2-3: Climate Model Results - Equilibrium Models

Eugene S. Takle
© 1997

We continue our comparison of global climate models with each other and with observed values. The first table shows comparison of temperature (top) and precipitation (bottom) for different global climate models in different regions of the globe. Two sets of observations are listed on this table to show that when averaging over large regions, particularly where actual measurements are sparse, there is some lack of agreement. The five regions represent the US Great Plains, Southeast Asia, the Sahel (Africa just below the Sahara Desert), Southern Europe, and Australia. For each region and each variable, I have underlined in yellow the entry closest to the observations. Notice again that no one model seems to uniformly excel in representing temperature and precipitation. And also, in some cases the low-resolution models give better results than high-resolution models. Comparison of model results with observations for various regions. Adapted from Table 4.2 IPCC, 1990.

The accompanying plot shows how one model, in this case the Goddard model, reproduces the global pattern of surface temperature difference between July and January. Observations are given in the upper panel and model results in the lower panel. Being a plot of difference, this map does not indicate how well the model simulates absolute temperature but rather the seasonal change from summer to winter. I have colored regions in the Northern Hemisphere with seasonal temperature differences larger than 25^oC in green and regions larger than 40^oC in red in both plots. The model evidently produces quite good seasonal temperature shifts as indicated by agreement between model and observations over large areas in North America and Russia. The next graph gives a similar plot for a different model (the NCAR model), with the model results in the upper panel and observations below. The results for this model are similar to those of the GISS model. Surface air temperature change, July.
Computed July minus January temperature. Adapted from Scheider, 1989, Science.

The accompanying two-panel map gives the model results and observations for DJF precipitation. This is from a fairly coarse resolution model (notice, for example, that the US does not include the Florida peninsula). If comparison is made for an isolated point on the map, the differences between model and observations will likely be large. However, the overall patterns are quite good and suggests that the model captures key large-scale features of global precipitation even if local values have large errors. Precipitation in northern winter (DJF). Adapted from Wilson and Mitchell, 1987, JGR.

The climate system, as has been discussed earlier, includes the atmosphere, ocean, land surface, biosphere, and ice masses. Models of the climate system must in some way consider the influences of each of these components. For instance, surface winds drive ocean currents and create waves that promote mixing in the upper layer of the ocean. This horizontal movement and vertical mixing of ocean water transports heat poleward from tropical and subtropical regions and downward from the surface. The heat transported poleward is given up to the atmosphere at higher latitudes. Failure to include ocean heat transport would create serious deficiencies in atmospheric global climate models.

Ocean circulation modeling has proceeded in parallel with atmospheric modeling and is based on the same set of basic equations as the atmosphere (except the equation of state is different). The atmosphere exchanges heat, moisture, and trace gases (notably CO₂ ) with the ocean, with the two fluid systems operating on different time scales.

Despite the difficult challenges posed by bringing together two complex numerical models, climate scientists have successfully coupled atmospheric and ocean circulation models. These ocean models may be fairly simple (simple heat diffusion poleward) or complex and use the full equations of motion as for the atmosphere.

A summary of the validation of climate models is given in the accompanying link, taken from the 1992 report of the Intergovernmental Panel on Climate Change and personal experience. According to Oreskes et al (Science 263, 641-645), climate models are so large and describe so many different processes that it is impossible to independently test and certify that all components of the models are correctly representing physical processes. However, present and past (paleoclimates, to be discussed later) do offer several opportunities to validate model performance. From these comparisons, we have the following conclusions: Validation of global climate models

Models show considerable skill in reproducing large-scale maps of surface pressure, temperature, wind, and precipitation in both summer and winter. On regional scales (sub-continental) all models show significant departures from observations for both temperature and precipitation. Soil moisture comparisons are limited by lack of data, but results, where data are available are in qualitative agreement. Snow cover is reasonably well simulated, except where temperatures are too high (e.g., at high latitudes in the Northern Hemisphere). Radiative fluxes at the top of the atmosphere are simulated well in some models. Daily and interannual variability is mixed, with most models giving good results in some locations but not good in others. Model response is good for slow changes in forcing such as El Nino, Mount Pinatubo, wet and dry periods in the Sahel, and select periods from the last 18,000 years. Ocean models reproduce large-scale features fairly well. Coupled atmosphere-ocean models do reasonably well in simulations of the last ice age.

It can be concluded that models have enough skill in simulating known climate features that they can be useful tools in trying to estimate the climatic impacts that are likely to occur from the changes in atmospheric chemistry that we discussed earlier in the semester. Recall that we concluded the section on radiative forcing with four scenarios of possible radiative forcing. There are two possible ways the global models can be used to estimate future climatic impacts. The most accurate procedure is to simulate the climate, day-by-day, year-by-year with the changes in radiative forcing previously discussed from the time of the Industrial Revolution until the year 2100 or other point in the future. An experiment of this magnitude is well beyond the capacity of present computers and computers envisioned in the next several years. A limited version of this experiment is to start the simulation at some time in the more recent past, such as about 1950 when the rise in anthropogenic greenhouse gases began. Experiments of this scope, called transient climate simulations, are just now being performed and some results will be presented.

An alternative way to estimate effects of future enhanced greenhouse gases is to simulate a shorter period (perhaps 20 to 30 years) with a constant amount of greenhouse gas (usually the equivalent of about 600 ppmv of carbon dioxide, which is approximately twice the pre-Industrial Revolution level). Concentrations of this magnitude are expected to exist in about the year 2050. These simulations, called simulations of an equilibrium climate, are much shorter to run on supercomputers and have been done for the last 15 years by several research groups. The factor not accurately represented in equilibrium models difference in time scales of components of the climate system. Oceans, soil moisture, and ice masses are much slower to respond than the atmosphere, and the real climate system is not expected to ever achieve an equilibrium state.

Some examples of results from equilibrium models of a doubled CO₂ climate will now be described. The accompanying table gives results for coupled models that use a "mixed layer" (shallow ocean of constant temperature and salinity in the vertical direction). The right-hand-most column gives the global mean temperature change and precipitation change projected by the model for a doubling of atmospheric CO₂ . Note that the temperatures projected for a doubling of CO₂ are all positive (warming rather than cooling) and range from 1.9^o C to 5.2^oC with a mean value of 3.7^oC. Similarly, all models report precipitation increases ranging from 3 to 15% with a mean of 8.7%. The next table gives some of the most recent results from longer runs (see length of run given in right-hand column). Changes in temperature and precipitation from a CO₂ doubling are comparable to the previous table. Summary of results from global mixed-layer ocean-atmosphere models used in 2 x CO₂ experiments. Adapted from Table 3.2, IPCC 1990.
Summary of results from new equilibrium simulations for double CO₂ with atmospheric GCMs with a seasonal cycle and a mixed-layer ocean. IPCC 1990 Table B2.

Maps showing the global distribution of changes in temperature, precipitation, and soil moisture for a doubling of atmospheric CO₂ are given in the following series of colored charts. These are from high-resolution models from the Geophysical Fluid Dynamics Laboratory and the United Kingdom meteorological Office, respectively.

The most notable features in the first graphs for December-January-February (DJF) temperature change are the dominance of warming at high latitudes, particularly in the Northern Hemisphere. Both models agree that the Northern Hemisphere polar region warms substantially (more than 8^oC over wide areas) but the exact boundaries of the areas are different. Also, the GFHI simulation shows more intense warming at the North Pole and less difference in warming between land and water areas at high latitudes. The models disagree on the fate of warming over Antarctica. This is not surprising in view of the wide model disagreement over the pressure and circulation patterns over this part of the globe, as previously discussed. Both models report only weak warming in tropical areas. Change in surface air temperature for DJF: GFHI and UKHI models. 1992 IPCC Supplement, Figure 5.4

Simulations of warming due to doubling of CO₂ for June-July-August (JJA) are given in the next plots. Compared to the previous graph, the GFHI simulation shifts the maximum warming to the Antarctic ice margin in keeping with the shift to winter in Southern Hemisphere. The UKHI model calculates less warming, although the site of maximum warming is similar to that of the GFHI map. Tropical regions again show little warming. Change in surface air temperature for JJA: GFHI and UKHI models. 1992 IPCC Supplement, (Figure 5.4 continued).

Patterns of changes in precipitation over the globe show much less spatial coherence, as shown in the accompanying map for DJF for the same two models. Reasonable agreement is shown between models in Africa and mid and northern latitudes in the Northern Hemisphere. This agreement may be misleading, however, because comparison with a third model (Canadian Climate Centre model, not shown) does not confirm the large-scale patterns shown in the accompanying graphs. Maps for JJA (accompanying graphs) also have some areas of agreement and large areas of disagreement.
Change in precipitation for DJF: GFHI and UKHI models. 1992 IPCC Supplement, Figure 5.6 Change in precipitation for JJA: GFHI and UKHI models. 1992 IPCC Supplement, (Figure 5.6 continued).

Soil moisture is a climate parameter of considerable scientific and practical (agricultural) interest that is not routinely measured over large areas of the earth. This impedes attempts to make comparisons of calculated values with actual measurements. The next graphs show soil DJF soil moisture from the same two models. The tendency for agreement of the two models on DJF precipitation in the mid and high latitudes of the Northern Hemisphere leads to a tendency for agreement on soil moisture as well. Values for JJA show no large of consistent agreement.

Change in soil moisture for DJF: GFHI and UKHI models. 1992 IPCC Supplement, Figure 5.8 Change in soil moisture for JJA: GFHI and UKHI models. 1992 IPCC Supplement, (Figure 5.8 continued).

The following series of images gives examples of what several global climate models project for the US under an equilibrium doubled CO₂ . In each case we will also examine the model validation as compared with the present climate. The models used in this comparison are the Oregon State University (OSU) 2-level model, the NASA GISS low-resolution model, and the GFDL model. The comparison will consist of a series of 4-panel displays of regional patterns of temperature and precipitation over North America. In each 4-panel display, the panel in the upper left corner will be the observed values that serve as the reference pattern for comparison with model results.

The accompanying figure gives the comparison of model validations on mean January temperatures. The maps for each of the three models give the difference in temperature between the model produced value and the observed temperature for that local part of the domain. If a model gave a perfect simulation of the present climate, its map would be a field consisting of all zeros. If the numbers are positive, the model overpredicts the temperature (gives values that are too warm), and negative numbers give a simulated climate that is colder than observed. The GISS model gives values that tend to be positive, suggesting that simulated January temperatures are warmer than observed. Most values are within 2o of observed, although a few are markedly larger. The largest errors tend to be positive and occur at high latitudes. January observed climate vs. GISS, GFDL, and OSU models. Data from Climate Models: The CO₂ Warming. Roy Jenne, NCAR.

The GFDL model is a higher-resolution model that gives a more even balance of positive and negative values but also has large positive numbers in the northeastern part of North America similar to the GISS model. The OSU model also has large positive and negative values but agrees with the other models in overpredicting January temperatures in eastern Canada.

The next figure gives a similar plot of July temperatures. In this case the GISS values are more negative and some of the largest negative values are in Northeastern Canada. The GFDL model is biased too warm but with lower magnitude than for January. The OSU results show large negative values off the east coast of the US and in the mountainous western US. It can be concluded that each model has its own bias that differs from region to region and month to month. These biases must be taken into account if they are to be used for projecting future climates. This can be done by comparing the values for the future climate with the results the model produces for the present climate, rather than the observed values for the present climate. This strategy assumes that if the model is biased in a certain region in a certain month that it may likely be biased similarly in the results for the future climate. Subtracting the model simulations of the present climate (1x CO₂ ) from the 2x CO₂ simulation reduces, if not eliminates, the model bias. July observed climate vs GISS, GFDL, and OSU models. Roy Jenne, NCAR.

The next graph shows the surface-temperature change predicted by the models for January for an equilibrium climate with a doubled CO₂ atmosphere. Since a temperature difference is being plotted, a value of zero indicates the model calculates no change in surface temperature with a doubled CO₂ atmosphere. The GISS model gives very large values of warming for the enhanced greenhouse climate, with generally higher values at high latitudes. The GFDL model produces results that are less severe and more uniform over the domain. The OSU results also are less severe and do not have such large values at high latitudes. A common feature of all models is that they all agree that the result of increased CO₂ is to produce a warming that averages something on the order of about 4^o C. Januray temperature: 2 x CO₂ vs. 1 x CO₂. Roy Jenne, NCAR.

The July calculations given in the next graph are similar to the previous results in that all models produce a pattern of warming. There is less evidence of severe warming at high latitudes in the July results and there is somewhat better agreement among the models in the values produced. The GFDL model tends to produce higher temperature changes than the others. July temperature: 2 x CO₂ vs 1 x CO₂. Roy Jenne, NCAR.

Model capabilities for producing precipitation are revealed in the next two plots. Ratios of calculated to observed values are used for precipitation because of the wide difference in precipitation for different parts of the domain. As for temperature, the values in the upper left panel give the observed values for each grid point. The first 4-panel graph gives comparisons of 1x CO₂ results with the observed climate. Perfect simulations of the present climate would give 1.0 at each point, since ratios are being plotted. Large percentage errors are produces by the models for precipitation. A value of 2.0 indicates the model produces twice the observed precipitation (100% error). Errors of 50 to 100% are common, and much larger values are observed at many grid points. Such results do not instill confidence in global climate model capability for simulating precipitation. The graph for the ratio of 2x CO₂ precipitation to the models' 1x CO₂ values gives values of 1.0 or less at most grid points. Because of the large biases of the previous graph, there is little justification for using these precipitation results in assessing impacts of climate change. Comparison of annual precipitation of 1xCO₂ to climate, Roy Jenne, NCAR.
Ratio of 2xCO₂ to 1x CO₂, Roy Jenne, NCAR.

Transcription by Theresa M. Nichols