norman A.
| |
| |title=The general circulation of the atmosphere: a numerical experiment|journal=Quarterly Journal of the [[Royal Meteorological Society]]|date=April 1956|volume=82|issue=352|pages=123–154
| |
| |accessdate=31 December 2010|doi=10.1002/qj.49708235202|bibcode=1956QJRMS..82..123P}}</ref><ref>{{cite book
| |
| |title=Storm Watchers|page=210|year=2002|author=Cox, John D.|publisher=John Wiley & Sons, Inc.|ISBN=0-471-38108-X}}</ref> Following Phillips's work, several groups began working to create '''general circulation model'''s.<ref name="Lynch Ch10"/> The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the [[NOAA]] [[Geophysical Fluid Dynamics Laboratory]].<ref name="noaa200"/> By the early 1980s, the United States' [[National Center for Atmospheric Research]] had developed the Community Atmosphere Model; this model has been continuously refined into the 2000s.<ref>{{cite web|last=Collins|first=William D. |title=Description of the NCAR Community Atmosphere Model (CAM 3.0)|url=http://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf|publisher=[[University Corporation for Atmospheric Research]]|accessdate=3 January 2011|coauthors=et al.|date=June 2004}}</ref> In 1996, efforts began to initialize and model soil and vegetation types, which led to more realistic forecasts.<ref>{{cite journal| url=http://www.geog.ucla.edu/~yxue/pdf/1996jgr.pdf |title=Impact of vegetation properties on U.S. summer weather prediction|page=7419|author=Xue, Yongkang and Michael J. Fennessey|journal=[[Journal of Geophysical Research]] |volume=101|issue=D3|date=20 March 1996|accessdate=6 January 2011|publisher=[[American Geophysical Union]]|doi=10.1029/95JD02169|bibcode=1996JGR...101.7419X}}</ref> Coupled ocean-atmosphere climate models such as the [[Hadley Centre for Climate Prediction and Research]]'s [[HadCM3]] model are currently being used as inputs for [[climate change]] studies.<ref name="Lynch Ch10">{{cite book|last=Lynch|first=Peter|title=The Emergence of Numerical Weather Prediction|year=2006|publisher=[[Cambridge University Press]]|isbn=978-0-521-85729-1|pages=206–208|chapter=The ENIAC Integrations}}</ref> The role of [[gravity wave]]s was neglected within these models until the mid-1980s. Now, gravity waves are required within global climate models to simulate regional and global scale circulations accurately, though their broad spectrum makes their incorporation complicated.<ref>{{cite book|page=188|title=A climate modelling primer|author=McGuffie, K. and A. Henderson-Sellers|publisher=John Wiley and Sons|year=2005|accessdate=24 February 2011|ISBN=978-0-470-85751-9}}</ref>
| |
| | |
| ==Atmospheric vs oceanic models==
| |
| There are both atmospheric GCMs (AGCMs) and oceanic GCMs (OGCMs). An AGCM and an OGCM can be coupled together to form an atmosphere-ocean coupled general circulation model (CGCM or AOGCM). With the addition of other components (such as a sea ice model or a model for evapotranspiration over land), the AOGCM becomes the basis for a full [[climate model]]. Within this structure, different variations can exist, and their varying response to climate change may be studied (e.g., Sun and Hansen, 2003).
| |
| | |
| ==Modelling trends==
| |
| A recent trend in GCMs is to apply them as components of Earth system models, e.g. by coupling to [[ice sheet model]]s for the dynamics of the [[Greenland ice sheet|Greenland]] and [[Antarctic ice sheet]]s, and one or more [[chemical transport model]]s (CTMs) for [[chemical species|species]] important to climate. Thus a [[Carbon cycle#Carbon cycle modelling|carbon CTM]] may allow a GCM to better predict changes in [[carbon dioxide]] concentrations resulting from changes in [[human impact on the environment|anthropogenic]] emissions. In addition, this approach allows accounting for inter-system feedback: e.g. chemistry-climate models allow the possible effects of climate change on the recovery of the ozone hole to be studied.<ref>{{cite web |url=http://www.theozonehole.com/climate.htm |title=Tango in the Atmosphere: Ozone and Climate Change |publisher=NASA Earth Observatory |accessdate=20 April 2010 |date=February 2004 |first=Jeannie |last=Allen }}</ref>
| |
| | |
| Climate prediction uncertainties depend on uncertainties in chemical, physical, and social models (see IPCC scenarios below).<ref>{{Cite journal | last = Ken | first = Richard A
| |
| | title = Global Warming: Rising Global Temperature, Rising Uncertainty | journal=Science | volume = 292 | issue=5515
| |
| | pages = 192–194 | date = 13 April 2001 | url = http://www.sciencemag.org/cgi/content/full/292/5515/192 | pmid = 11305301
| |
| | doi = 10.1126/science.292.5515.192 | accessdate =20 April 2010 }}</ref> Progress has been made in incorporating more realistic chemistry and physics in the models, but significant uncertainties and unknowns remain, especially regarding the future course of human population, industry, and technology.
| |
| | |
| Note that many simpler levels of [[climate model]] exist; some are of only heuristic interest, while others continue to be scientifically relevant.
| |
| | |
| ==Model structure==
| |
| Three-dimensional (more properly four-dimensional) GCMs discretise the equations for fluid motion and integrate these forward in time. They also contain parameterisations for processes – such as convection – that occur on scales too small to be resolved directly. More sophisticated models may include representations of the carbon and other cycles.
| |
| | |
| A '''simple general circulation model (SGCM)''', a minimal GCM, consists of a [[dynamical core]] that relates material properties such as temperature to dynamical properties such as pressure and velocity. Examples are programs that solve the [[primitive equations]], given energy input into the model, and energy [[dissipation]] in the form of scale-dependent [[friction]], so that [[atmospheric wave]]s with the highest [[wavenumber]]s are the ones most strongly attenuated. Such models may be used to study atmospheric processes within a simplified framework but are not suitable for future climate projections.
| |
| | |
| '''Atmospheric GCMs (AGCMs)''' model the atmosphere (and typically contain a land-surface model as well) and impose [[sea surface temperature]]s (SSTs). A large amount of information including model documentation is available from [[Atmospheric Model Intercomparison Project|AMIP]].<ref>{{cite web |url=http://www-pcmdi.llnl.gov/projects/amip/index.php |title=Atmospheric Model Intercomparison Project |publisher=The Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory |accessdate=21 April 2010 }}</ref> They may include atmospheric chemistry.
| |
| * AGCMs consist of a dynamical core which integrates the equations of fluid motion, typically for:
| |
| ** surface pressure
| |
| ** horizontal components of velocity in layers
| |
| ** temperature and water vapor in layers
| |
| * There is generally a radiation code, split into solar/short wave and terrestrial/infra-red/long wave
| |
| * [[Parametrization (climate)|Parametrizations]] are used to include the effects of various processes. All modern AGCMs include parameterizations for:
| |
| ** convection
| |
| ** land surface processes, [[albedo]] and [[hydrology]]
| |
| ** cloud cover
| |
| | |
| A GCM contains a number of [[prognostic equation]]s that are stepped forward in time (typically winds, temperature, moisture, and surface pressure) together with a number of [[diagnostic equation]]s that are evaluated from the simultaneous values of the variables. As an example, pressure at any height can be diagnosed by applying the [[hydrostatic equation]] to the predicted surface pressure and the predicted values of temperature between the surface and the height of interest. The pressure diagnosed in this way then is used to compute the pressure gradient force in the time-dependent equation for the winds.
| |
| | |
| '''Oceanic GCMs (OGCMs)''' model the ocean (with fluxes from the atmosphere imposed) and may or may not contain a [[sea ice]] model. For example, the standard resolution of [[HadCM3#Ocean model (HadOM3)|HadOM3]] is 1.25 degrees in latitude and longitude, with 20 vertical levels, leading to approximately 1,500,000 variables.
| |
| | |
| '''Coupled atmosphere–ocean GCMs (AOGCMs)''' (e.g. [[HadCM3]], [[GFDL CM2.X]]) combine the two models. They thus have the advantage of removing the need to specify fluxes across the interface of the ocean surface. These models are the basis for sophisticated model predictions of future climate, such as are discussed by the [[Intergovernmental Panel on Climate Change|IPCC]].
| |
| | |
| AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. They are the only tools that could provide detailed regional predictions of future climate change. However, they are still under development. The simpler models are generally susceptible to simple analysis and their results are generally easy to understand. AOGCMs, by contrast, are often nearly as hard to analyse as the real climate system.
| |
| | |
| ===Model grids===
| |
| | |
| The fluid equations for AGCMs are discretised using either the [[finite difference method]] or the [[spectral method]]. For finite differences, a grid is imposed on the atmosphere. The simplest grid uses constant angular grid spacing (i.e., a latitude / longitude grid), however, more sophisticated non-rectantangular grids (e.g., icosahedral) and grids of variable resolution<ref>C. Jablonowski , M. Herzog , J. E. Penner , R. C. Oehmke , Q. F. Stout , B. van Leer, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1 January 1960.5091 "Adaptive Grids for Weather and Climate Models"] (2004). See also Christiane Jablonowski, [http://www-personal.umich.edu/~cjablono/amr.html Adaptive Mesh Refinement (AMR) for Weather and Climate Models] page (Retrieved 24 July 2010)</ref> are more often used.<ref>NCAR Command Language documentation: [http://www.ncl.ucar.edu/Document/Graphics/contour_grids.shtml Non-uniform grids that NCL can contour] (Retrieved 24 July 2010)</ref> The "LMDz" model can be arranged to give high resolution over any given section of the planet. [[HadGEM1]] (and other ocean models) use an ocean grid with higher resolution in the tropics to help resolve processes believed to be important for [[ENSO]]. Spectral models generally use a [[gaussian grid]], because of the mathematics of transformation between spectral and grid-point space. Typical AGCM resolutions are between 1 and 5 degrees in latitude or longitude: the Hadley Centre model HadCM3, for example, uses 3.75 in longitude and 2.5 degrees in latitude, giving a grid of 96 by 73 points (96 x 72 for some variables); and has 19 levels in the vertical. This results in approximately 500,000 "basic" variables, since each grid point has four variables ([[Wind speed|''u'',''v'']], [[Temperature|''T'']], [[Humidity|''Q'']]), though a full count would give more (clouds; soil levels). HadGEM1 uses a grid of 1.875 degrees in longitude and 1.25 in latitude in the atmosphere; HiGEM, a high-resolution variant, uses 1.25 x 0.83 degrees respectively.<ref>{{cite web
| |
| |url=http://higem.nerc.ac.uk/
| |
| |title=High Resolution Global Environmental Modelling (HiGEM) home page
| |
| |publisher=Natural Environment Research Council and Met Office
| |
| |date= 18 May 2004
| |
| |accessdate=5 October 2010 }}</ref> These resolutions are lower than is typically used for weather forecasting.<ref>{{cite web|title=Mesoscale modelling|url=http://www.metoffice.gov.uk/science/creating/hoursahead/mesoscale.html|accessdate=5 October 2010}}</ref> Ocean resolutions tend to be higher, for example HadCM3 has 6 ocean grid points per atmospheric grid point in the horizontal.
| |
| | |
| For a standard finite difference model, uniform gridlines converge towards the poles. This would lead to computational instabilities (see [[Courant–Friedrichs–Lewy condition|CFL condition]]) and so the model variables must be filtered along lines of latitude close to the poles. Ocean models suffer from this problem too, unless a rotated grid is used in which the North Pole is shifted onto a nearby landmass. Spectral models do not suffer from this problem. There are experiments using [[geodesic grid]]s<ref>{{cite web
| |
| |url=http://www.unisci.com/stories/20013/0924011.htm
| |
| |title=Climate Model Will Be First To Use A Geodesic Grid
| |
| |publisher=Daly University Science News
| |
| |date=24 September 2001
| |
| |accessdate=3 May 2011 }}</ref> and icosahedral grids, which (being more uniform) do not have pole-problems. Another approach to solving the grid spacing problem is to deform a Cartesian cube such that it covers the surface of a sphere.<ref>{{cite web|title=Gridding the sphere|url=http://mitgcm.org/projects/cubedsphere/|work=MIT GCM|accessdate=9 September 2010}}</ref>
| |
| | |
| ===Flux correction===
| |
| Some early incarnations of AOGCMs required a somewhat ''ad hoc'' process of "flux correction" to achieve a stable climate (not all model groups used this technique). This resulted from separately prepared ocean and atmospheric models each having a different implicit flux from the other component than the other component could actually provide. If uncorrected this could lead to a dramatic drift away from observations in the coupled model. However, if the fluxes were 'corrected', the problems in the model that led to these unrealistic fluxes might be unrecognised and that might affect the model sensitivity. As a result, there has always been a strong disincentive to use flux corrections, and the vast majority of models used in the current round of the [[Intergovernmental Panel on Climate Change]] do not use them. The model improvements that now make flux corrections unnecessary are various, but include improved ocean physics, improved resolution in both atmosphere and ocean, and more physically consistent coupling between atmosphere and ocean models.
| |
| Confidence in model projections is increased by the improved performance of several models that do not use flux adjustment. These models now maintain stable, multi-century simulations of surface climate that are considered to be of sufficient quality to allow their use for climate change projections.<ref>{{cite web|title=IPCC Third Assessment Report - Climate Change 2001 - Complete online versions|url=http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/309.htm|publisher=IPCC|accessdate=12 January 2014}}</ref>
| |
| | |
| ===Convection===
| |
| Moist convection causes the release of latent heat and is important to the Earth's energy budget. Convection occurs on too small a scale to be resolved by climate models, and hence it must be parameterized. This has been done since the earliest days of climate modelling, in the 1950s. [[Akio Arakawa]] did much of the early work, and variants of his scheme are still used,<ref>{{cite web|url=http://www.aip.org/history/climate/arakawa.htm |title=Arakawa's Computation Device |publisher=Aip.org |accessdate=2012-02-18}}</ref> although there are a variety of different schemes now in use.<ref>{{cite web|url=http://grads.iges.org/reps/rep27/colarep27.html |title=COLA Report 27 |publisher=Grads.iges.org |date=1996-07-01 |accessdate=2012-02-18}}</ref><ref>{{cite web|url=http://www-pcmdi.llnl.gov/projects/modeldoc/amip/10Tbl2.10.html |title=Table 2-10 |publisher=Pcmdi.llnl.gov |accessdate=2012-02-18}}</ref><ref>{{cite web|url=http://rainbow.llnl.gov/projects/modeldoc/cmip/table4.html |title=Table of Rudimentary CMIP Model Features |publisher=Rainbow.llnl.gov |date=2004-12-02 |accessdate=2012-02-18}}</ref> Clouds are typically parametrized, not because their physical processes are poorly understood, but because they occur on a scale smaller than the resolved scale of most GCMs. The causes and effects of their small scale actions on the large scale are represented by large scale parameters, hence "parameterization". The fact that cloud processes are not perfectly parameterized is due in part to a lack of understanding of clouds, but not due to some inherent shortcoming of the method.<ref>{{cite web|url=http://www.aip.org/history/climate/GCM.htm |title=General Circulation Models of the Atmosphere |publisher=Aip.org |accessdate=2012-02-18}}</ref>
| |
| | |
| ===Output variables===
| |
| Most models include software to diagnose a wide range of variables for comparison with observations or [[process study|study of processes]] within the atmosphere. An example is the 1.5-metre temperature, which is the standard height for near-surface observations of air temperature. This temperature is not directly predicted from the model but is deduced from the surface and lowest-model-layer temperatures. Other software is used for creating plots and animations.
| |
| | |
| ==Projections of future climate change==
| |
| {{Main|Global warming}}
| |
| [[File:Animation of projected annual mean surface air temperature from 1970-2100, based on SRES emissions scenario A1B (NOAA GFDL CM2.1).webm|thumb|480px|right|alt=In the 21st century, changes in global mean temperature are projected to vary across the world|Projected annual mean surface air temperature from 1970-2100, based on [[Special Report on Emissions Scenarios|SRES]] emissions scenario A1B, using the NOAA GFDL CM2.1 climate model (credit: [[NOAA]] [[Geophysical Fluid Dynamics Laboratory]]).<ref name="gfdl cm2.1 global warming projections">
| |
| {{citation
| |
| | url=http://www.gfdl.noaa.gov/patterns-of-greenhouse-warming-ar4
| |
| | title=NOAA GFDL Climate Research Highlights Image Gallery: Patterns of Greenhouse Warming
| |
| | publisher=NOAA GFDL
| |
| | date=9 October 2012
| |
| | author=NOAA Geophysical Fluid Dynamics Laboratory (GFDL)
| |
| }}
| |
| </ref>]]
| |
| Coupled ocean–atmosphere GCMs use [[transient climate simulation]]s to project/predict future temperature changes under various scenarios. These can be idealised scenarios (most commonly, CO<sub>2</sub> increasing at 1%/yr) or more realistic (usually the "IS92a" or more recently the [[Special Report on Emissions Scenarios|SRES]] scenarios). Which scenarios should be considered most realistic is currently uncertain, as the projections of future CO<sub>2</sub> (and sulphate) emission are themselves uncertain.
| |
| | |
| The 2001 [[IPCC Third Assessment Report]] [http://www.grida.no/climate/ipcc_tar/wg1/fig9-3.htm figure 9.3] shows the global mean response of 19 different coupled models to an idealised experiment in which CO<sub>2</sub> is increased at 1% per year.<ref>{{cite web|url=http://www.grida.no/climate/ipcc_tar/wg1/348.htm#fig93 |title=Climate Change 2001: The Scientific Basis |publisher=Grida.no |accessdate=2012-02-18}}</ref> [http://www.grida.no/climate/ipcc_tar/wg1/fig9-5.htm Figure 9.5] shows the response of a smaller number of models to more realistic forcing. For the 7 climate models shown there, the temperature change to 2100 varies from 2 to 4.5 °C with a median of about 3 °C.
| |
| | |
| Future scenarios do not include unknowable events – for example, volcanic eruptions or changes in solar forcing. These effects are believed to be small in comparison to GHG forcing in the long term, but large volcanic eruptions, for example, are known to exert a temporary cooling effect.
| |
| | |
| Human emissions of GHGs are an external input to the models, although it would be possible to couple in an economic model to provide these as well. Atmospheric GHG levels are usually supplied as an input, though it is possible to include a carbon cycle model including land vegetation and oceanic processes to calculate GHG levels.
| |
| | |
| ===Emissions scenarios===
| |
| {{See also|economics of global warming#Scenarios}}
| |
| [[File:Projected change in annual mean surface air temperature from the late 20th century to the middle 21st century, based on SRES emissions scenario A1B.png|thumb|left|alt=In the 21st century, changes in global mean temperature are projected to vary across the world|Projected change in annual mean surface air temperature from the late 20th century to the middle 21st century, based on SRES emissions scenario A1B (credit: [[NOAA]] [[Geophysical Fluid Dynamics Laboratory]]).<ref name="gfdl cm2.1 global warming projections"/>]]
| |
| For the six SRES marker scenarios, IPCC (2007:7–8) gave a "best estimate" of global mean temperature increase (2090–2099 relative to the period 1980–99) that ranged from 1.8 °C to 4.0 °C.<ref name="ar4 spm projections"/> Over the same time period, the "likely" range (greater than 66% probability, based on expert judgement) for these scenarios was for a global mean temperature increase of between 1.1 and 6.4 °C.<ref name="ar4 spm projections">
| |
| {{citation
| |
| | chapter=Summary for Policymakers
| |
| | chapter-url=http://www.ipcc.ch/publications_and_data/ar4/syr/en/spm.html
| |
| | at=[http://www.ipcc.ch/publications_and_data/ar4/syr/en/spms3.html 3. Projected climate change and its impacts]
| |
| }}, in {{harvnb|IPCC AR4 SYR|2007}}
| |
| </ref>
| |
| | |
| Pope (2008) described a study where climate change projections were made using several different emission scenarios.<ref>
| |
| {{cite web
| |
| |year=2008
| |
| |title=Met Office: The scientific evidence for early action on climate change
| |
| |publisher=Met Office website
| |
| |author=Pope, V.
| |
| |url=http://www.metoffice.gov.uk/climatechange/policymakers/action/evidence.html
| |
| |accessdate=7 March 2009
| |
| | archiveurl=https://web.archive.org/web/20101229170710/http://www.metoffice.gov.uk/climatechange/policymakers/action/evidence.html
| |
| | archivedate=29 December 2010
| |
| }}
| |
| </ref> In a scenario where global emissions start to decrease by 2010 and then decline at a sustained rate of 3% per year, the likely global average temperature increase was predicted to be 1.7 °C above pre-industrial levels by 2050, rising to around 2 °C by 2100. In a projection designed to simulate a future where no efforts are made to reduce global emissions, the likely rise in global average temperature was predicted to be 5.5 °C by 2100. A rise as high as 7 °C was thought possible but less likely.
| |
| | |
| Sokolov ''et al.'' (2009) examined a scenario designed to simulate a future where there is no policy to reduce emissions. In their integrated model, this scenario resulted in a median warming over land (2090–99 relative to the period 1980–99) of 5.1 °C. Under the same emissions scenario but with different modeling of the future climate, the predicted median warming was 4.1 °C.<ref>{{cite journal
| |
| |year=2009
| |
| |title=Probabilistic Forecast for 21st century Climate Based on Uncertainties in Emissions (without Policy) and Climate Parameters
| |
| |author=Sokolov, A.P. ''et al.''
| |
| |journal=Journal of Climate
| |
| |volume=22
| |
| |issue=19
| |
| |pages=5175–5204
| |
| |doi=10.1175/2009JCLI2863.1
| |
| |url=http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2F2009JCLI2863.1
| |
| |accessdate=12 January 2009|bibcode = 2009JCli...22.5175S }}</ref>
| |
| | |
| ===Accuracy of models that predict global warming===
| |
| [[Image:Hadcm3-era-sst-annual.png|thumb|240px|SST errors in [[HadCM3]]]]
| |
| [[Image:Climate model NA annual precipitation 2002.jpg|thumb|240px|North American precipitation from various models.]]
| |
| [[Image:Global Warming Predictions.png|thumb|240px|Temperature predictions from some climate models assuming the SRES A2 emissions scenario.]]
| |
| | |
| AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. However, they are still under development and uncertainties remain. They may be coupled to models of other processes, such as the [[carbon cycle]], so as to better model feedback effects. Most recent simulations show "plausible" agreement with the measured temperature anomalies over the past 150 years, when forced by observed changes in greenhouse gases and aerosols, and better agreement is achieved when both natural and man-made forcings are included.<ref name='f4'>IPCC, [http://www.grida.no/climate/ipcc_tar/wg1/005.htm Summary for Policy Makers], [http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc_tar/wg1/figspm-4.htm Figure 4], in {{citation
| |
| | year = 2001
| |
| | author=IPCC TAR WG1
| |
| | author-link = IPCC
| |
| | title = Climate Change 2001: The Scientific Basis
| |
| | series = Contribution of Working Group I to the [[IPCC Third Assessment Report|Third Assessment Report]] of the Intergovernmental Panel on Climate Change
| |
| | editor = Houghton, J.T.; Ding, Y.; Griggs, D.J.; Noguer, M.; van der Linden, P.J.; Dai, X.; Maskell, K.; and Johnson, C.A.
| |
| | publisher=Cambridge University Press
| |
| | url = http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc_tar/wg1/index.htm
| |
| | isbn = 0-521-80767-0
| |
| }} (pb: {{ISBNT|0-521-01495-6}}).</ref><ref>{{citation needed|date=September 2011}} <!-- Urls are not citations. --> {{cite web | title=Simulated global warming 1860–2000 | url=http://www.hadleycentre.gov.uk/research/hadleycentre/pubs/talks/sld017.html }}</ref>
| |
| | |
| No model – whether a wind-tunnel model for designing aircraft, or a climate model for projecting global warming – perfectly reproduces the system being modeled. Such inherently imperfect models may nevertheless produce useful results. In this context, GCMs are capable of reproducing the general features of the observed global temperature over the past century.<ref name='f4'/>
| |
| | |
| A debate over how to reconcile climate model predictions that upper air (tropospheric) warming should be greater than surface warming, with observations some of which appeared to show otherwise<ref>The National Academies Press website press release, Jan. 12, 2000:
| |
| [http://web.archive.org/web/20060420125451/http://www4.nationalacademies.org/news.nsf/isbn/0309068916?OpenDocument Reconciling Observations of Global Temperature Change].</ref> now appears to have been resolved in favour of the models, following revisions to the data: see [[satellite temperature record]].
| |
| | |
| The effects of [[clouds]] are a significant area of uncertainty in climate models. Clouds have ''competing'' effects on the climate. One of the roles that clouds play in climate is in cooling the surface by reflecting sunlight back into space; another is warming by increasing the amount of infrared radiation emitted from the atmosphere to the surface.<ref>[http://web.archive.org/web/20000901022925/http://liftoff.msfc.nasa.gov/academy/space/greenhouse.html Nasa Liftoff to Space Exploration Website: Greenhouse Effect]. Archive.com. Recovered 1 Oct 2012.</ref> In the 2001 IPCC report on climate change, the possible changes in cloud cover were highlighted as one of the dominant uncertainties in predicting future climate change;<ref>{{citation needed|date=September 2011}} <!-- WHICH report? Section or page number also needed. --></ref> see also<ref>{{cite journal |url=http://ams.allenpress.com/amsonline/?request=get-document&doi=10.1175%2FJCLI3799.1
| |
| |last=Soden |first= Brian J.|first2=Isaac M. |last2=Held |year=2006 |title=An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models |journal=J. Climate |issue= 19 |pages= 3354–3360 |doi= 10.1175/JCLI3799.1|bibcode = 2006JCli...19.3354S }}</ref>
| |
| | |
| Thousands of climate researchers around the world use climate models to understand the climate system. There are thousands of papers published about model-based studies in peer-reviewed journals – and a part of this research is work improving the models. Improvement has been difficult but steady (most obviously, state of the art AOGCMs no longer require flux correction), and progress has sometimes led to discovering new uncertainties.
| |
| | |
| In 2000, a comparison between measurements and dozens of GCM simulations of [[ENSO]]-driven tropical precipitation, water vapor, temperature, and outgoing longwave radiation found similarity between measurements and simulation of most factors. However the simulated change in precipitation was about one-fourth less than what was observed. Errors in simulated precipitation imply errors in other processes, such as errors in the evaporation rate that provides moisture to create precipitation. The other possibility is that the satellite-based measurements are in error. Either indicates progress is required in order to monitor and predict such changes.<ref>[http://ams.allenpress.com/amsonline/?request=get-abstract&doi=10.1175%2F1520-0442(2000)013%3C0538:TSOTTH%3E2.0.CO%3B2 ]{{dead link|date=February 2012}}</ref>
| |
| | |
| A more complete discussion of climate models is provided in the IPCC's Third Assessment Report.<ref>McAvaney ''et al.'', [http://www.grida.no/climate/ipcc_tar/wg1/308.htm Chapter 8: Model Evaluation],{{Page needed|date=December 2011}} in {{Citation
| |
| | year = 2001
| |
| | author=IPCC TAR WG1
| |
| | author-link = IPCC
| |
| | title = Climate Change 2001: The Scientific Basis
| |
| | series = Contribution of Working Group I to the [[IPCC Third Assessment Report|Third Assessment Report]] of the Intergovernmental Panel on Climate Change
| |
| | editor = Houghton, J.T.; Ding, Y.; Griggs, D.J.; Noguer, M.; van der Linden, P.J.; Dai, X.; Maskell, K.; and Johnson, C.A.
| |
| | publisher=Cambridge University Press
| |
| | url = http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc_tar/wg1/index.htm
| |
| | isbn = 0-521-80767-0
| |
| }} (pb: {{ISBNT|0-521-01495-6}}).</ref>
| |
| | |
| * The model mean exhibits good agreement with observations.
| |
| * The individual models often exhibit worse agreement with observations.
| |
| * Many of the non-flux adjusted models suffered from unrealistic climate drift up to about 1 °C/century in global mean surface temperature.
| |
| * The errors in model-mean surface air temperature rarely exceed 1 °C over the oceans and 5 °C over the continents; precipitation and sea level pressure errors are relatively greater but the magnitudes and patterns of these quantities are recognisably similar to observations.
| |
| * Surface air temperature is particularly well simulated, with nearly all models closely matching the observed magnitude of variance and exhibiting a correlation > 0.95 with the observations.
| |
| * Simulated variance of sea level pressure and precipitation is within ±25% of observed.
| |
| * All models have shortcomings in their simulations of the present day climate of the stratosphere, which might limit the accuracy of predictions of future climate change.
| |
| ** There is a tendency for the models to show a global mean cold bias at all levels.
| |
| ** There is a large scatter in the tropical temperatures.
| |
| ** The [[polar night jet]]s in most models are inclined poleward with height, in noticeable contrast to an equatorward inclination of the observed jet.
| |
| ** There is a differing degree of separation in the models between the winter [[sub-tropical jet]] and the polar night jet.
| |
| * For nearly all models the r.m.s. error in zonal- and annual-mean surface air temperature is small compared with its natural variability.
| |
| ** There are problems in simulating natural seasonal variability. {{citation needed|date=September 2011}} [http://pubs.giss.nasa.gov/abstracts/2000/CoveyAbeOuchi.html ( 2000)]
| |
| *** In flux-adjusted models, seasonal variations are simulated to within 2 [[kelvin|K]] of observed values over the oceans. The corresponding average over non-flux-adjusted models shows errors up to about 6 K in extensive ocean areas.
| |
| *** Near-surface land temperature errors are substantial in the average over flux-adjusted models, which systematically underestimates (by about 5 K) temperature in areas of elevated terrain. The corresponding average over non-flux-adjusted models forms a similar error pattern (with somewhat increased amplitude) over land.
| |
| *** In Southern Ocean mid-latitudes, the non-flux-adjusted models overestimate the magnitude of January-minus-July temperature differences by ~5 K due to an overestimate of summer (January) near-surface temperature. This error is common to five of the eight non-flux-adjusted models.
| |
| *** Over Northern Hemisphere mid-latitude land areas, zonal mean differences between July and January temperatures simulated by the non-flux-adjusted models show a greater spread (positive and negative) about observed values than results from the flux-adjusted models.
| |
| *** The ability of coupled GCMs to simulate a reasonable seasonal cycle is a necessary condition for confidence in their prediction of long-term climatic changes (such as global warming), but it is not a sufficient condition unless the seasonal cycle and long-term changes involve similar climatic processes.
| |
| * Coupled climate models do not simulate with reasonable accuracy clouds and some related hydrological processes (in particular those involving upper tropospheric humidity). Problems in the simulation of clouds and upper tropospheric humidity, remain worrisome because the associated processes account for most of the uncertainty in climate model simulations of anthropogenic change.
| |
| | |
| The precise magnitude of future changes in climate is still uncertain;<ref>Cubasch ''et al.'', [http://www.grida.no/climate/ipcc_tar/wg1/338.htm Chapter 9: Projections of Future Climate Change], [http://www.grida.no/climate/ipcc_tar/wg1/339 Executive Summary], in {{Citation
| |
| | year = 2001
| |
| | author=IPCC TAR WG1
| |
| | author-link = IPCC
| |
| | title = Climate Change 2001: The Scientific Basis
| |
| | series = Contribution of Working Group I to the [[IPCC Third Assessment Report|Third Assessment Report]] of the Intergovernmental Panel on Climate Change
| |
| | editor = Houghton, J.T.; Ding, Y.; Griggs, D.J.; Noguer, M.; van der Linden, P.J.; Dai, X.; Maskell, K.; and Johnson, C.A.
| |
| | publisher=Cambridge University Press
| |
| | url = http://www.grida.no/publications/other/ipcc%5Ftar/?src=/climate/ipcc_tar/wg1/index.htm
| |
| | isbn = 0-521-80767-0
| |
| }} (pb: {{ISBNT|0-521-01495-6}}).</ref> for the end of the 21st century (2071 to 2100), for [[Special Report on Emissions Scenarios|SRES]] scenario A2, the change of global average SAT change from AOGCMs compared with 1961 to 1990 is +3.0 °C (4.8 °F) and the range is +1.3 to +4.5 °C (+2 to +7.2 °F).
| |
| | |
| ==Relation to weather forecasting==
| |
| | |
| The global climate models used for climate projections are very similar in structure to (and often share computer code with) [[numerical weather prediction|numerical models for weather prediction]] but are nonetheless logically distinct.
| |
| | |
| Most [[weather forecasting]] is done on the basis of interpreting the output of numerical model results. Since forecasts are short—typically a few days or a week—such models do not usually contain an ocean model but rely on imposed SSTs. They also require accurate initial conditions to begin the forecast—typically these are taken from the output of a previous forecast, with observations blended in. Because the results are needed quickly the predictions must be run in a few hours; but because they only need to cover a week of real time these predictions can be run at higher resolution than in climate mode. Currently the [[ECMWF]] runs at {{convert|40|km|abbr=on|adj=on}} resolution<ref>[http://www.ecmwf.int/index_forecasts.html ]{{dead link|date=February 2012}}</ref> as opposed to the {{convert|100|to|200|km|abbr=on|adj=on}} scale used by typical climate models. Often nested models are run forced by the global models for boundary conditions, to achieve higher local resolution: for example, the [[Met Office]] runs a mesoscale model with an {{convert|11|km|abbr=on|adj=on}} resolution<ref>[http://www.metoffice.gov.uk/research/nwp/numerical/operational/index.html ]{{dead link|date=February 2012}}</ref> covering the UK, and various agencies in the U.S. also run nested models such as the NGM and NAM models. Like most global numerical weather prediction models such as the [[Global Forecast System|GFS]], global climate models are often spectral models<ref>{{cite web|url=http://www-das.uwyo.edu/~geerts/cwx/notes/chap12/nwp_gcm.html |title=What are general circulation models (GCM)? |publisher=Das.uwyo.edu |accessdate=2012-02-18}}</ref> instead of grid models. Spectral models are often used for global models because some computations in modeling can be performed faster thus reducing the time needed to run the model simulation.
| |
| | |
| ==Computations involved==
| |
| '''Climate models''' use [[quantitative method]]s to simulate the interactions of the [[Earth's atmosphere|atmosphere]], oceans, [[land surface]], and [[cryosphere|ice]]. They are used for a variety of purposes from study of the dynamics of the climate system to projections of future [[climate]].
| |
| | |
| All climate models take account of incoming energy as short wave [[electromagnetic radiation]], chiefly [[Visible spectrum|visible]] and short-wave (near) [[infrared]], as well as outgoing energy as long wave (far) infrared electromagnetic radiation from the earth. Any imbalance results in a [[First law of thermodynamics|change in temperature]].
| |
| | |
| The most talked-about models of recent years have been those relating temperature to [[exhaust gas|emission]]s of [[carbon dioxide]] (see [[greenhouse gas]]). These models project an upward trend in the [[surface temperature record]], as well as a more rapid increase in temperature at higher altitudes.<ref>Meehl ''et al.'', [http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch10.html Climate Change 2007 Chapter 10: Global Climate Projections],{{Page needed|date=December 2011}} in {{Citation
| |
| | year = 2007
| |
| | author = IPCC AR4 WG1
| |
| | author-link = IPCC
| |
| | title = Climate Change 2007: The Physical Science Basis
| |
| | series = Contribution of Working Group I to the [[IPCC Fourth Assessment Report|Fourth Assessment Report]] of the Intergovernmental Panel on Climate Change
| |
| | editor = Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; and Miller, H.L.
| |
| | publisher = Cambridge University Press
| |
| | url = http://www.ipcc.ch/publications_and_data/ar4/wg1/en/contents.html
| |
| | isbn = 978-0-521-88009-1
| |
| }} (pb: {{ISBNT|978-0-521-70596-7}})
| |
| </ref>
| |
| | |
| Three (or more properly, four since time is also considered) dimensional GCM's discretise the equations for fluid motion and energy transfer and integrate these over time. They also contain parametrisations for processes—such as convection—that occur on scales too small to be resolved directly.
| |
| | |
| Atmospheric GCMs (AGCMs) model the atmosphere and impose [[sea surface temperature]]s as boundary conditions. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. [[HadCM3]], [[EdGCM]], [[GFDL CM2.X]], ARPEGE-Climat<ref>[http://www.cnrm.meteo.fr/gmapdoc/spip.php?article202 ARPEGE-Climat homepage, Version 5.1], 3 Sep 2009. Retrieved 1 Oct 2012. [http://www.cnrm.meteo.fr/gmgec/spip.php?article83 ARPEGE-Climat homepage], 6 Aug 2009. Retrieved 1 Oct 2012.</ref>) combine the two models.
| |
| | |
| Models can range from relatively simple to quite complex:
| |
| * A simple [[radiant heat]] transfer model that treats the earth as a single point and averages outgoing energy
| |
| * this can be expanded vertically (radiative-convective models), or horizontally
| |
| * finally, (coupled) atmosphere–ocean–[[sea ice]] '''global climate models''' discretise and solve the full equations for mass and energy transfer and radiant exchange.
| |
| | |
| This is not a full list; for example "box models" can be written to treat flows across and within ocean basins. Furthermore, other types of modelling can be interlinked, such as [[land use]], allowing researchers to predict the interaction between climate and ecosystems.
| |
| | |
| ==Simplified models of climate==
| |
| {{Main|Climate model}}
| |
| | |
| ===Box models===
| |
| | |
| '''Box models''' are simplified versions of complex systems, reducing them to boxes (or reservoirs) linked by fluxes. The boxes are assumed to be mixed homogeneously. Within a given box, the concentration of any [[chemical species]] is therefore uniform. However, the abundance of a species within a given box may vary as a function of time due to the input to (or loss from) the box or due to the production, consumption or decay of this species within the box.
| |
| | |
| Simple box models, i.e. box model with a small number of boxes whose properties (e.g. their volume) do not change with time, are often useful to derive analytical formulas describing the dynamics and steady-state abundance of a species. More complex box models are usually solved using numerical techniques.
| |
| | |
| Box models are used extensively to model environmental systems or ecosystems and in studies of [[ocean circulation]] and the [[carbon cycle]].<ref name=Sarmiento1984>{{cite journal
| |
| | author=Sarmiento, J.L.
| |
| | coauthors = Toggweiler, J.R.
| |
| | year = 1984
| |
| | title = A new model for the role of the oceans in determining atmospheric P CO 2
| |
| | journal=Nature
| |
| | volume = 308
| |
| | pages = 621–4
| |
| | doi = 10.1038/308621a0
| |
| | url = http://www.nature.com/nature/journal/v308/n5960/abs/308621a0.html
| |
| | issue=5960
| |
| |bibcode = 1984Natur.308..621S }}</ref>
| |
| | |
| === Zero-dimensional models ===
| |
| | |
| A very simple model of the radiative equilibrium of the Earth is:
| |
| | |
| :<math>(1-a)S \pi r^2 = 4 \pi r^2 \epsilon \sigma T^4</math>
| |
| | |
| where
| |
| | |
| * the left hand side represents the incoming energy from the Sun
| |
| * the right hand side represents the outgoing energy from the Earth, calculated from the [[Stefan–Boltzmann law]] assuming a constant radiative temperature, ''T'', that is to be found,
| |
| | |
| and
| |
| | |
| * ''S'' is the [[solar constant]] – the incoming solar radiation per unit area—about 1367 W·m<sup>−2</sup>
| |
| * ''<math>a</math>'' is the Earth's average [[albedo]], measured to be 0.3.<ref>{{cite journal |last=Goode |first=P. R. |coauthors=''et al.'' |year=2001 |title=Earthshine Observations of the Earth’s Reflectance |journal=Geophys. Res. Lett. |volume=28 |issue=9 |pages=1671–4 |doi=10.1029/2000GL012580 |bibcode=2001GeoRL..28.1671G}}</ref><ref>{{cite web |title=Scientists Watch Dark Side of the Moon to Monitor Earth's Climate |url=http://www.agu.org/sci_soc/prrl/prrl0113.html |work=American Geophysical Union | date=17 April 2001 }}</ref>
| |
| * ''r'' is Earth's radius—approximately 6.371×10<sup>6</sup >m
| |
| * ''[[pi|π]]'' is the mathematical constant (3.141...)
| |
| * ''<math> \sigma </math>'' is the [[Stefan-Boltzmann constant]]—approximately 5.67×10<sup>−8</sup> J·K<sup>−4</sup>·m<sup>−2</sup>·s<sup>−1</sup>
| |
| * ''<math> \epsilon </math>'' is the effective [[emissivity]] of earth, about 0.612
| |
| | |
| The constant ''πr''<sup>2</sup> can be factored out, giving
| |
| | |
| :<math>(1-a)S = 4 \epsilon \sigma T^4</math>
| |
| | |
| Solving for the temperature,
| |
| :<math>T = \sqrt[4]{ \frac{(1-a)S}{4 \epsilon \sigma}}</math>
| |
| | |
| This yields an average earth temperature of {{convert|288|K|abbr=on|lk=on}}.<ref>[http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf NASA, Goddard Space Flight Center, Langley Research Center: Clouds and the Earth's Radiant Energy System (CERES)].Retrieved 1 Oct 2012.</ref> This is because the above equation represents the effective ''radiative'' temperature of the Earth (including the clouds and atmosphere). The use of effective emissivity and albedo account for the [[greenhouse effect]].
| |
| | |
| This very simple model is quite instructive. For example, it easily determines what the effect on average earth temperature of changes in solar constant or change of albedo or effective earth emissivity would be in the absence of feedback effects. Using the simple formula, the percent change of the average amount of each parameter, considered independently, to cause a one degree Celsius change in steady-state average earth temperature (''i.e.'', the ''[[climate sensitivity]]'') is as follows:
| |
| | |
| * Solar constant 1.4%
| |
| * Albedo 3.3%
| |
| * Effective emissivity 1.4%
| |
| | |
| The average emissivity of the earth is readily estimated from available data. The emissivities of terrestrial surfaces are all in the range of 0.96 to 0.99<ref>[http://www.icess.ucsb.edu/modis/EMIS/html/seawater.html Seawater Samples: Emissivities, ICESS.org]. Retrieved 1 Oct 2012.
| |
| </ref><ref>{{cite journal |doi=10.1175/JCLI3720.1 |author=Jin M, Liang S |title=An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations |journal=J. Climate |volume=19 |issue=12 |pages=2867–81 |date=15 June 2006 |url=
| |
| http://www.glue.umd.edu/~sliang/papers/Jin2006.emissivity.pdf|bibcode = 2006JCli...19.2867J }}</ref> (except for some small desert areas which may be as low as 0.7). Clouds, however, which cover about half of the earth’s surface, have an average emissivity of about 0.5<ref>{{cite conference |author=T.R. Shippert, S.A. Clough, P.D. Brown, W.L. Smith, R.O. Knuteson, and S.A. Ackerman |title=Spectral Cloud Emissivities from LBLRTM/AERI QME |booktitle=Proceedings of the Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting March 1998 Tucson, Arizona |url=http://www.arm.gov/publications/proceedings/conf08/extended_abs/shippert_tr.pdf }}</ref> (which must be reduced by the fourth power of the ratio of cloud absolute temperature to average earth absolute temperature) and an average cloud temperature of about {{convert|258|K|abbr=on}}.<ref>{{cite conference |author=A.G. Gorelik, V. Sterljadkin, E. Kadygrov, and A. Koldaev |title=Microwave and IR Radiometry for Estimation of Atmospheric Radiation Balance and Sea Ice Formation |booktitle=Proceedings of the Eleventh Atmospheric Radiation Measurement (ARM) Science Team Meeting March 2001 Atlanta, Georgia |url=http://www.arm.gov/publications/proceedings/conf11/extended_abs/gorelik_ag.pdf }}</ref> Taking all this properly into account results in an effective earth emissivity of about 0.64 (earth average temperature {{convert|285|K|abbr=on}}).
| |
| | |
| This simple model readily determines the effect of changes in solar output or change of earth albedo or effective earth emissivity on average earth temperature. It says nothing, however about what might cause these things to change, and does not incorporate feedback effects. Zero-dimensional models do not address the temperature distribution on the earth or the factors that move energy about the earth.
| |
| | |
| === Radiative-convective models ===
| |
| | |
| The zero-dimensional model above, using the solar constant and given average earth temperature, determines the effective earth emissivity of long wave radiation emitted to space. This can be refined in the vertical to a zero-dimensional radiative-convective model, which considers two processes of energy transport:
| |
| | |
| * upwelling and downwelling radiative transfer through atmospheric layers that both absorb and emit infrared radiation
| |
| * upward transport of heat by convection (especially important in the lower [[troposphere]]).
| |
| | |
| The radiative-convective models have advantages over the simple model: they can determine the effects of varying [[greenhouse gas]] concentrations on effective emissivity and therefore the surface temperature. But added parameters are needed to determine local emissivity and albedo and address the factors that move energy about the earth.
| |
| | |
| Links:
| |
| * "Effect of Ice-Albedo Feedback on Global Sensitivity in a One-Dimensional Radiative-Convective Climate Model"<ref name=WangSstone1980>{{Cite doi|10.1175.2F1520-0469.281980.29037.3C0545:EOIAFO.3E2.0.CO.3B2}}
| |
| </ref>
| |
| * http://www.grida.no/climate/ipcc_tar/wg1/258.htm
| |
| | |
| === Higher-dimensional models ===
| |
| | |
| The zero-dimensional model may be expanded to consider the energy transported horizontally in the atmosphere. This kind of model may well be [[Zonal and meridional|zonally]] averaged. This model has the advantage of allowing a rational dependence of local albedo and emissivity on temperature – the poles can be allowed to be icy and the equator warm – but the lack of true dynamics means that horizontal transports have to be specified.
| |
| * http://www.shodor.org/master/environmental/general/energy/application.html
| |
| | |
| === EMICs (Earth-system models of intermediate complexity) ===
| |
| | |
| Depending on the nature of questions asked and the pertinent time scales, there are, on the one extreme, conceptual, more inductive models, and, on the other extreme, [[general circulation model]]s operating at the highest spatial and temporal resolution currently feasible. Models of intermediate complexity bridge the gap. One example is the Climber-3 model. Its atmosphere is a 2.5-dimensional statistical-dynamical model with 7.5° × 22.5° resolution and time step of 1/2 a day; the ocean is MOM-3 ([[Modular Ocean Model]]) with a 3.75° × 3.75° grid and 24 vertical levels.
| |
| * http://www.pik-potsdam.de/emics/
| |
| | |
| ==Climate modellers==
| |
| | |
| A climate modeller is a person who designs, develops, implements, tests, maintains or exploits climate models. There are three major types of institutions where a climate modeller may be found:
| |
| | |
| * In a national meteorological service. Most national weather services have at least a [[climatology]] section.
| |
| * In a university. Departments that may have climate modellers on staff include atmospheric sciences, meteorology, climatology, or geography, amongst others.
| |
| * In national or international research laboratories specialising in this field, such as the [[National Center for Atmospheric Research]] (NCAR, in [[Boulder, Colorado]], USA), the [[Geophysical Fluid Dynamics Laboratory]] (GFDL, in Princeton, New Jersey), the [[Hadley Centre for Climate Prediction and Research]] (in Exeter, UK), the Max Planck Institute for Meteorology in Hamburg, Germany, or the [[Institut Pierre-Simon Laplace]] (IPSL in Paris, France). The [[World Climate Research Programme]] (WCRP), hosted by the [[World Meteorological Organization]] (WMO), coordinates research activities on climate modeling worldwide.
| |
| | |
| ==See also==
| |
| {{Portal|Global warming|Ecology|Environment|Energy}}
| |
| * [[Atmospheric Model Intercomparison Project]] (AMIP)
| |
| * [[Atmospheric Radiation Measurement]] (ARM) (in the US)
| |
| * [[CCCma]]
| |
| * [[Climateprediction.net]] is a distributed computing project.
| |
| * [[Earth Simulator]]
| |
| * [[EdGCM]]
| |
| * [[GFDL CM2.X]]
| |
| * [[Global Environmental Multiscale Model]]
| |
| * [[HadCM3]]
| |
| * [[Intermediate General Circulation Model]]
| |
| * [[NCAR]]
| |
| * [[Prognostic variable]]
| |
| | |
| ==Climate models on the web==
| |
| * [http://nomads.ncdc.noaa.gov/ National Operational Model Archive and Distribution System] (NOMADS) is a [[NOAA]] Web-services based project providing both real-time and retrospective format independent access to climate and weather model data.
| |
| * [http://dapper.pmel.noaa.gov/dchart/index.html?cid=AAAAHg@@ Dapper/DChart ] – plot and download model data referenced by the Fourth Assessment Report (AR4) of the [[Intergovernmental Panel on Climate Change]].
| |
| * http://www.hadleycentre.gov.uk/research/hadleycentre/models/modeltypes.html – [[Hadley Centre for Climate Prediction and Research]] – general info on their models
| |
| * http://www.ccsm.ucar.edu/ – [[NCAR]]/[[University Corporation for Atmospheric Research|UCAR]] [[Community Climate System Model]] (CCSM)
| |
| * http://www.climateprediction.net – do it yourself climate prediction
| |
| *http://www.giss.nasa.gov/tools/modelE/ – the primary research GCM developed by NASA/GISS (Goddard Institute for Space Studies)
| |
| * http://edgcm.columbia.edu/ – the original NASA/GISS global climate model (GCM) with a user-friendly interface for PCs and Macs
| |
| * http://www.cccma.bc.ec.gc.ca/ – [[CCCma]] model info and interface to retrieve model data
| |
| * http://nomads.gfdl.noaa.gov/CM2.X/ – [[NOAA]] / [[Geophysical Fluid Dynamics Laboratory]] CM2 global climate model info and model output data files
| |
| * http://www.climate.uvic.ca/ – [[University of Victoria]] Global climate model, free for download. Leading researcher was a contributing author to the recent [[Intergovernmental Panel on Climate Change|IPCC]] report on climate change.
| |
| | |
| {{Global warming}}
| |
| | |
| {{Atmospheric, Oceanographic and Climate Models}}
| |
| {{Computer modeling}}
| |
| | |
| ==Notes==
| |
| | |
| {{Reflist|2}}
| |
| | |
| ==References==
| |
| | |
| <!-- Note:
| |
| * please add new entries in alphabetical order of author's last name.
| |
| * These are the 'general references' to the source; please do not incorporate quotes, etc. here.
| |
| * 'citation' works better than 'cite xxx'.
| |
| *Some of these references are used as part of the [[Template:Harvnb]] template. Removing these references will break some of the citations in the article.
| |
| -->
| |
| | |
| * {{Citation
| |
| | year = 2007
| |
| | author = IPCC AR4 SYR
| |
| | author-link = IPCC
| |
| | title = Climate Change 2007: Synthesis Report (SYR)
| |
| | series = Contribution of Working Groups I, II and III to the [[IPCC Fourth Assessment Report|Fourth Assessment Report]] (AR4) of the Intergovernmental Panel on Climate Change
| |
| | editor = Core Writing Team; Pachauri, R.K; and Reisinger, A.
| |
| | publisher = IPCC
| |
| | location=[[Geneva]], Switzerland
| |
| | url = http://www.ipcc.ch/publications_and_data/ar4/syr/en/contents.html
| |
| | isbn = 92-9169-122-4
| |
| }}.
| |
| | |
| ==Further reading==
| |
| * {{cite book |author= Ian Roulstone and John Norbury |title=Invisible in the Storm: the role of mathematics in understanding weather |url=http://books.google.co.uk/books/about/Invisible_in_the_Storm.html?id=qnMrFEHMrWwC&redir_esc=y|year=2013 |publisher=Princeton University Press}}
| |
| | |
| ==External links==
| |
| * [http://www.iop.org/activity/policy/Publications/file_4147.pdf Climate Change Prediction: A challenging scientific problem (2005)]. By Prof. A.J. Thorpe. Explains how predictions of future climate change are made using climate models.
| |
| * [http://stephenschneider.stanford.edu/Publications/PDF_Papers/SunHansenJOC.pdf Climate Simulations for 1951–2050 with a Coupled Atmosphere–Ocean Model] by Sun and Hansen (2003)
| |
| * [http://www.aip.org/history/climate/GCM.htm History of Global Climate Modelling]
| |
| *[http://www.gfdl.noaa.gov/e-media-gfdl-ccvp-group-main E-Media from GFDL's CCVP Group]. Includes videos, animations, podcasts and transcripts on climate models.
| |
| *[http://www.gfdl.noaa.gov/~fms] GFDL's Flexible Modeling System containing code for the climate models.
| |
| * [http://dapper.pmel.noaa.gov/dchart/index.html?cid=AAAAHg@@ Dapper/DChart ] – plot and download model data referenced by the Fourth Assessment Report (AR4) of the [[Intergovernmental Panel on Climate Change]].
| |
| * [http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf Chapter 8: Climate Models and Their Evaluation]. The IPCC Working Group I [[IPCC Fourth Assessment Report|Fourth Assessment Report]] (2007).
| |
| * [http://www.climatescience.gov/Library/sap/sap3-1/final-report/default.htm CCSP, 2008: Climate Models: An Assessment of Strengths and Limitations] A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Bader D.C., C. Covey, W.J. Gutowski Jr., I.M. Held, K.E. Kunkel, R.L. Miller, R.T. Tokmakian and M.H. Zhang (Authors)]. Department of Energy, Office of Biological and Environmental Research, Washington, D.C., USA, 124 pp.
| |
| *[http://news.bbc.co.uk/1/hi/sci/tech/6320515.stm BBC News: Models 'key to climate forecasts']. Dr Vicky Pope of the Hadley Centre explains how computer models are used to predict the day-to-day weather and changes to the climate (2007).
| |
| *[http://www.youtube.com/watch?v=klgKyVotW7E The scientific basis for projections of climate change (in a nutshell)]. Video of a lecture given at [[Princeton University]] by Isaac Held, Professor of Geosciences and Atmospheric and Oceanic Sciences, Princeton University and Geophysical Fluid Dynamics Laboratory (GFDL). 26 February 2008.
| |
| * [http://www.grida.no/climate/ipcc_tar/wg1/313.htm (IPCC 2001 section 8.3)] – on model hierarchy
| |
| * [http://www.grida.no/climate/ipcc_tar/wg1/308.htm (IPCC 2001 section 8)] – much information on coupled GCM's
| |
| * [http://www-pcmdi.llnl.gov/projects/modeldoc/cmip/index.html Coupled Model Intercomparison Project]
| |
| * [http://ams.allenpress.com/amsonline/?request=get-abstract&doi=10.1175%2F2786.1 On the Radiative and Dynamical Feedbacks over the Equatorial Pacific Cold Tongue]
| |
| * [http://www.aip.org/history/climate/Radmath.htm Basic Radiation Calculations] – The Discovery of Global Warming
| |
| *{{Cite book |author=Henderson-Sellers, A.; Robinson, P. J. |title=Contemporary Climatology |publisher=Longman |location=New York |year=1999 |isbn=0-582-27631-4 |url=http://www.pearsoned.co.uk/Bookshop/detail.asp?item=100000000002249}}
| |
| | |
| <br />
| |
| {{Atmospheric, Oceanographic and Climate Models}}
| |
| {{global warming}}
| |
| {{physical oceanography|expanded=none}}
| |
| | |
| {{DEFAULTSORT:Global Climate Model}}
| |
| [[Category:Climate change modelling]]
| |
| [[Category:Numerical climate and weather models]]
| |
| [[Category:Climate forcing]]
| |
| [[Category:Computational science]]
| |
| [[Category:Global warming]]
| |
| | |
| [[pl:Globalny model klimatu]]
| |