The Uncertain Value of Perfect ENSO Phase Forecasts: Stochastic Agricultural Prices and Intra-Phase Climatic Variations

Climate and crop yield variability associated with El Niño—Southern Oscillation (ENSO) are now predictable within limits. This predictability suggests a potential to tailor agricultural management to mitigate impacts of adverse conditions and to take advantage of favorable conditions. However, improved climate predictions may benefit society only with parallel advances in our ability to use this knowledge. We show that the value that will accrue to any given actor from an ENSO phase forecast should be viewed not as a known number but instead as a random draw from a distribution, even when the forecast is always correct. Forecast value depends on the highly variable contexts in which forecasts are used. Randomness in forecast value has significant implications for choices made by forecasters, forecast users and policy makers. To show randomness, we estimate potential economic values of ENSO forecasts for agricultural producers based on two realistic assumptions: the crop prices farmers receive are uncertain; and within an ENSO phase, the actual climate is variable in ways that affect profits. The use of synthetic weather and crop price series, with crop simulation models, helps show the range and likelihood of climate forecast value.     

The effect of rate of change, variability, and extreme events on the pace of adaptation to a changing climate

When is it time to adopt different technologies, management strategies, and resource use practices as underlying climate change occurs? We apply risk and decision analysis to test hypotheses about the timing and pace of adaption in response to different profiles of climate change and extremes expressed as yield and income variation for a simulated dryland wheat farm in the United States Great Plains. Climate scenarios include gradual change with typical or increased noise (standard deviation), rapid and large change, and gradual change with extreme events stepped through the simulation. We test decision strategies that might logically be utilized by farmers facing a climate trend that worsens crop enterprise outcomes. Adaptation quickens with the rate of change, especially for decision strategies based on performance thresholds, but is delayed by larger climate variability, especially for decision strategies based on recognizing growing differential between adaptive and non-adaptive performance. Extreme events evoke adaptation sooner than gradual change alone, and in some scenarios extremes evoke premature, inefficient, adaptation.     

Potential predictability of Eurasian snow cover

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Niño-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.     

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO₂ runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO₂, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.     

南京信息工程大学气候预测系统1.0版简介

南京信息工程大学气候预测系统1.0版(NUIST CFS1.0)是基于日本海洋科学技术开发机构(JAMSTEC)的SINTEX-F模式发展而来,可以实现对全球气候异常的季节-年际预测。对过去近40 a的集合历史回报预测试验结果的评估发现,该预测系统对热带太平洋和印度洋海温异常具有良好的预测技巧,并且该系统能提前1.5~2 a对ENSO(Nino3.4指数)做出有技巧的预测(即相关系数达0.5),同时也可以提前1~2个季节对印度洋偶极子(IOD)做出有较高技巧的预测,展现了对主要热带气候信号的良好预测技巧。但是与国内外所有动力模式预测系统类似,该系统对东亚地区的气候异常预测还存在较大的不足。考虑到ENSO对东亚地区气候异常的强烈影响,本文尝试去除与ENSO预测相关的系统偏差来初步订正东亚地区夏季温度异常和降水距平百分率的预测结果。对比订正前后的结果表明,这一简单的订正方法有助于提高我国气候异常的预测准确率。同时选取2019年夏季气温异常和降水距平百分率的实时预测结果作为个例进行分析,发现订正能够提供一定的技巧改善,但与观测结果相比仍存在较大偏差,需要在今后的工作中不断改进完善。此外,本文也初步评估了NUIST CFS1.0对我国冬春季的气候预测技巧,并提供了经简单订正后的2019/2020年冬季和2020年春季的实时预测结果。     

Precipitation extremes in a karst region: a case study in the Guizhou province, southwest China

The topography of hilly landscapes modifies crop environment changing the fluxes of water and energy, increasing risk in these vulnerable agriculture systems, which could become more accentuated under climate change (drought, increased variability of rainfall). In order to quantify how wheat production in hilly terrain will be affected by future climate, a newly developed and calibrated micro-meteorological model for hilly terrain was linked to a crop growth simulation model to analyse impact scenarios for different European regions. Distributions of yield and growing length of rainfed winter wheat and durum wheat were generated as probabilistic indices from baseline and low (B2) and high (A2) emission climate scenarios provided from the Hadley Centre Regional Climate Model (HadRM3). We used site-specific terrain parameters for two sample catchments in Europe, ranging from humid temperate (southeast UK) to semi-arid Mediterranean (southern Italy). Results for baseline scenario show that UK winter wheat is mainly affected by annual differences in precipitation and yield distributions do not change with terrain, whilst in the southern Mediterranean climate yield variability is significantly related to a slope × elevation index. For future climate, our simulations confirm earlier predictions of yield increase in the UK, even under the high emission scenario. In the southern Mediterranean, yield reduction is significantly related to slope × elevation index increasing crop failure in drier elevated spots but not in wet years under baseline weather. In scenarios for the future, the likelihood of crop failure rises sharply to more than 60%, and even in wet years, yields are likely to decrease in elevated spots.     

Dynamical seasonal prediction of Southern African summer precipitation

Prediction skill for southern African (16°–33°E, 22°–35°S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Niño/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Niño, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.     

Impact of ENSO-Related Climate Anomalies on Crop Yields in the U.S.

Historical daily thermal and precipitation data from selected stations across the United States are composited into climate scenarios for the three phases of ENSO: Warm Events (El Niño), Cold Events (El Viejo or La Niña), and Neutral. Using these scenarios, yields of 7 field crops were simulated using the EPIC biophysical model during the one-year period coincident with maximum SST anomalies in the equatorial Pacific. The response of simulated agricultural productivity to the ENSO-related climate-variability parameters, is presented. A sensitivity calculation confirms the relevance of precipitation totals/medians and suggests ENSO-related yields are sensitive to changes in statistical properties characterizing precipitation distribution and occurrence. Results are spatially dependent, with the southwest and northern plains regions indicating the highest sensitivity to the inclusion of additional precipitation characteristics. The southeast yields are not as sensitive. The yield deviations (expressed as normalized differences to neutral yields) associated with the two extreme ENSO phases (Warm Events and Cold Events) are spatially and crop dependent with ranges up to ±120%. The largest yield deviations are in the south, southwest, and northern plains. Overall, Cold Events demonstrate larger impacts in the southern regions and Warm Events have a larger impact in the north. Additionally, the notion that climate anomalies associated with Cold and Warm Events and subsequent impacts on yields should be of opposite sign (i.e., linear) is not valid in many regions. For the eastern half of the U.S., modeled yield deviations under Warm Event conditions are nearly all less than neutral. Conversely, in the western half, results are more mixed. Under Cold Event conditions, yields in the east are enhanced in the south, but worsened in the north; while in the western half, yields have decreased in general. The results highlight the critical role of climate and production-related data on station or county levels in quantifying the impact of ENSO climate anomalies on yields. Both the diverse nature of the ENSO-related yield deviations as well as their sensitivity to monthly frequency distribution and occurrence characteristics imply that ENSO-related seasonal precipitation forecasts might be beneficial for agricultural application only if details were provided regarding not only totals, but also predicted changes in temporal and spatial variability of a more comprehensive suite of characteristics.     

Smallholder Maize Production and Climatic Risk: A Case Study from Mexico

The article explores the strategies employed by smallholder farmers in Mexico to cope with the affects of climatic variability, and how seasonal climate forecasts may assist these farmers in mitigating climatic risk. Recognizing that the decisions of smallholder farmers are intricately tied to the political-economic circumstances in which they operate, the article discusses how agricultural policy in Mexico affects the vulnerability of small-scale producers and may inhibit their ability to use climatic forecasts to their advantage. The article first reviews the literature on smallholder adaptation in Mexico, and discusses briefly policy and institutional issues affecting adaptation at the farm-level. Using the case of small-scale maize producers in Tlaxcala, Mexico, as an illustration, the article then argues that political-economic uncertainty outweighs climatic variability as a determinant of the production strategies of small-scale producers. In these circumstances, the farmers are unlikely to use new seasonal climate forecasts.     

Quantifying the value of historical climate knowledge and climate forecasts using agricultural systems modelling

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts.     

Interannual variability of the surface mass balance of West Antarctica from ITASE cores and ERA40 reanalyses, 1958–2000

Time series of west-Antarctic (WA) annual surface mass balance (SMB) from ITASE firn/ice cores are compared with the ECMWF 1958–2001 ERA40 reanalysis-based model forecasts. The ITASE series partially confirm the spatial structure of the signature of El Nino Southern Oscillation (ENSO) in WA precipitation as previously identified in ERA40 and other models. However, an improvement of ERA40’s ability to reproduce the west-Antarctic SMB since the 1970s is evidenced and is probably related to the onset and increasing use of satellite data in late 1972 and 1978. Restricting the analysis to the 1973–2000 (satellite) period, interannual correlations between ITASE cores and ERA40 SMB series are generally significant (95% confidence level) but weak. The fraction of common variability increases when the series are spatially averaged, suggesting that small-scale perturbation (SSP) of the large-scale SMB variability significantly contributes to year-to-year variability in single core series. A comparison of stake network and core data from the South Pole suggests that SSP can almost fully obscure the large-scale component of the SMB variability as recorded in a single core. Because of SSP, the 1973–2000 period is too brief to verify whether all aspects of the WA large-scale signatures of ENSO and of the Antarctic Oscillation suggested by ERA40 are confirmed in the core series. More annually resolved field data from cores and stakes, spatially extended using high-resolution ground penetrating radar, are necessary to fully assess the relationship between the Antarctic SMB and the large-scale climate as currently suggested by meteorological and climate models.     

U.S. Agriculture and Climate Change: New Results

We examined the impacts on U.S. agriculture of transient climate change assimulated by 2 global general circulation models focusing on the decades ofthe 2030s and 2090s. We examined historical shifts in the location of cropsand trends in the variability of U.S. average crop yields, finding thatnon-climatic forces have likely dominated the north and westward movement ofcrops and the trends in yield variability. For the simulated future climateswe considered impacts on crops, grazing and pasture, livestock, pesticide use,irrigation water supply and demand, and the sensitivity to international tradeassumptions, finding that the aggregate of these effects were positive for theU.S. consumer but negative, due to declining crop prices, for producers. Weexamined the effects of potential changes in El Niño/SouthernOscillation (ENSO) and impacts on yield variability of changes in mean climateconditions. Increased losses occurred with ENSO intensity and frequencyincreases that could not be completely offset even if the events could beperfectly forecasted. Effects on yield variability of changes in meantemperatures were mixed. We also considered case study interactions ofclimate, agriculture, and the environment focusing on climate effects onnutrient loading to the Chesapeake Bay and groundwater depletion of theEdward's Aquifer that provides water for municipalities and agriculture to theSan Antonio, Texas area. While only case studies, these results suggestenvironmental targets such as pumping limits and changes in farm practices tolimit nutrient run-off would need to be tightened if current environmentalgoals were to be achieved under the climate scenarios we examined     

气候变化对雨养冬小麦水分利用效率的影响估算

研究气候变化对雨养冬小麦水分利用效率的影响规律,可为农业适应气候变化提供科学依据。通过构建代表站雨养冬小麦产量和土壤水分变化量的模拟方程,分析水分利用效率的历史变化,并结合两种区域气候模式PRECIS和REGCM4.0输出的4种不同气候变化情景资料,估算未来2021—2050年雨养冬小麦水分利用效率的可能变化。结果表明:1981—2010年甘肃、山西和河南代表站的雨养冬小麦水分利用效率呈二次曲线变化趋势,最大值出现在2003年前后。4种气候变化情景的模拟结果均显示:2021—2050年冬小麦全生育期耗水量明显增加,各代表站不同情景平均增加6.2%;产量有增有减,平均产量变化率为1.4%;水分利用效率平均减小3.8%,且变率减小。区域气候模式PRECIS估算的水分利用效率的减小量A2情景大于B2情景,REGCM4.0模式估算的水分利用效率的减小量RCP8.5情景大于RCP4.5情景。整体来看,RCP气候情景对雨养冬小麦水分利用效率的负面影响更大。     

Agricultural Value of ENSO Information under Alternative Phase Definition

The El Niño-Southern Oscillation (ENSO) effect has been found to be associated with regional climate variations in many regions of the world, and, in turn, with variation in crop yields. Previous studies have found that early releases of ENSO phase information could permit agricultural producers to make adjustments in their decisions and in turn generate an increase in agricultural sector welfare. This study examines whether the value of the agricultural responses can be enhanced by releasing more detailed ENSO information. Namely we evaluate the implications for projected agricultural welfare under release and adaptation to the Stone and Auliciems five phase definition of ENSO states as opposed to the more standard three phase definition. This value is estimated using a stochastic, U.S./global agricultural model representing 22 climate years. The results indicate that the release and exploitation of the more detailed ENSO phase definition almost doubles the welfare impact. The results also indicate that there is room for up to another doubling of information value through further refinements.     

ENSO modulation by mountain uplift

How climate changes will modify the behavior of El Niño/Southern Oscillation (ENSO) is one of the important questions in future climate projections. An investigation under different climate forcing gives us a good insight on the mechanism of ENSO variability and its changes. In this paper, sensitivity on ENSO by progressive mountain uplift is investigated with an atmosphere–ocean coupled general circulation model. We used eight different mountain heights: 0% (no mountain), 20, 40, 60, 80, 100 (control run), 120, and 140%. Land–sea distribution is the same for all experiments and all mountains in the world are uniformly varied. Systematic changes in precipitation and circulation fields as well as SST are obtained with progressive mountain uplift. In the summertime, the precipitation area moved inland of the Asian continent with mountain uplift, while the Pacific subtropical anticyclone and associated trade winds became stronger. The western Pacific warm pool and ENSO also systematically changed. When the mountain height is low, a warm pool is located over the central Pacific due to weak trade winds in the Pacific. The model ENSO is strongest, its frequency longest, and is most periodic in the no mountain run. The model ENSO becomes weaker, shorter and less periodic when the mountain height increases. Strengthening the mean state trade winds and narrowing meridional extent of equatorial wind and ocean response by mountain uplift would be responsible for ENSO modulation.     

Estimating hydrological characteristics in the Amu Darya River basin under climate change conditions

Under consideration are results of solving the problem of the river water content estimation under conditions of uncertainties of climate change forecasts and the catchment state with a reference to the Amu Darya River basin. When constructing regional climate models, one selected a multimodel approach using the results of several global models and a statistical downscaling method that made the climate scenarios more detailed. The estimates demonstrated that in the medium- and long-term perspective, the Amu Darya River runoff is expected to decrease. As a result of the Bayesian ideology application, using the calculations got with a total probability formula, a prognostic probability curve of an annual river runoff supply of the basin rivers was derived based on different weights given to the estimates of a mean value for different climate scenarios. Prognostic characteristics of the annual runoff for the Amu Darya basin rivers are estimated in a form acceptable for hydrologic and hydroeconomic application.     

气候变化对我国小麦发育及产量可能影响的模拟研究

利用随机天气模型, 将气候模式对大气中CO₂倍增时预测的气候情景与CERES-小麦模式相连接, 研究了气候变化对我国冬小麦和春小麦生产的可能影响.结果表明, 气候变化后小麦发育将加快, 生育期缩短, 冬小麦平均缩短7.3天, 春小麦平均缩短10.5天, 春小麦生育期缩短的绝对数和相对数均大于冬小麦.籽粒产量呈下降趋势, 冬小麦平均减产7%~8%, 雨养条件下比水分适宜时减产幅度略大.春小麦的减产幅度大于冬小麦, 水分适宜时平均减产17.7%, 雨养时平均减产31.4%.     

An Assessment of Integrated Climate Change Impacts on the Agricultural Economy of Egypt

This study used a quadratic programming sector model to assess the integrated impacts of climate change on the agricultural economy of Egypt. Results from a dynamic global food trade model were used to update the Egyptian sector model and included socio-economic trends and world market prices of agricultural goods. In addition, the impacts of climate change from three bio-physical sectors – water resources, crop yields, and land resources – were used as inputs to the economic model. The climate change scenarios generally had minor impacts on aggregated economic welfare (sum of Consumer and Producer Surplus or CPS), with the largest reduction of approximately 6 percent. In some climate change scenarios, CPS slightly improved or remained unchanged. These scenarios generally benefited consumers more than producers, as world market conditions reduced the revenue generating capacity of Egyptian agricultural exporters but decreased the costs of imports. Despite increased water availability and only moderate yield declines, several climate change scenarios showed producers being negatively affected by climate change. The analysis supported the hypothesis that smaller food importing countries are at a greater risk to climate change, and impacts could have as much to do with changes in world markets as with changes in local and regional biophysical systems and shifts in the national agricultural economy.     

Does the CDM discourage emission reduction targets in advanced developing countries?

Under the Kyoto Protocol, developing countries can voluntarily participate in climate change mitigation through the Clean Development Mechanism (CDM), in which industrialized countries, in order to meet their mitigation commitments, can buy emission reduction credits from projects in developing countries. Before its implementation, developing-country experts opposed the CDM, arguing that it would sell-off their countries’ cheapest emission reduction options and force them to invest in more expensive measures to meet their future reduction targets. This ‘low-hanging fruit’ argument is analysed empirically by comparing marginal abatement cost curves. Emissions abatement costs and potentials for CDM projects are estimated for different technologies in eight countries, using capital budgeting tools and information from project documentation. It is found that the CDM is not yet capturing a large portion of the identified abatement potential in most countries. Although the costs of most emissions reduction opportunities grasped are below the average credit price, there are still plenty of available low-cost opportunities. Mexico and Argentina appear to use the CDM predominantly for harvesting the low-hanging fruit, whereas in the other countries more expensive projects are accessing the CDM. This evidence at first sight challenges the low-hanging fruit claim, but needs to be understood in the light of the barriers for the adoption of low-cost abatement options.     

Potential Impacts of Climate Change on Agriculture: A Case of Study of Coffee Production in Veracruz, Mexico

This paper explores the relation between coffee production and climatic and economic variables in Veracruz in order to estimate the potential impacts of climate change. For this purpose, an econometric model is developed in terms of those variables. The model is validated by means of statistical analysis, and then used to project coffee production under different climatic conditions. Climate change scenarios are produced considering that the observed trends of climate variables will continue to prevail until the year 2020. An approach for constructing simple probability scenarios for future climate variability is presented and used to assess possible impacts of climate change beyond what is expected from changes in mean values. The model shows that temperature is the most relevant climatic factor for coffee production, since production responds significantly to seasonal temperature patterns. The results for the projected climate change conditions for year 2020 indicate that coffee production might not be economically viable for producers, since the model indicates a reduction of 34% of the current production. Although different economic variables (the state and international coffee prices, a producer price index for raw materials for coffee benefit, the national and the USA coffee stocks) were considered as potentially relevant, our model suggests that the state real minimum wage could be regarded as the most important economic variable. Real minimum wage is interpreted here as a proxy for the price of labor employed for coffee production. This activity in Mexico is very labor intensive representing up to 80% of coffee production costs. As expected, increments in the price of such an important production factor increase production costs and have strong negative effects on production. Different assumptions on how real minimum wage could evolve for the year 2020 are considered for developing future production scenarios.