New meta-study sheds doubt on reliability of climate models
Even though there is an overwhelming scientific consensus on the fact that humans are responsible for climate change, there remains controversy and doubt over the reliability of climate models used to forecast future changes. A new study comparing the composite output of 22 leading global climate models with actual climate data finds that the models do an unsatisfactory job of mimicking climate change in key portions of the atmosphere.
The 22 climate models used in the study are the same models used by the UN Intergovernmental Panel on Climate Change (see the IPCC's own evaluation of climate models, in Chapter 8 of the Working Group I Report, 'The Physical Science Basis'). The usual discussion is whether the climate model forecasts of Earth's climate 100 years or so into the future are realistic, says lead author Dr. David H. Douglass from the University of Rochester. But the new study asks a more fundamental question: can these same models accurately explain the climate from the recent past? It seems that the answer is no.
Scientists from Rochester, the University of Alabama in Huntsville (UAH) and the University of Virginia who publish their findings in the Royal Meteorological Society's International Journal of Climatology, compared the climate change forecasts from the 22 most widely-cited global circulation models with tropical temperature data collected by surface, satellite and balloon sensors. The models predicted that the lower atmosphere should warm significantly more than it actually did.
Models are very consistent in forecasting a significant difference between climate trends at the surface and in the troposphere, the layer of atmosphere between the surface and the stratosphere, says Dr. John Christy, director of UAH's Earth System Science Center. The models forecast that the troposphere should be warming more than the surface and that this trend should be especially pronounced in the tropics.
But when the researchers looked at actual climate data, however, they did not see accelerated warming in the tropical troposphere. Instead, the lower and middle atmosphere was warming the same or less than the surface. In layers near 5 km, the modelled trend was 100 to 300% higher than observed, and, above 8 km, modelled and observed trends even have opposite signs. For those layers of the atmosphere, the warming trend they observed in the tropics is typically less than half of what the models forecast, shedding serious doubt on the reliability of the models.
The atmospheric temperature data were obtained from two versions of data collected by sensors aboard NOAA satellites since late 1979, plus several sets of temperature data gathered twice a day at dozens of points in the tropics by thermometers carried into the atmosphere by helium balloons. The surface data were from three datasets:
energy :: sustainability :: biomass :: bioenergy :: climate change :: global warming :: global climate model :: forecast :: IPCC ::
After years of rigorous analysis and testing, the high degree of agreement between the various atmospheric data sets gives an equally high level of confidence in the basic accuracy of the climate data.
The last 25 years constitute a period of more complete and accurate observations, and more realistic modeling efforts, says Dr. Fred Singer from the University of Virginia. Nonetheless, the models are seen to disagree with the observations. The researchers suggest, therefore, that projections of future climate based on these models should be viewed with much caution.
Contradictions
The findings of this study contrast strongly with those of a recent analysis that used 19 of the same climate models and similar climate datasets. That study concluded that any difference between model forecasts and atmospheric climate data is probably due to errors in the data.
The question was, what would the models 'forecast' for upper air climate change over the past 25 years and how would that forecast compare to reality? To answer that, the scientists needed climate model results that matched the actual surface temperature changes during that same time. If the models got the surface trend right but the tropospheric trend wrong, then they could pinpoint a potential problem in the models.
As it turned out, the average of all of the climate models forecasts came out almost like the actual surface trend in the tropics. That meant the researchers could do a very robust test of their reproduction of the lower atmosphere.
Instead of averaging the model forecasts to get a result whose surface trends match reality, the earlier study looked at the widely scattered range of results from all of the model runs combined. Many of the models had surface trends that were quite different from the actual trend, Christy says. Nonetheless, that study concluded that since both the surface and upper atmosphere trends were somewhere in that broad range of model results, any disagreement between the climate data and the models was probably due to faulty data.
The researchers think their new experiment is more robust and provides more meaningful results.
Illustration: projections of temperature and precipitation changes in Africa due to climate change, indicating the number of climate models used. Credit: IPCC, Fourth Assessment Report, The Physical Science Basis, Chapter 9.
References:
David H. Douglass, John R. Christy, Benjamin D. Pearson, S. Fred Singer, "A comparison of tropical temperature trends with model predictions (p n/a)", International Journal of Climatology, Dec 5 2007, DOI: 10.1002/joc.1651
Eurekalert: New study increases concerns about climate model reliability - December 11, 2007.
Article continues
The 22 climate models used in the study are the same models used by the UN Intergovernmental Panel on Climate Change (see the IPCC's own evaluation of climate models, in Chapter 8 of the Working Group I Report, 'The Physical Science Basis'). The usual discussion is whether the climate model forecasts of Earth's climate 100 years or so into the future are realistic, says lead author Dr. David H. Douglass from the University of Rochester. But the new study asks a more fundamental question: can these same models accurately explain the climate from the recent past? It seems that the answer is no.
Scientists from Rochester, the University of Alabama in Huntsville (UAH) and the University of Virginia who publish their findings in the Royal Meteorological Society's International Journal of Climatology, compared the climate change forecasts from the 22 most widely-cited global circulation models with tropical temperature data collected by surface, satellite and balloon sensors. The models predicted that the lower atmosphere should warm significantly more than it actually did.
Models are very consistent in forecasting a significant difference between climate trends at the surface and in the troposphere, the layer of atmosphere between the surface and the stratosphere, says Dr. John Christy, director of UAH's Earth System Science Center. The models forecast that the troposphere should be warming more than the surface and that this trend should be especially pronounced in the tropics.
But when the researchers looked at actual climate data, however, they did not see accelerated warming in the tropical troposphere. Instead, the lower and middle atmosphere was warming the same or less than the surface. In layers near 5 km, the modelled trend was 100 to 300% higher than observed, and, above 8 km, modelled and observed trends even have opposite signs. For those layers of the atmosphere, the warming trend they observed in the tropics is typically less than half of what the models forecast, shedding serious doubt on the reliability of the models.
The atmospheric temperature data were obtained from two versions of data collected by sensors aboard NOAA satellites since late 1979, plus several sets of temperature data gathered twice a day at dozens of points in the tropics by thermometers carried into the atmosphere by helium balloons. The surface data were from three datasets:
energy :: sustainability :: biomass :: bioenergy :: climate change :: global warming :: global climate model :: forecast :: IPCC ::
After years of rigorous analysis and testing, the high degree of agreement between the various atmospheric data sets gives an equally high level of confidence in the basic accuracy of the climate data.
The last 25 years constitute a period of more complete and accurate observations, and more realistic modeling efforts, says Dr. Fred Singer from the University of Virginia. Nonetheless, the models are seen to disagree with the observations. The researchers suggest, therefore, that projections of future climate based on these models should be viewed with much caution.
Contradictions
The findings of this study contrast strongly with those of a recent analysis that used 19 of the same climate models and similar climate datasets. That study concluded that any difference between model forecasts and atmospheric climate data is probably due to errors in the data.
The question was, what would the models 'forecast' for upper air climate change over the past 25 years and how would that forecast compare to reality? To answer that, the scientists needed climate model results that matched the actual surface temperature changes during that same time. If the models got the surface trend right but the tropospheric trend wrong, then they could pinpoint a potential problem in the models.
As it turned out, the average of all of the climate models forecasts came out almost like the actual surface trend in the tropics. That meant the researchers could do a very robust test of their reproduction of the lower atmosphere.
Instead of averaging the model forecasts to get a result whose surface trends match reality, the earlier study looked at the widely scattered range of results from all of the model runs combined. Many of the models had surface trends that were quite different from the actual trend, Christy says. Nonetheless, that study concluded that since both the surface and upper atmosphere trends were somewhere in that broad range of model results, any disagreement between the climate data and the models was probably due to faulty data.
The researchers think their new experiment is more robust and provides more meaningful results.
Illustration: projections of temperature and precipitation changes in Africa due to climate change, indicating the number of climate models used. Credit: IPCC, Fourth Assessment Report, The Physical Science Basis, Chapter 9.
References:
David H. Douglass, John R. Christy, Benjamin D. Pearson, S. Fred Singer, "A comparison of tropical temperature trends with model predictions (p n/a)", International Journal of Climatology, Dec 5 2007, DOI: 10.1002/joc.1651
Eurekalert: New study increases concerns about climate model reliability - December 11, 2007.
Article continues
Tuesday, December 11, 2007
Site-specific nutrient management sees increases in rice yields
The findings can be immediately linked to the situation in Africa, where the revolution still has to begin and where rice production is increasing gradually (for recent examples of what the most basic of interventions, the application of fertilizer, can achieve, see the story about Malawi's super-harvest and its transformation from being a begging bowl to becoming a net food exporter supplying the World Food Program, here; and research results from trials amongst smallholders in Zimbabwe, here). The continent holds by far the largest potential for the production of sustainable bioenergy (that is, producing energy after meeting all food, fiber, fodder and forest product needs of growing populations and without deforestation). If African farmers adopt basic 'Green Revolution' agricultural methods, the continent's exportable bioenergy potential is estimated to be around 350 Exajoules of energy by 2050, or roughly 2.5 times the energy contained in the world's total current oil consumption.
When this transition to modern agriculture is made - and last year, 500 scientists called for the switch at the African Fertilizer Summit - Africa too can refine its nutrient management strategies and traverse to precision farming, as is being done in the Punjab trials. Later, more technology-intensive challenges can be addressed, such as improving the photosynthetic efficiency of plants, developing dedicated energy crops with specific bioconversion characteristics, or managing large integrated carbon negative bioenergy systems relying on biochar or geosequestration.
But let us look at the achievements in the Punjab province. Nowadays the region accounts for 10 percent of the Indian rice production and is currently witnessing a slower rice grain yield growth rate as compared to the spectacular yield growth rate witnessed during the Green Revolution phase (1960-1986). To meet the expected food demand in the next 30 years, rough estimates for India suggest the need to increase the average farm productivity of the system, which is currently at 45 to 60% of the attainable yield potential, to 70 to 80% of the attainable potential.
The researchers hypothesized that decreased nutrient supply capacity of soil and improper nutrient management approaches were key factors in the slower growth rate. By analyzing the existing soil nutrient composition and applying site-specific nutrient management (SSNM) the scientists were able to increase average rice grain yields by 17 percent compared with current farmers' fertilizer practice. Similarly, profits rose about 14 percent using SSNM.
Over a two year period the scientists applied calculated amounts of nutrients at 56 sites in six key irrigated rice-wheat regions to evaluate the effectiveness of SSNM in increasing yield growth rates. Using the 'Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS)' model, which predicts crop yields from chemical soil characteristics, the scientists refined their nutrient applications and schedules on a site-specific basis:
energy :: sustainability :: biomass :: bioenergy :: biofuels :: food :: fertilizer :: rice :: agronomy :: Punjab ::
In addition to yield and profit increases, improved timing of fertilizer applications led to a measured 13 to 15 percent increases in plant accumulations of nitrogen, phosphorous and potassium.
While further yield increases are likely to occur in small, incremental steps that involve gradual buildup of soil fertility and fine-tuning of crop management, the authors conclude that the agronomic and economic successes of SSNM are due to its site-specific and dynamic nature which take soil variability into account. They suggest that the major challenges for SSNM will be to reduce the complexity of the technology as it is disseminated to farmers and to combat environmental pollution stemming from nutrient leaching and runoff from rice fields.
The International Rice Research Institute (IRRI) is the oldest and largest international agricultural research institute in Asia. It is an autonomous, nonprofit rice research and training organization with staff based in 14 countries in Asia and Africa. It played a key role in the Green Revolution.
References:
Harmandeep S. Khuranaa, Steven B. Phillips, Bijay-Singh, Achim Dobermann, Ajmer S. Sidhu, Yadvinder-Singh and Shaobing Peng, "Performance of Site-Specific Nutrient Management for Irrigated, Transplanted Rice in Northwest India", Agron J 99:1436-1447 (2007), DOI: 10.2134/agronj2006.0283.
American Society of Agronomy: Site-specific nutrient management sees increases in rice yields - December 10, 2007.
Biopact: Malawi's super harvest proves biofuel critics wrong - or, how to beat hunger and produce more oil than OPEC - December 04, 2007
Biopact: Fertilizers boost crop production amongst smallholders in Zimbabwe - April 13, 2007
Article continues
posted by Biopact team at 11:30 PM 0 comments links to this post