Types Of General Circulation Models Research Articles

Spatial niche modelling of five endemic cacti from the Brazilian Caatinga: Past, present and future

AbstractClimate change, together with human activities, impacts on natural and human systems on all continents and poses a major threat to biodiversity, especially in environments with a high rate of endemism and where species are profoundly adapted to specific environmental conditions, as is the case of the seasonally dry tropical forests, noticeably the Caatinga, an exclusively Brazilian biome. The objective of this study was to build spatial niche models of five species of Cactaceae (Arrojadoa penicillata, Brasilicereus phaeacanthus, Pereskia aureiflora, Stephanocereus leucostele and Tacinga inamoena) endemic to the Caatinga and with different traits, to evaluate the impact of climate change on their geographical distribution. The species records and environmental variable values were overlaid on a grid of 6818 cells with 0.5° spatial resolution. Niche models were obtained for five types of general circulation models between ocean and atmosphere and 12 different ecological models. The ensemble ecological niche model was calculated at present and projected to past (last glacial maximum – LGM, 21 000; and mid‐Holocene – Hol, 6000 years ago) and future climate conditions (average of 2080), under the effect of climate change, in the greenhouse gas emission scenario RCP4.5. The distribution pattern of the studied species indicates an area with less environmental suitability in the LGM, followed by an expansion that began in the Hol and continued until the present period. In the future (2080), the models predicted a retraction of areas of environmental suitability, in which P. aureiflora and B. phaeacanthus, given their more restricted, marginal habitat and woody habit, present a great risk of extinction, whilst S. leucostele, A. penicillata and T. inamoena present a smaller reduction in suitable area, partly reflecting their spreading, less woody habit. Regional conservation actions for Cactaceae species and their habitat need to take these findings into account if we are to ensure the survival of these species.

Assessment of potato response to climate change and adaptation strategies

Abstract This study was conducted to simulate the climate change impacts on potato production and evaluate the planting date and variety management as possible climate change adaptation strategies in Isfahan province, Iran. Two types of General Circulation Models (HadCM3 and IPCM4) and three scenarios (A1B, A2 and B1) were employed. Daily climatic parameters were generated by Long Ashton Research Station-Weather Generator (LARS − WG). The SUBSTOR-Potato model was used to simulate the baseline and future potato growth and development. Results indicated that LARS-WG and SUBSTOR-Potato had an appropriate accuracy to simulate climatic and growth parameters of potato. Simulated results showed that the maximum leaf area index (LAI), days to tuber initiation (DTTI), days to harvest (DTH) and fresh tuber yield of evaluated variety will be declined as affected by future climate change. Based on the simulation results, delayed planting date (31 May) would increase tuber yield under future climatic conditions. In the contrary, early planting (30 April) would accelerate harmful effects of climate change on potato yield. The medium and early maturing varieties showed a better tuber yield under climate change conditions than common (delayed maturing) variety. In essence, early maturing variety and delayed planting date are reported as the most efficient agronomical approaches for mitigating harmful effects of climate change and proposed to be considered in designing and managing potato ecosystems of the region for future climatic conditions. Generally, our results highlight the importance of considering early maturing variety and delayed planting date as the efficient agronomical approaches for mitigating harmful effects of climate change on potato production.

Adaptation of maize to climate change impacts in Iran

Adaptation is a key factor for reducing the future vulnerability of climate change impacts on crop production. The objectives of this study were to simulate the climate change effects on growth and grain yield of maize (Zea mays L.) and to evaluate the possibilities of employing various cultivar of maize in three classes (long, medium and short maturity) as an adaptation option for mitigating the climate change impacts on maize production in Khorasan Razavi province of Iran. For this purpose, we employed two types of General Circulation Models (GCMs) and three scenarios (A1B, A2 and B1). Daily climatic parameters as one stochastic growing season for each projection period were generated by Long Ashton Research Station-Weather Generator (LARS−WG). Also, crop growth under projected climate conditions was simulated based on the Cropping System Model (CSM)-CERES-Maize. LARS-WG had appropriate prediction for climatic parameters. The predicted results showed that the day to anthesis (DTA) and anthesis period (AP) of various cultivars of maize were shortened in response to climate change impacts in all scenarios and GCMs models; ranging between 0.5 % to 17.5 % for DTA and 5 % to 33 % for AP. The simulated grain yields of different cultivars was gradually decreased across all the scenarios by 6.4 % to 42.15 % during the future 100 years compared to the present climate conditions. The short and medium season cultivars were faced with the lowest and highest reduction of the traits, respectively. It means that for the short maturing cultivars, the impacts of high temperature stress could be much less compared with medium and long maturity cultivars. Based on our findings, it can be concluded that cultivation of early maturing cultivars of maize can be considered as the effective approach to mitigate the adverse effects of climate.

Adaptation strategies for maize cultivation under climate change in Iran: irrigation and planting date management

Climate change is affecting the productivity of crops and their regional distribution. Strategies to enhance local adaptation capacity are needed to mitigate climate change impacts and to maintain regional stability of food production. The objectives of this study were to simulate the climate change effects on phenological stages, Leaf Area Index (LAI), biomass and grain yield of maize (Zea mays L.) in the future and to explore the possibilities of employing irrigation water and planting dates as adaptation strategies to decrease the climate change impacts on maize production in Khorasan Razavi province, Iran. For this purpose, we employed two types of General Circulation Models ((United Kingdom Met. Office Hadley Center: HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1). Long Ashton Research Station-Weather Generator (LARS-WG) was used to produce daily climatic parameters as one stochastic growing season for each projection period. Also, crop growth under projected climate conditions was simulated based on the Cropping System Model (CSM)-CERES-Maize. The results of model evaluation showed that LARS-WG had appropriate prediction for climatic parameters. Time period from cultivation until anthesis and maturity were reduced in majority of scenarios as affected by climate change. The results indicated that the grain yield of maize may be reduced (11 % to 38 %) as affected by climate change based on common planting date in baseline and changed (−61 % to 48 %) in response to different irrigation regimes in the future climate change, under all scenarios and times. In general, earlier planting date (1 May) and decreasing irrigation intervals in the anthesis stage (11 applications) caused higher yield compared with other planting dates due to adaption to high temperature. Based on our findings, it seems that management of irrigation water and planting dates can be beneficial for adaptation of maize to climate change in this region.

A multimodel assessment of the climate change effect on the drought severity–duration–frequency relationship

AbstractIn this study, the patterns of past and future drought occurrences in the Seoul region were analysed using observed historical data from the Seoul weather station located in the Korean Peninsula and four different types of general circulation models (GCMs), namely, GFDL:CM2_1, CONS:ECHO‐G, MRI:CGCM2_3_2 and UKMO:HADGEM1. To analyse statistical properties such as drought frequency duration and return period, the Standardized Precipitation Index was used to derive the severity–duration–frequency (SDF) curve from the drought frequency analysis. In addition, a drought spell analysis was conducted to estimate the frequency and change of drought duration for each drought classification. The results of the analysis suggested a decrease in the frequency of mild droughts and an increase in the frequency of severe and extreme droughts in the future. Furthermore, the average duration of droughts is expected to increase. A comparison of the SDF relationship derived from the observed data with that derived via the GCMs indicated that the drought severity for each return period was reduced as drought duration increased and that the drought severity derived from the GCMs was severer than the severity obtained using the observed data for the same duration and return period. Furthermore, among the four types of GCMs used in this study, the MRI model predicted the most severe future drought for the Seoul region, and the SDF curve derived using the MRI model also resulted in the highest degree of drought severity compared with the other GCMs. Copyright © 2012 John Wiley & Sons, Ltd.

Mitigation of climate change impacts on maize productivity in northeast of Iran: a simulation study

Development and evaluation of mitigation strategies are fundamental to manage climate change risks. This study was built on (1) quantifying the response of maize (Zea mays L.) grain yield to potential impacts of climate change and (2) investigating the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two types of General Circulation Models (GCM: (United Kingdom Met Office Hadley Center :HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Long Ashton Research Station-Weather Generator (LARS-WG) was employed for generating the future climate. The Cropping System Model (CSM)-CERES-Maize was used for crop growth simulation under projected climate conditions. The results showed the simulated grain yields of maize gradually would decrease (from −1% to −39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. The simulation results suggested that delayed sowing date from May to June at all study locations, except Sabzevar location is the most effective mitigation option for avoiding thermal stress at end of growth period. In addition, shifting in sowing date to March or April will be beneficial in terms of obtaining higher yields in Sabzevar. Grain yield did not show special trend from north to south of Khorasan Province in the future climate. In general, change of sowing date may be quite beneficial to mitigate climate change impacts on grain yield of maize in northeast of Iran.

Evaluation of the Simulated Interannual and Subseasonal Variability in an AMIP-Style Simulation Using the CSU Multiscale Modeling Framework

Abstract The Colorado State University (CSU) Multiscale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds, and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF has been used to perform a 19-yr-long Atmospheric Model Intercomparison Project–style simulation using the 1985–2004 sea surface temperature (SST) and sea ice distributions as prescribed boundary conditions. Particular focus has been given to the simulation of the interannual and subseasonal variability. The annual mean climatology is generally well simulated. Prominent biases include excessive precipitation associated with the Indian and Asian monsoon seasons, precipitation deficits west of the Maritime Continent and over Amazonia, shortwave cloud effect biases west of the subtropical continents due to insufficient stratocumulus clouds, and longwave cloud effect biases due to overestimation of high cloud amounts, especially in the tropics. The geographical pattern of the seasonal cycle of precipitation is well reproduced, although the seasonal variance is considerably overestimated mostly because of the excessive monsoon precipitation mentioned above. The MMF does a good job of reproducing the interannual variability in terms of the spatial structure and magnitude of major anomalies associated with El Niño–Southern Oscillation (ENSO). The subseasonal variability of tropical climate associated with the Madden–Julian oscillation (MJO) and equatorially trapped waves are particular strengths of the simulation. The wavenumber–frequency power spectra of the simulated outgoing longwave radiation (OLR), precipitation rate, and zonal wind at 200 and 850 mb for time scales in the range of 2–96 days compare very well to the spectra derived from observations, and show a robust MJO and Kelvin and Rossby waves with phase speeds similar to those observed. The geographical patterns of the MJO and Kelvin wave–filtered OLR variance for summer and winter seasons are well simulated; however, the variances tend to be overestimated by as much as 50%. The observed seasonal and interannual variations of the strength of the MJO are also well reproduced. The physical realism of the simulated marine stratocumulus clouds is demonstrated by an analysis of the composite diurnal cycle of cloud water content, longwave (IR) cooling, vertical velocity variance, rainfall, and subcloud vertical velocity skewness. The relationships between vertical velocity variance, IR cooling, and negative skewness all suggest that, despite the coarse numerical grid of the CRM, the simulated clouds behave in a manner consistent with the understanding of the stratocumulus dynamics. In the stratocumulus-to-cumulus transition zone, the diurnal cycle of the inversion layer as simulated by the MMF also bears a remarkable resemblance to in situ observations. It is demonstrated that in spite of the coarse spacing of the CRM grid used in the current version of MMF, the bulk of vertical transport of water in the MMF is carried out by the circulations explicitly represented on the CRM grid rather than by the CRM’s subgrid-scale parameterization.