A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

A new approach to identifying the weather-crop yield functionals for assessing climate change consequences

A new approach to identifying the weather-crop yield functionals is suggested. It is shown that elimination of crop yield trends using the difference regression (the first and second orders) makes it possible to substantially increase the accuracy and reliability of estimates of climate change (variation) influence on the agriculture productivity. The methodology suggested for assessing a climate change influence is realized for the grain crops in two regions of the Russian Federation with contrast climate conditions. At the same time, it is found that short-term (up to 2–3 years) crop yield trends taken into account and related to changes in the soil effective fertility promote a noticeable increase in the quality of long-term crop yield forecasts.     

Climate change impacts on dryland cropping systems in the Central Great Plains,USA

Agricultural systems models are essential tools to assess potential climate change (CC) impacts on crop production and help guide policy decisions. In this study, impacts of projected CC on dryland crop rotations of wheat-fallow (WF), wheat-corn-fallow (WCF), and wheat-corn-millet (WCM) in the U.S. Central Great Plains (Akron, Colorado) were simulated using the CERES V4.0 crop modules in RZWQM2. The CC scenarios for CO₂, temperature and precipitation were based on a synthesis of Intergovernmental Panel on Climate Change (IPCC 2007) projections for Colorado. The CC for years 2025, 2050, 2075, and 2100 (CC projection years) were super-imposed on measured baseline climate data for 15–17 years collected during the long-term WF and WCF (1992–2008), and WCM (1994–2008) experiments at the location to provide inter-annual variability. For all the CC projection years, a decline in simulated wheat yield and an increase in actual transpiration were observed, but compared to the baseline these changes were not significant (p > 0.05) in all cases but one. However, corn and proso millet yields in all rotations and projection years declined significantly (p < 0.05), which resulted in decreased transpiration. Overall, the projected negative effects of rising temperatures on crop production dominated over any positive impacts of atmospheric CO₂ increases in these dryland cropping systems. Simulated adaptation via changes in planting dates did not mitigate the yield losses of the crops significantly. However, the no-tillage maintained higher wheat yields than the conventional tillage in the WF rotation to year 2075. Possible effects of historical CO₂ increases during the past century (from 300 to 380 ppm) on crop yields were also simulated using 96 years of measured climate data (1912–2008) at the location. On average the CO₂ increase enhanced wheat yields by about 30%, and millet yields by about 17%, with no significant changes in corn yields.     

Historical effects of temperature and precipitation on California crop yields

For the 1980–2003 period, we analyzed the relationship between crop yield and three climatic variables (minimum temperature, maximum temperature, and precipitation) for 12 major Californian crops: wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios. The months and climatic variables of greatest importance to each crop were used to develop regressions relating yield to climatic conditions. For most crops, fairly simple equations using only 2–3 variables explained more than two-thirds of observed yield variance. The types of variables and months identified suggest that relatively poorly understood processes such as crop infection, pollination, and dormancy may be important mechanisms by which climate influences crop yield. Recent climatic trends have had mixed effects on crop yields, with orange and walnut yields aided, avocado yields hurt, and most crops little affected by recent climatic trends. Yield-climate relationships can provide a foundation for forecasting crop production within a year and for projecting the impact of future climate changes.     

Tree-Ring Reconstructed Maize Yield in Central Mexico: 1474–2001

Maize was domesticated more than 6,000 years ago in central Mexico, and remains a vital staple food and cultural symbol in Mesoamerica. Maize yield in the central highlands is strongly dependant on adequate rainfall early in the growing season (April–June) because late maturation of the crop may result in damage from autumn frost. Climate-induced crop failures with profound socioeconomic impacts have punctuated Mexican history. However, reliable records of maize harvest have not been available until very recently, and historical records of crop yield and price are discontinuous and can be difficult to interpret. We have developed a continuous, exactly dated, tree-ring reconstruction of maize yield variability in central Mexico from 1474 to 2001 that provides new insight into the history of climate and food availability in the heartland of the Mesoamerican cultural province. The reconstruction indicates that seven of the most severe agricultural crises in Mexican history occurred during decadal-scale episodes of reconstructed maize shortfalls.     

Sensitivity of southern African maize yields to the definition of sowing dekad in a changing climate

Most African countries struggle with food production and food security. These issues are expected to be even more severe in the face of climate change. Our study examines the likely impacts of climate change on agriculture with a view to propose adaptation options, especially in hard hit regions. We use a crop model to evaluate the impact of various sowing decisions on the water satisfaction index (WSI) and thus the yield of maize crop. The crop model is run for 176 stations over southern Africa, subject to climate scenarios downscaled from 6 GCMs. The sensitivity of these simulations is analysed so as to distinguish the contributions of sowing decisions to yield variation. We compare the WSI change between a 20 year control period (1979–1999) and a 20 year future period (2046–2065) over southern Africa. These results highlight areas that will likely be negatively affected by climate change over the study region. We then calculate the contribution of sowing decisions to yield variation, first for the control period, then for the future period. This contribution (sensitivity) allows us to distinguish the efficiency of adaptation decisions under both present and future climate. In most countries rainfall in the sowing dekad is shown to contribute more significantly to the yield variation and appears as a long term efficient decision to adapt. We discuss these results and additional perspectives in order to propose local adaptation directions.     

Climate change,weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Climate change has led to increased temperatures, and simulation models suggest that this should affect crop production in important agricultural regions of the world. Nations at higher latitudes, such as Canada, will be most affected. We studied the relationship between climate variability (temperature and precipitation) and corn yield trends over a period of 33 years for the Monteregie region of south-western Quebec using historical yield and climate records and statistical models. Growing season mean temperature has increased in Monterregie, mainly due to increased September temperature. Precipitation did not show any clear trend over the 33 year period. Yield increased about 118 kg ha⁻¹ year⁻¹ from 1973 to 2005 (under normal weather conditions) due mainly to changes in technology (genetics and management). Two climate variables were strongly associated with corn yield variability: July temperature and May precipitation. These two variables explain more than a half of yield variability associated with climate. In conclusion, July temperatures below normal and May precipitation above normal have negative effects on corn yield, and the growing seasons have warmed, largely due to increases in the September temperature.     

Climate change and critical thresholds in China’s food security

Identification of ‘critical thresholds’ of temperature increase is an essential task for inform policy decisions on establishing greenhouse gas (GHG) emission targets. We use the A2 (medium-high GHG emission pathway) and B2 (medium-low) climate change scenarios produced by the Regional Climate Model PRECIS, the crop model – CERES, and socio-economic scenarios described by IPCC SRES, to simulate the average yield changes per hectare of three main grain crops (rice, wheat, and maize) at 50 km × 50 km scale. The threshold of food production to temperature increases was analyzed based on the relationship between yield changes and temperature rise, and then food security was discussed corresponding to each IPCC SRES scenario. The results show that without the CO₂ fertilization effect in the analysis, the yield per hectare for the three crops would fall consistently as temperature rises beyond 2.5 ^{^}C; when the CO₂ fertilization effect was included in the simulation, there were no adverse impacts on China’s food production under the projected range of temperature rise (0.9–3.9 ^{^}C). A critical threshold of temperature increase was not found for food production. When the socio-economic scenarios, agricultural technology development and international trade were incorporated in the analysis, China’s internal food production would meet a critical threshold of basic demand (300 kg/capita) while it would not under A2 (no CO₂ fertilization); whereas basic food demand would be satisfied under both A2 and B2, and would even meet a higher food demand threshold required to sustain economic growth (400 kg/capita) under B2, when CO₂ fertilization was considered.     

Changes in Climate and Crop Production During the 20th Century in Argentina

This work was focused on the assessment of changes occurring in crop production and climate during the 20th century in Argentina. The study was carried out for nine sites located in the Pampas region that are representative of contrasting environments. We have considered the four main crops cultivated in this area (wheat, maize, sunflower and soybean). Historical climatic data and crop production related variables (yield, planted area, harvested area) were analyzed and, by means of crop simulation models, we quantified the impact of climate on crop yields. Changes occurring in climate during the three last decades of the 20th century were characterized by important increases in precipitation especially between October and March, decreases in maximum temperature and solar radiation in particular during spring and summer and increases in minimum temperature during almost all of the year. These changes contributed to increases in yields, especially in summer crops and in the semiarid zone, mostly due to increases in precipitation, although changes in temperature and radiation also affected crop yields but to a lesser extent. Comparing the period 1950–1970 with 1971–1999, yields increases attributable to changes in climate were 38% in soybean, 18% in maize, 13% in wheat, and 12% in sunflower while mean observed yield increases were 110% for maize, 56% for wheat and 102% for sunflower.     

Standardized Precipitation Index Reconstructed from Turkish Tree-Ring Widths

May–July Standardized Precipitation Index (SPI) for the land area of most of Turkey and some adjoining regions are reconstructed from tree rings for the period 1251–1998. The reconstruction was developed from principal components analysis (PCA) of four Juniperus excelsa chronologies from southwestern and south-central Turkey and is based on reliable and replicable statistical relationships between climate and tree ring growth. The SPI reconstruction shows climate variability on both interannual and interdecadal time scales. The longest period of consecutive drought years in the reconstruction (SPI threshold ≤−1) is 2 yr. These occur in 1607–1608, 1675–1676, and 1907–1908. There are five wet events (SPI threshold ≥+1) of two consecutive years each (1330–1331, 1428–1429, 1503–1504, 1629–1630, and 1913–1914). A 5-yr moving average of the reconstructed SPI shows that two sustained drought periods occurred from the mid to late 1300s and the early to mid 1900s. Both episodes are characterized by low variability.     

A study of NAO variability and its possible non-linear influences on European surface temperature

 The relationship between European winter temperature spatial and temporal modes of variability and the North Atlantic Oscillation (NAO) has been studied during the period 1852–1997. Temporal modes of variability of the NAO and temperatures are analysed using wavelet transform. Results show that the NAO presents a strong non-stationary behaviour. The most important feature is the existence of a quasi-periodic oscillation, with a period between 6–10 years and maximum amplitude of eight years, during the periods 1842–1868 and 1964–1994. Between 1875 and 1939 the spectra of the NAO is almost white. The possible relationship between the occurrence of extreme events of the NAO and its spectral behaviour has been analysed. The results indicate that quasi-periodic oscillations in the NAO do not lead to more extreme episodes, but rather that an extreme value of the oscillation is more likely to persist for few years. Particularly energetic modes of coherent variability between temperature and NAO are found between 2–6 years for 1857–1879 and 1978–1984, and between 6–10 years from 1961 to 1991. The relationship between the NAO and temperatures as a function of the state of the oscillation has been studied using composites. Empirical evidence has been found suggesting that winter temperatures, in a great part of the study area, do not vary in a linear manner with respect to phase and intensity of the NAO. Regions in the study area differ in sensitivity to changes in the NAO. The spatial patterns of variability of the temperatures are found to be independent of the NAO spectra. Received: 8 April 1999 / Accepted: 19 September 2000     

Low and high frequency Madden–Julian oscillations in austral summer: interannual variations

The Madden–Julian oscillation (MJO) is the main component of intraseasonal variability of the tropical convection, with clear climatic impacts at an almost-global scale. Based on satellite observations, it is shown that there are two types of austral-summer MJO events (broadly defined as 30–120 days convective variability with eastward propagation of about 5 m/s). Equatorial MJO events have a period of 30–50 days and tend to be symmetric about the equator, whereas MJO events centered near 8°S tend to have a longer period of 55–100 days. The lower-frequency variability is associated with a strong upper-ocean response, having a clear signature in both sea surface temperature and its diurnal cycle. These two MJO types have different interannual variations, and are modulated by the Indian Ocean Dipole (IOD). Following a negative IOD event, the lower-frequency southern MJO variability increases, while the higher-frequency equatorial MJO strongly diminishes. We propose two possible explanations for this change in properties of the MJO. One possibility is that changes in the background atmospheric circulation after an IOD favour the development of the low-frequency MJO. The other possibility is that the shallower thermocline ridge and mixed layer depth, by enhancing SST intraseasonal variability and thus ocean–atmosphere coupling in the southwest Indian Ocean (the breeding ground of southern MJO onset), favour the lower-frequency southern MJO variability.     

Factors that affect the amplitude of El Nino in global coupled climate models

Historically, El Nino-like events simulated in global coupled climate models have had reduced amplitude compared to observations. Here, El Nino-like phenomena are compared in ten sensitivity experiments using two recent global coupled models. These models have various combinations of horizontal and vertical ocean resolution, ocean physics, and atmospheric model resolution. It is demonstrated that the lower the value of the ocean background vertical diffusivity, the greater the amplitude of El Nino variability which is related primarily to a sharper equatorial thermocline. Among models with low background vertical diffusivity, stronger equatorial zonal wind stress is associated with relatively higher amplitude El Nino variability along with more realistic east–west sea surface temperature (SST) gradient along the equator. The SST seasonal cycle in the eastern tropical Pacific has too much of a semiannual component with a double intertropical convergence zone (ITCZ) in all experiments, and thus does not affect, nor is it affected by, the amplitude of El Nino variability. Systematic errors affecting the spatial variability of El Nino in the experiments are characterized by the eastern equatorial Pacific cold tongue regime extending too far westward into the warm pool. The time scales of interannual variability (as represented by time series of Nino3 SSTs) show significant power in the 3–4 year ENSO band and 2–2.5 year tropospheric biennial oscillation (TBO) band in the model experiments. The TBO periods in the models agree well with the observations, while the ENSO periods are near the short end of the range of 3–6 years observed during the period 1950–94. The close association between interannual variability of equatorial eastern Pacific SSTs and large-scale SST patterns is represented by significant correlations between Nino3 time series and the PC time series of the first EOFs of near-global SSTs in the models and observations. Received: 17 April 2000 / Accepted: 17 August 2000     

Weakened SST variability in the tropical Atlantic Ocean since 2000

A prominent weakening in equatorial Atlantic sea surface temperature (SST) variability, occurring around the year 2000, is investigated by means of observations, reanalysis products and the linear recharge oscillator (ReOsc) model. Compared to the time period 1982–1999, during 2000–2017 the May–June–July SST variability in the eastern equatorial Atlantic has decreased by more than 30%. Coupled air–sea feedbacks, namely the positive Bjerknes feedback and the negative net heat flux damping are important drivers for the equatorial Atlantic interannual SST variability. We find that the Bjerknes feedback weakened after 2000 while the net heat flux damping increased. The weakening of the Bjerknes feedback does not appear to be fully explainable by changes in the mean state of the tropical Atlantic. The increased net heat flux damping is related to an enhanced response of the latent heat flux to the SST anomalies (SSTa). Strengthened trade winds as well as warmer SSTs are suggested to increase the air–sea specific humidity difference and hence, enhancing the latent heat flux response to SSTa. A combined effect of those two processes is proposed to be responsible for the weakened SST variability in the eastern equatorial Atlantic. The ReOsc model supports the link between reduced SST variability, weaker Bjerknes feedback and stronger net heat flux damping.     

Modelling climate change impacts on maize growth and development in the Czech Republic

Summary The crop growth model CERES-Maize is used to estimate the direct (through enhanced fertilisation effect of ambient CO₂) and indirect (through changed climate conditions) effects of increased concentration of atmospheric CO₂ on maize yields. The analysis is based on multi-year crop model simulations run with daily weather series obtained alternatively by a direct modification of observed weather series and by a stochastic weather generator. The crop model is run in two settings: stressed yields are simulated in water and nutrient limited conditions, potential yields in water and nutrient unlimited conditions. The climate change scenario was constructed using the output from the ECHAM3/T42 model (temperature), regression relationships between temperature and solar radiation, and an expert judgement (precipitation). Results: (i) After omitting the two most extreme misfits, the standard error between the observed and modelled yields is 11%. (ii) The direct effect of doubled CO₂: The stressed yields would increase by 36–41% in the present climate and by 61–66% in the 2 × CO₂ climate. The potential yields would increase only by 9–10% as the improved water use efficiency does not apply. (iii) The indirect effect of doubled CO₂: The stressed yields would decrease by 27–29% (14–16%) at present (doubled) ambient CO₂ concentration. The increased temperature shortens the phenological phases and does not allow for the optimal development of the crop. The simultaneous decrease of precipitation and increase of temperature and solar radiation deepen the water stress, thereby reducing the yields. The reduction of the potential yields is significantly smaller as the effect of the increased water stress does not apply. (iv) If both direct and indirect effects of doubled CO₂ are considered, the stressed yields should increase by 17–18%, and the potential yields by 5–14%. (v) The decrease of the stressed yields due to the indirect effect may be reduced by applying earlier planting dates. Received March 9, 2001 Revised September 25, 2001     

Climate change,the monsoon,and rice yield in India

Recent research indicates that monsoon rainfall became less frequent but more intense in India during the latter half of the Twentieth Century, thus increasing the risk of drought and flood damage to the country’s wet-season (kharif) rice crop. Our statistical analysis of state-level Indian data confirms that drought and extreme rainfall negatively affected rice yield (harvest per hectare) in predominantly rainfed areas during 1966–2002, with drought having a much greater impact than extreme rainfall. Using Monte Carlo simulation, we find that yield would have been 1.7% higher on average if monsoon characteristics, especially drought frequency, had not changed since 1960. Yield would have received an additional boost of nearly 4% if two other meteorological changes (warmer nights and lower rainfall at the end of the growing season) had not occurred. In combination, these changes would have increased cumulative harvest during 1966–2002 by an amount equivalent to about a fifth of the increase caused by improvements in farming technology. Climate change has evidently already negatively affected India’s hundreds of millions of rice producers and consumers.     

Spatial Analysis of Environmental Change Impacts on Wheat Production in Mid-Lower North, South Australia

Three environmental change scenarios (the best scenario, the most likely scenario and the worst scenario) were used by the APSIM (Agricultural Production System sIMulator) Wheat module to study the possible impacts of future environmental change (climate change plus pCO₂ change) on wheat production in the Mid-Lower North of South Australia. GIS software was used to manage spatial-climate data and spatial-soil data and to present the results. Study results show that grain yield (kg ha⁻¹) was adversely affected under the worst environmental change scenario (−100% ∼ −42%) and the most likely environmental change scenario (−58% ∼ −3%). Grain nitrogen content (% N) either increased or decreased depending on the environmental change scenarios used and climate divisions (−25% ∼ +42%). Spatial variability was found for projected impact outcomes within climate divisions indicating the necessity of including the spatial distribution of soil properties in impact assessment.     

Trend and interannual variability of temperature in Malaysia: 1961–2002

Summary This paper investigates the warming trend and interannual variability of surface air temperatures in the Malaysian region during the period 1961–2002. The trend analyses show that most regions in Malaysia experience warming over the period at comparable rates to those in regions surrounding the Bay of Bengal. The regions of Peninsular Malaysia and northern Borneo experience warming rates of between 2.7–4.0 °C/100 years. However, the warming rates are lower in the south-western region of Borneo. The interannual variability of Malaysian temperature is largely dominated by the El Ni?o-Southern Oscillation (ENSO). Regardless of the warming trends, all regions in Malaysia experience uniform warming during an El Ni?o event, particularly during the October–November–December (OND) and the January–February–March (JFM) periods. This uniform warming is associated with the latent heat released from the central eastern Pacific region and forced adiabatic subsidence in the Maritime Continent during an El Ni?o event. During its early development period i.e. during the July–August–September (JAS) season, the El Ni?o’s impact on the Malaysian temperatures is relatively weak compare to its influence during the OND and JFM seasons. However, the warming continues to the April–May–June (AMJ) season although during this period the anomalous conditions in the eastern central Pacific have begun or have returned to normal. The Indian Ocean Dipole (IOD) mode exerts an influence on Malaysian temperatures. When it co-occurs with ENSO, it tends to weaken the ENSO influence particularly during an OND period. However, it appears to have an appreciable influence only during an AMJ period when it occurs in the absence of an ENSO event. Despite the strong influence of the ENSO, the warming rates during the 42-year period appears to be least affected by interannual variability.     

The band characteristics of long-term variation of precipitation and water resources in east asia monsoon region

In this paper, by means of the “CWH” (circulation-weather and climate-agricultural harvest) analytical meth-od, drawing from the monthly mean long-term data of the global sea level pressure field and annual precipitation data and grain yield data of some regions in East Asia, the time-space characters of low-frequency fluctuation of East Asian precipitation with band distribution are analyzed.It is revealed that the band of 20-30 years quasi-periodic fluctuation is located in Southeast Asia and the coastal areas of South China. Moving forward to west and north, the period of fluctuation of precipitation tends to become longer, both bands, with about 40-yr and 60-80-yr quasi-periodic fluctuation are respectively situated in the middle-lower reaches of the Yangtze River and North or Southwest China. Furthermore, the relationships between the fluctuations of precipitation bands and large-scale circulations are analyzed. The relationships between precipita-tion and types of water resources are discussed as well. In addition, the tendency of long-term variation in rainfall is predicted.     

Effect of topography on farm-scale spatial variation in extreme temperatures in the Southern Mallee of Victoria, Australia

Extreme temperatures around flowering of wheat have the potential to reduce grain yield and at farm scale their impact can be spatially variable depending on topography. Twenty-five data loggers were installed at 0.8-m height across a 164-ha farm in the southern Mallee of Victoria, Australia to spatially record the daily course of temperatures around the average date of flowering of wheat in the region. The experiment was conducted during 2-years period. In 1 year, the farm had no crop cover and in another year the farm had a wheat crop. Multiple linear regression analysis techniques were used to fit models relating daily extreme temperatures to the farm topographic features of elevation, aspect and slope, and the average maximum and minimum temperatures of each day at the farm in order to identify areas of high risk of extreme temperatures around the time of the flowering of wheat. The fitted regression models explained 90% and 97% of the variability in maximum and minimum temperatures, respectively, when the farm had no crop cover and 80% and 94% of the variability in maximum and minimum temperatures, respectively, when the farm had a wheat crop cover. When the farm had no crop, only minimum temperature was partially explained by the topography however, both maximum and minimum temperatures were partially explained by the topography when the farm had a wheat crop. From this study it was concluded that, (1) high temperature variations were found across the farm (2) temperature variations were only partially explained from the developed model presumably due to the flatter topography of the farm and (3) the relationships obtained from this study could be used in a crop model which can explain variation in grain yield based on the topography of a field.