Projecting date palm distribution in Iran under climate change using topography,physicochemical soil properties,soil taxonomy,land use,and climate data

This study set out to model potential date palm distribution under current and future climate scenarios using an emission scenario, in conjunction with two different global climate models (GCMs): CSIRO-Mk3.0 (CS), and MIROC-H (MR), and to refine results based on suitability under four nonclimatic parameters. Areas containing suitable physicochemical soil properties and suitable soil taxonomy, together with land slopes of less than 10° and suitable land uses for date palm (Phoenix dactylifera) were selected as appropriate refining tools to ensure the CLIMEX results were accurate and robust. Results showed that large regions of Iran are projected as likely to become climatically suitable for date palm cultivation based on the projected scenarios for the years 2030, 2050, 2070, and 2100. The study also showed CLIMEX outputs merit refinement by nonclimatic parameters and that the incremental introduction of each additional parameter decreased the disagreement between GCMs. Furthermore, the study indicated that the least amount of disagreement in terms of areas conducive to date palm cultivation resulted from CS and MR GCMs when the locations of suitable physicochemical soil properties and soil taxonomy were used as refinement tools.     

Attribution of SST variability in global oceans and the role of ENSO

Based on a novel design of coupled model simulations where sea surface temperature (SST) variability in the equatorial tropical Pacific was constrained to follow the observed El Niño—Southern Oscillation (ENSO) variability, while rest of the global oceans were free to evolve, the ENSO response in SSTs over the other ocean basins was analyzed. Conceptually the experimental setup was similar to discerning the contribution of ENSO variability to interannual variations in atmospheric anomalies. A unique feature of the analysis was that it was not constrained by a priori assumptions on the nature of the teleconnected response in SSTs. The analysis demonstrated that the time lag between ENSO SST and SSTs in other ocean basins was about 6 months. A signal-to-noise analysis indicated that between 25 and 50 % of monthly mean SST variance over certain ocean basins can be attributed to SST variability over the equatorial tropical Pacific. The experimental setup provides a basis for (a) attribution of SST variability in global oceans to ENSO variability, (b) a method for separating the ENSO influence in SST variations, and (c) understanding the contribution from other external factors responsible for variations in SSTs, for example, changes in atmospheric composition, volcanic aerosols, etc.     

Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2

Subseasonal forecast skills and biases of global summer monsoons are diagnosed using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2. Predictions for subseasonal variability of zonal wind and precipitation are generally more skillful over the Asian and Australian monsoon regions than other monsoon regions. Climatologically, forecasts for the variations of dynamical monsoon indices have high skills at leads of about 2 weeks. However, apparent interannual differences exist, with high skills up to 5 weeks in exceptional cases. Comparisons for the relationships of monsoon indices with atmospheric circulation and precipitation patterns between skillful and unskillful forecasts indicate that skills for subseasonal variability of a monsoon index depend partially on the degree to which the observed variability of the index attributes to the variation of large-scale circulation. Thus, predictions are often more skillful when the index is closely linked to atmospheric circulation over a broad region than over a regional and narrow range. It is also revealed that, the subseasonal variations of biases of winds, precipitation, and surface temperature over various monsoon regions are captured by a first mode with seasonally independent biases and a second mode with apparent phase transition of biases during summer. The first mode indicates the dominance of overall weaker-than-observed summer monsoons over major monsoon regions. However, at certain stages of monsoon evolution, these underestimations are regionally offset or intensified by the time evolving biases portrayed by the second mode. This feature may be partially related to factors such as the shifts of subtropical highs and intertropical convergence zones, the reversal of biases of surface temperature over some monsoon regions, and the transition of regional circulation system. The significant geographical differences in bias growth with increasing lead time reflect the distinctions of initial memory capability of the climate system over different monsoon regions.     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Biodiversity and seasonal variation of benthic macrofauna in Minicoy Island, Lakshadweep, India

From the Indian coast only limited data are available on the benthic fauna of the seagrass communities. In this study, seasonal variation in the distribution of macrobenthos and influence of environmental parameters was explored at four seagrass beds and two mangrove stations along the Minicoy Island, Lakshadweep, India, from September 1999 to August 2001. A total of 160 macrobenthic species from eight major groups represented the macrofauna of the Minicoy Island. Of the identified taxa, molluscs 70（gastropods 41.46%, bivalves 7.5%）, polychaetes 27（16.88%）, crustaceans 30（18.75%）, echinoderms 11（6.88%） and remaining others. Average seasonal abundance of benthic macrofauna ranged from 219 to 711 ind./m2, species diversity varied from 1.45 to 3.64 bits per individual, species richness index ranged from 4.01 to 26.17, evenness 0.69 to 1.66. In general, the higher abundance and species diversity was noticed in southern seagrass stations and northern seagrass stations, but in the mangrove stations comparatively low species diversity was observed. Three-way analysis of variance indicated that all communities resulted as being significantly different between seagrass and mangrove station, mainly when the seasonal interaction was considered. Multivariate analyses were employed to help define benthic characteristic and the relationship between environmental parameters at the six monitoring stations. Results of cluster analyses and multidimensional scale plot suggest that for mangrove region, different physiographic provinces, lower salinity, dissolved oxygen and sediment biotic structure have a higher influence on the species composition and diversity than other oceanographic conditions.     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

Landscape characterization of Sariska National Park (India) and its surroundings

Landscape characterization gives an overall information on the status of Land Use and Land Cover(LULC),changes in its composition and the impact of natural and human influences operating at different spatial and temporal scales.This information can be used to monitor changes in natural forest resources and protected areas,delineate potential conservation areas and can serve in effective management of ecologically fragile landscapes.In the present study,geo-spatial tools were used to characterize the landscape of Sariska National Park and its surroundings.Satellite data was used to prepare LULC maps for 1989 and 2000,change detection analysis and computation of landscape metrics.Climatic data,field records and modeling tools were used to map the po-tential spread of two invasive species,Prosopis juliflora and Adhatoda vasica.The results show that the forest area increased from 1989 to 2000,indicating better management practices.Landscape metrics(PAFRAC,PLADJ and AI)also support this argument.Improvements in the degraded forest can further enhance this effect.The entire reserve however is suitable for the invasion of P.juliflora and A.vasica but is more pronounced in Boswellia serrata and Anogeissus pendula-Acacia catechu(open)forests.A detailed landscape characterization map can help forest managers to make important policy decisions concerning issues such as in-vasive species.     

Evaluation of the Diurnal Cycle in the Atmospheric Boundary Layer Over Land as Represented by a Variety of Single-Column Models: The Second GABLS Experiment

We present the main results from the second model intercomparison within the GEWEX (Global Energy and Water cycle EXperiment) Atmospheric Boundary Layer Study (GABLS). The target is to examine the diurnal cycle over land in today??s numerical weather prediction and climate models for operational and research purposes. The set-up of the case is based on observations taken during the Cooperative Atmosphere-Surface Exchange Study-1999 (CASES-99), which was held in Kansas, USA in the early autumn with a strong diurnal cycle with no clouds present. The models are forced with a constant geostrophic wind, prescribed surface temperature and large-scale divergence. Results from 30 different model simulations and one large-eddy simulation (LES) are analyzed and compared with observations. Even though the surface temperature is prescribed, the models give variable near-surface air temperatures. This, in turn, gives rise to differences in low-level stability affecting the turbulence and the turbulent heat fluxes. The increase in modelled upward sensible heat flux during the morning transition is typically too weak and the growth of the convective boundary layer before noon is too slow. This is related to weak modelled near-surface winds during the morning hours. The agreement between the models, the LES and observations is the best during the late afternoon. From this intercomparison study, we find that modelling the diurnal cycle is still a big challenge. For the convective part of the diurnal cycle, some of the first-order schemes perform somewhat better while the turbulent kinetic energy (TKE) schemes tend to be slightly better during nighttime conditions. Finer vertical resolution tends to improve results to some extent, but is certainly not the solution to all the deficiencies identified.     

Simulations of Cyclone Sidr in the Bay of Bengal with a high-resolution model: sensitivity to large-scale boundary forcing

The predictability of Cyclone Sidr in the Bay of Bengal was explored in terms of track and intensity using the Advanced Research Hurricane Weather Research Forecast (AHW) model. This constitutes the first application of the AHW over an area that lies outside the region of the North Atlantic for which this model was developed and tested. Several experiments were conducted to understand the possible contributing factors that affected Sidr??s intensity and track simulation by varying the initial start time and domain size. Results show that Sidr??s track was strongly controlled by the synoptic flow at the 500-hPa level, seen especially due to the strong mid-latitude westerly over north-central India. A 96-h forecast produced westerly winds over north-central India at the 500-hPa level that were notably weaker; this likely caused the modeled cyclone track to drift from the observed actual track. Reducing the model domain size reduced model error in the synoptic-scale winds at 500?hPa and produced an improved cyclone track. Specifically, the cyclone track appeared to be sensitive to the upstream synoptic flow, and was, therefore, sensitive to the location of the western boundary of the domain. However, cyclone intensity remained largely unaffected by this synoptic wind error at the 500-hPa level. Comparison of the high resolution, moving nested domain with a single coarser resolution domain showed little difference in tracks, but resulted in significantly different intensities. Experiments on the domain size with regard to the total precipitation simulated by the model showed that precipitation patterns and 10-m surface winds were also different. This was mainly due to the mid-latitude westerly flow across the west side of the model domain. The analysis also suggested that the total precipitation pattern and track was unchanged when the domain was extended toward the east, north, and south. Furthermore, this highlights our conclusion that Sidr was influenced from the west side of the domain. The displacement error was significantly reduced after the domain size from the western model boundary was decreased. Study results demonstrate the capability and need of a high-resolution mesoscale modeling framework for simulating the complex interactions that contribute to the formation of tropical cyclones over the Bay of Bengal region.