This post is going to look at the results of the following paper which is part of the ISI-MIP interdisciplinary analysis. Full paper is available in pdf on the PNAS web site.
Cynthia Rosenzweig PNAS 2013 Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison
PNAS 2013 ; published ahead of print December 16, 2013, doi:10.1073/pnas.1222463110
Significance
Agriculture is arguably the sector most affected by climate change, but assessments differ and are thus difficult to compare. We provide a globally consistent, protocol-based, multimodel climate change assessment for major crops with explicit characterization of uncertainty. Results with multimodel agreement indicate strong negative effects from climate change, especially at higher levels of warming and at low latitudes where developing countries are concentrated. Simulations that consider explicit nitrogen stress result in much more severe impacts from climate change, with implications for adaptation planning.
Abstract
Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
While the impacts of climate change on agricultural productivity have been studied for about 20 years this research was not coordinated in terms of consistency of data and was modeled on a variety of platforms using different methods of analysis. This situation makes it difficult to draw conclusions about the various results and to meld them into a consistent result. This situation is very similar to many of the weaknesses found in the results of the IPCC AR analysis over the years that many complain about. This papers effort is to coordinate this research by working all of the various models with the same set of data and comparing that to AR5.
The magnitude, rate, and pattern of climate change impacts on agricultural productivity have been studied for approximately two decades. To evaluate these impacts, researchers use bio- physical process-based models ..., agro-ecosystem models ..., and statistical analyses of historical data ...... Although these and other methods have been widely used to forecast potential impacts of climate change on future agricultural productivity, the protocols used in previous assessments have varied to such an extent that they constrain cross- study syntheses and limit the ability to devise relevant adaptation options (9, 10). In this project we have brought together seven global gridded crop models (GGCMs) for a coordinated set of simulations of global crop yields under evolving climate conditions. ....In order to facilitate analyses across models and sectors, all global models are driven with consistent bias- corrected climate forcings derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive (13). The objectives are to (i) establish the range of uncertainties of climate change impacts on crop productivity worldwide,(ii)determine key differences in current approaches used by crop modeling groups in global analyses, and (iii) propose improvements inGGCMs and in the methodologies for future intercomparisons to produce more reliable assessments.
We examine the basic patterns of response to climate across crops, latitudes, time periods, regional temperatures, and atmo- spheric carbon dioxide concentrations [CO2]. In anticipation of the wider scientific community using these model outputs and the expanded application of GGCMs, we introduce these models and present guidelines for their practical application. Related studies in this special issue focus on crop water demand and the fresh- water supply for irrigation (14), the application of the crop model results as part of wider intersectoral analyses (15), and the in- tegration of crop-climate impact assessments with agro-economic models (16).
As with the global climate models the models for agricultural productivity are structured with a wide variety of parameters which are not consistent between models.
These models differ in regard to model type, inclusion and parameterization of soil and crop processes, management inputs, and outputs. These dissimilarities must be taken into account in interpreting the results of the intercomparison and in the use of results in other analyses (SI Appendix, Table S1). Examples include the biological and environmental stresses affecting crops in each model and the treatment of how increasing [CO2] affects plant growth and yield. GAEZ-IMAGE, LPJ-GUESS, and LPJmL focus on water and temperature responses, whereas the other four models also consider stresses related to nitrogen deficiency and severe heat during various stages of development. In addition to these, pDSSAT considers oxygen stress, PEGASUS considers phosphorus and potassium stresses, and EPIC and GEPIC both consider oxygen, phosphorus, bulk density, and aluminum stresses.
When the researchers ran these models in a consistent fashion it turned out the results were broadly consistent with each other. These results were then compared to the results on agriculture yields from the IPCC AR4 report. What was found was that the results are generally similar but the ranges of uncertainty grew significantly. Much of this increased uncertainty was due to the greater diversity of modeling techniques, ranges of temperatures and greater geographic coverage. Some examples of results in this area. when using the 7 crop models under the RCP 8.5 scenario and comparing their results to AR4, are :
That at +2C yields in the mid to high latitudes for corn, wheat, rice and soybeans are almost identical and that the numbers are overall slightly positive (yields are up). For low latitudes corn yield is down about 10% from AR4 and the uncertainty range is dramatically lower; for rice, wheat and soybeans the results are similar and yields are down a significant amount (approximately 10%). The low latitude numbers are on the edge of real production problems.
That at +4C in the mid to high latitudes AR4 and the new analysis have similar results and that yields are overall unchanged with the uncertainty range growing to the high yield side. For low latitudes at +4C results are similar for corn and rice and better for wheat. But the take away here is that actual yields have reached what will amount to catastrophic declines from present with drops of 20% typical and big downside uncertainties. It is game over at this point.
Note: This part of the analysis has not yet included water stress and irrigation issues which are the subject of an additional paper. Those results when combined with the results of this work show very significant additional declines and indicate collapse well before +4C.
An additional issue, which once again mimics an issue from the Global Climate Models, is that these models were designed to produce information on not only different aspects of growing crops but also differ on the scale of geographic coverage. So even though the results were aggregated depending on what one is looking at and where one model might be much more accurate than the aggregated result from all of them. So some of those downside risks might be very close to being right depending on a specific crop in a specific place. And the aggregate might be too optimistic in some areas still as critical factors are not fully implemented yet. For instance the projections are for increasing yields in the high latitudes with rising temperatures, but the models have not yet included the poorer soils at high latitudes which quite possibly will reduce yields in all cases.
Other factors which it is not clear to me are yet taken into account (if they even can be) are weather issues. While the climate is warming and theoretically growing regions are moving away from the equator there still remains the vagaries of weather. Does anyone think that killing frosts will not still happen with some frequency in these warming regions? Maybe not as often or quite as cold as previously, but no farmer is going to plant a warmer region crop in a formerly colder area until he is sure that the odds are way in his favor that it will survive and prosper. If a crop failure due to a cold snap is at even a 1 in 5 chance he can't afford to take that chance. A farmer has to plant based upon what the first and last average frost dates are, not the average temperature. I am not clear on whether the frost dates move in a synchronous fashion with the rising average temperatures or not.
My next post on this subject will include the work from the paper on irrigation water availability found via this info on the PNAS site.
Constraints and potentials of future irrigation water availability on agricultural production under climate change PNAS 2013 ; published ahead of print December 16, 2013, doi:10.1073/pnas.1222474110
