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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

# Hotter deserts and the impending challenges for the spiny-tailed lizard in India [https://doi.org/10.5061/dryad.x95x69psd](https://doi.org/10.5061/dryad.x95x69psd) This dataset encompasses all the data utilized for analysis within the manuscript. ## Description of the data and file structure \################################################################################################################################################## Field body temperature This datafile contains the data collected from thermal loggers on lizards from 15 March 2021 to 23 May 2021. \* Lizard_ID: Identifies individual lizards \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Temp.body: Calibrated skin temperature of the lizard in °C \################################################################################################################################################### Operative temperature habitat This datafile contains the data collected from copper models in different microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Burrow: Operative temperature in burrow in °C \* Open: Operative temperature in open in °C \**Cells with "null" suggest missing data as we removed copper models from the field on rainy/overcast days. \################################################################################################################################################### Operative temperature site This datafile contains the data collected from copper models in open microhabitat from two sampling sites. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_S: Operative temperature in open collected from Sam sampling site in °C \* Open_B: Operative temperature in open collected from Bedhiya sampling site in °C \################################################################################################################################################### Operative temperature size This datafile contains the data collected from copper models of two different sizes in the three microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_ad: Operative temperature in open collected using an average adult STL sized copper model in °C \* Open_juv: Operative temperature in open collected using an average juvenile STL sized copper model in °C \* Bur_ad: Operative temperature in burrow collected using an average adult STL sized copper model in °C \* Bur_juv: Operative temperature in burrow collected using an average juvenile STL sized copper model in °C \################################################################################################################################################### TBAE This datafile contains the data collected from focal sampling and the data derived from operative temperatures using the TBAE algorithm. \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* Activity_test: Data from focal sampling indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \* Activity_algorithm: Data from TBAE algorithm indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \################################################################################################################################################### Locomotor performace This datafile contains data collected by analysing performance run videos. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* body.temp: Cloacal temperatutre of the lizard in °C \* Speed(m/s): Speed of the lizard in m/s \################################################################################################################################################### Critical thermal limits This datafile contains the data collected in the laboratory about the critical thermal limits of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* CTmax: Critical Thermal Maxima of the lizard in °C \* CTmin: Critical Thermal Minima of the lizard in °C \################################################################################################################################################### Preferred Temperature This datafile contains the data collected in the laboratory about the preferred temperature of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Q25: First quartile lower quartile 25th percentile of the preferrence data for the lizard in °C \* Median: Second quartile median 50th percentile of the preferrence data for the lizard in °C \* Q75: Third quartile upper quartile 75th percentile of the preferrence data for the lizard in °C \################################################################################################################################################### Scan Survey This datafile contains the data collected in the field field using scan sampling. \* Open.temp: Operative temperature in open in °C \* Burrow: Proportion of individuals inside burrows \* Basking: Proportion of individuals basking \* Foraging: Proportion of individuals foraging \* Mating: Proportion of individuals engaged in courtship/mating \* Territorial: Proportion of individuals engaged in territorial behavior (fight/intimidation) \* Total: Proportion of individuals active \################################################################################################################################################## Calibration This datafile contains the data collected to calibrate skin and copper model temperatures to cloacal temperatures. \* Lizard_ID: Identifies individual lizards \* Cloacal: Cloacal temperature of the lizard in °C \* Skin: Skin temperature of the lizard in °C \* Copper_model: Copper_model temperature in °C \################################################################################################################################################## ## Code/Software All data was analysed using R 4.1.0. Ectotherms are particularly vulnerable to climate change, especially those living in extreme areas, such as deserts, where species are already thermally constrained. Using the vulnerable herbivorous lizard, Saara hardwickii, as a model system, we used a multi-pronged approach to understand the thermal ecology of a desert agamid and the potential impacts of rising temperatures. Our data included field-based measures of operative temperatures, body temperatures, and activity, as well as lab-based measures of thermal limits, preferences, and sprint speed. As expected, the temperature dependence of locomotor performance and foraging activity was different, and in the worst-case global warming scenario (SSP5-8.5), potential sprint speed may decrease by up to 14.5% and foraging activity may decrease by up to 43.5% by 2099. Burrows are essential thermal refuges, and global warming projections suggest that S. hardwickii may be restricted to burrows for up to 9 hours per day by 2099, which would greatly limit critical activities, like foraging and seeking mating opportunities. Overall, we show that key information on thermal ecology, including temperature-sensitive behaviours in the wild, is necessary to understand the multiple ways in which increasing temperatures may influence ectothermic vertebrates, especially for species like S. hardwickii which are already vulnerable to environmental change. Detailed data collection methodology has been provided in the manuscript.

Characterization of glacier changes in the surface area, terminus, equilibrium line altitude (ELA), elevation, and velocity was worked out for the Jankar Chhu Watershed (JCW) of Lahaul Himalaya using freely available satellite remote sensing data and the limited number of field observations. We studied changes using Corona (1971), Landsat (1993‒2017), Sentinel 2A (2016), the SRTM Digital Elevation Model (DEM; 2000), and the global TanDEM‒X DEM (2014). Our results showed that changes in glacier area (‒14.7 ± 4.3 km²), terminus (‒4.7 ± 0.4 m a¯¹), and ELA (~ 20 m rise) between 1971 and 2016 are smaller than previously reported. Glacier lake area increased by ~0.3 km² during 1971‒2016. An intricate pattern of mass changes across the JCW was observed, with surface lowering on an average of ‒0.7 ± 0.4 m a¯¹ which equates to a geodetic mass balance of ‒0.6 ± 0.4 m w.e. a¯¹ during 2000‒14. The computed glacier surface velocities (1971‒2017) reveal nearly stagnant debris-covered ablation zone but the dynamically active main trunk. The present study provides valuable insights into the recent multiparameter glacier variations, which are of critical importance to assess the future glacier dynamics on a regional scale in areas like the present one.

Project: High Resolution dynamically downscaled CMIP5 climate data over India - The datasets in this project are developed as a part of a project associated with the Indian Institute of Technology Delhi, India, and the National Mission for Clean Ganga, Government of India. We have dynamically downscaled a coarser resolution CMIP5 GCM (CESMv1) climate data using the Weather Research and Forecasting (WRF) model for the current (2006-2015) and future (2091-2100) RCP8.5 emission scenario to produce a 10km resolution dataset over India. This dataset is expected to be of massive value to fine-scale regional modelling based climate change adaptive and mitigative studies in fields of water resources, energy, agriculture, and forestry over India. This project is financially supported by the National Mission for Clean Ganga (NMCG), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India, through grant number TE-16015/02/2019/NMCG. Summary: The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

The cloud computing industry has set new goals for better service delivery and deployment, so anyone can access services such as computation, application, and storage anytime. Cloud computing promises new possibilities for approaching sustainable solutions to deploy and advance their services in this distributed environment. This work explores energy-efficient approaches and how cloud-based architecture can reduce energy consumption levels amongst enterprises leveraging cloud computing services. Adopting cloud-based networking, database, and server machines provide a comprehensive means of achieving the potential gains in energy efficiency that cloud computing offers. In energy-efficient cloud computing, virtualization is one aspect that can integrate several technologies to achieve consolidation and better resource utilization. Moreover, the Green Cloud Architecture for cloud data centers is discussed in terms of cost, performance, and energy consumption, and appropriate solutions for various application areas are provided.

Electric Vehicles have several potential advantages over traditional internal combustion engine vehicles. They promise zero emission and efficiency. State of charge of a battery is considered as one of the important parameters of Lead acid battery. Study has been done and identified that temperature is a main factor that affects the state of charge of the battery and some measures are to be taken to improve its performance. Unscented kalman filter plays a vital role in estimating the real state of charge of a lead acid battery. Testing of lead acid battery has been done in real-time. Unscented kalman filter code is implemented in MATLAB environment and the state of charge has been estimated for the degrading battery.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CCCR-IITM.IITM-ESM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles. International Journal of Sustainable Energy Planning and Management, Vol 7 (2015)