A Short Summary Of Why Skillful Climate Prediction Is Much More Difficult Than Skillful Weather Prediction

Climate Science has already weblogged on the claim in the 2007 IPCC WG1 report that,

““Projecting changes in climate due to changes in greenhouse gases 50 years from now is a very different and much more easily solved problem than forecasting weather patterns just weeks from now. To put it another way, long-term variations brought about by changes in the composition of the atmosphere are much more predictable than individual weather events.â€? [from page 105]

This weblog provides a short summary of why such a claim is absurd.

First, all climate and weather models include two components; a dynamic core (which involves advection, the pressure gradient force, and the gravitational acceleration) and parameterized or prescribed) physical, chemical and biological processes. Only the dynamic core is basic physics. All parameterizations are engineering code which means they include tunable components.

Weather prediction models parameterize long- and short-wave radiative flux divergence, stable clouds and precipitation, deep cumulus clouds, turbulence, and air-sea and air-land fluxes. The state variables in weather model are the three components of velocity, temperature, pressure, density of air, and the three phases of water (and sometimes other gaseous and aerosol components). A detailed discussion of this type of model is given, for example, in

Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp. [Table of Contents]

The state variables are initialized from real world observations such as from radiosonde and satellite data. If the weather model is a regional model, it obtains information through lateral boundary conditions. The dynamic core of the weather model, therefore, is constrained by the real-world initial conditions and lateral boundary conditions. Most of the surface boundary conditions are prescribed. This includes, for instance, sea surface temperature, sea ice coverage, vegetation, and snow cover. Only certain quantities, such as soil moisture and land surface temperature may be permitted to change in response to the land-air fluxes. When the initial conditions of the weather model are “forgotten”, the parameterizations must skillfully predict the evolution of the state variables from that time forward, which is the reason that the weather prediction accuracy degrades and becomes of no value after a certain time period (e.g. see).

A climate model, in contrast, must model more processes than in a weather model (such as biogeochemistry of vegetation on land and plants in the ocean; sea ice dynamics; aerosol processes; ocean circulation; ground freezing and thawing; snow accumulation and melt and sublimation, etc. – see). For some of these climate processes (which involve physics, biology and chemistry) they are modeled, as with a weather model, by a dynamical core and by parameterizations. These include sea ice dynamics and ocean circulation, which both have advection, pressure gradient and gravitational parts, as well as the parameterization of other effects (such as turbulence, phase changes of water). Some of the climate processes, such as biogeochemistry and biogeography have no dynamical core, and are completely parameterized models.

Thus, a climate model involves more parameterizations with their tunable components than for a weather model, as well as additional new state variables (such as salinity, ice, snow, vegetation type and its root depth etc) for which initial conditions are required for all of these variables.

The climate model also has no real world constraint such as supplied by real-world initial conditions (and for a regional model lateral boundary conditions). This real-world data constrains its predictions. Instead, the state variables required for the dynamic core of each component of the climate model (i.e. the state variables for the atmosphere, land, ocean and continental ice) must be generated from the parameterizations!

The claim by the IPCC that an imposed climate forcing (such as added atmospheric concentrations of CO2) can work through the parameterizations involved in the atmospheric, land, ocean and continental ice sheet components of the climate model to create skillful global and regional forecasts decades from now is a remarkable statement. That the IPCC states that this is a “much more easily solved problem than forecasting weather patterns just weeks from now” is clearly a ridiculous scientific claim. As compared with a weather model, with a multi-decadal climate model prediction there are more state variables, more parameterizations, and a lack of constraint from real-world observed values of the state variables.

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