Climate Data Sources Research Articles

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count – all proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

Applicability of CHIRPS-based Pitman model for simulation of climate change flows

Hydrological modelling and continuous monitoring are important for the management of water resources, especially with intensified climate variability. Effective water management is reliant on the availability of climate data and simulations of future projections of the available water resources. The increased likelihood of climatic extremes has made effective water management challenging, especially for vulnerable data-scarce catchments that are highly reliant on natural water resources and have low adaptive capacities. However, advancements in remote sensing have availed alternative sources of climate data for hydrological studies of such catchments. This research evaluated the applicability of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) in simulating climate change flows. Climate change data of 11 Global Circulatory Models (GCMs), enforced by Representative Concentration Pathways (RCP) 4.5 & 8.5 were downloaded and downscaled to represent near (2021–2060) and far (2061–2099) future scenarios. Climate change signals were derived by comparing future scenarios with the present-day period (1981–2019). Climate change signals were then used to adjust input climate data for the Pitman model. The Pitman model was calibrated and projected future flows were simulated for study catchments in drainage regions B, V and G of South Africa. Climate change simulation results demonstrated projected decreases of between 1.49% and 29.96% for the near future scenarios & between 0.45% and 40.31% for the far future scenarios in all the study catchments. Simulated flows for drainage region B recorded contrasting increasing projections of between 17.18% and 24.88% (RCP 4.5) for the near future scenarios while drainage region G also illustrated contrasting increasing projections of between 6.23% and 7.67% (RCP 4.5) for the far future scenarios. Generally, the results of simulated climate change flows based on observed rainfall and CHIRPS data demonstrated decreasing future flows for the South African case study catchments, corroborating with previous studies in projecting a drier future. This research hence recommends the application of CHIRPS estimates as a suitable substitute to dwindling observed rainfall data in the simulation of climate change flows for data-scarce catchments using the Pitman model.

Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change

Understanding microclimate spatial variability is crucial for sustainable and optimised grape production within vineyard plots. By employing a combination of a microclimate model (NicheMapR) and multiple climate data sources, this study aimed to achieve microclimatic analysis in two vineyard plots, Quinta do Bomfim (northern Portugal) and Herdade do Esporão (southern Portugal). This approach provides an innovative 10 m spatial resolution for climate variables. This study incorporated local station hourly data with quantile mapping bias correction on the ERA5-land data. The microclimate model output was employed to perform bias correction on a EURO-CORDEX model ensemble. Climate extreme and bioclimatic indices specifically targeted to viticulture were calculated for each vineyard plot. The 10 m scale was analysed to identify potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables for grape cultivation within each specific plot. The significance of microclimate analyses was higher in areas with intricate topography, while in areas with smooth slopes, the variation of climatic variables was determined to be negligible. There was a projected increase in the median temperature of approximately 3.5 °C and 3.6 °C and a decrease in precipitation of approximately 98 mm and 105 mm in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing a future scenario for the period 2071–2100 against the historical period (1981–2010). Hence, this study offers a comprehensive and future-oriented method for analysing microclimates in vineyard plots. By incorporating geospatial data, ERA5-land data, and the microclimate NicheMapR model, this research aimed to enhance the understanding of current microclimates and future climate scenarios for viticulturists.

Bioclimatic zoning for building performance using tailored clustering method and high-resolution climate data

Building environments are specific and complex bioclimatic systems. Thus, well-suited climate classification methods for buildings are essentially needed to develop building design guidelines and standards. To address it, the present research introduces a novel fit-for-purpose clustering method for bioclimatic zoning based on the hygrothermal and energy performance of buildings. This bioclimatic zoning was developed to update the Brazilian standard and has been validated across various climates and building typologies (residential and commercial) in Brazil. In a preliminary analysis, three classification methods were developed using K-means and Decision Tree to classify climates according to building performance. Subsequently, a final bioclimatic zoning method was developed using a tailored version of the best method designed for real-world applications (Decision Tree) in the Brazilian context. The performance of the bioclimatic zoning achieved was compared with three existing climate classifications: Köppen-Geiger, ASHRAE 169-2020, and ABNT-NBR 15220-3 (Brazilian Standard). The results showed that the new bioclimatic zoning method outperformed the existing ones to cluster the building performance indicators. Moreover, high-resolution spatial climate databases, such as NASA-POWER, CRU, and ERA5-Land, were processed and analyzed to be employed in locations without properly measured data. Three metamodels of climate indicators were developed and compared with these databases to select the most accurate climate data sources. Finally, these databases were employed to classify all 5570 Brazilian municipalities according to the final bioclimatic zoning, which enabled the development of an accurate and high-resolution map.

Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations

ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration..

Using Data Mining Techniques to Develop Climate Change Mitigation and Adaptation Strategies in Nigeria

Climate change is a severe and growing challenge facing Nigeria, with significant impacts on the country's economy, environment, and society. To cope with this challenge, there is a need for climate change mitigation and adaptation strategies that are evidence-based and tailored to the country's unique vulnerabilities and opportunities. This study aimed to develop such strategies using data mining techniques. A range of climate and non-climate data sources were explored to identify key drivers of climate change in Nigeria and their associated impacts on different sectors, such as agriculture, water resources, and energy. Subsequently, data mining algorithms, including decision trees, clustering, and association rule mining, were applied to model and analyze the complex relationships between these drivers and their impacts on the sectors. The study found that a combination of mitigation and adaptation measures could be effective in reducing the severity of climate change impacts in Nigeria. These measures include promoting the use of renewable energy sources, improving water-use efficiency, and developing climate-smart agricultural practices. The data mining techniques used in this study proved useful for identifying these measures and for predicting their effectiveness under different scenarios. The results of this study provide important insights to policymakers and practitioners in Nigeria and other countries facing similar climate change challenges. It also highlights the potential of data mining techniques for developing climate change mitigation and adaptation strategies that are evidence-based, effective, and tailored to specific local contexts. Overall, this study contributes to the growing body of literature on leveraging data mining and machine learning techniques for addressing complex environmental and societal challenges.

Empowering urban climate resilience and adaptation: Crowdsourcing weather citizen stations-enhanced temperature prediction

The growing impact of climate change, including extreme weather events, represents a significant challenge for humanity. With most of the world's population living in urban areas, the urban heat island effect and anthropogenic heat contribute to elevated city temperatures. This increase in urban warming threatens human health and demands a deeper understanding of thermal distribution in urban environments. Collecting accessible and widespread temperature data in urban areas is essential to address this challenge. This study aims to develop a methodology for anticipating temperature distribution in urban environments, leveraging Citizen Weather Stations (CWS) as valuable crowdsourcing data sources. The ultimate goal is to create a predictive model that estimates urban temperatures based on government meteorological station forecasts, improving urban planning, regulating temperature-based routes, preventing health issues in vulnerable populations, and enhancing urban livability. The methodology is divided into three fundamental stages: data acquisition through CWS with citizen collaboration, the development and evaluation of optimal forecast models based on government weather stations (SWS) data, and its exploitation in terms of utility and applicability. This methodology encompasses data collection and filtering to ensure its usefulness and implement reliable models. The resulting tool facilitates informed decision-making and precise seasonal event planning in urban environments, effectively addressing the challenges of climate extrapolation and contributing to more effective adaptation and mitigation strategies in climate change and heatwaves. The results obtained probe the feasibility of using CWS to predict temperatures in urban environments, which has been demonstrated accurately. This is a significant achievement, as CWS has proven to be a reliable source of climate data for this context. Also, the filtering process described and applied to the case study has proven effective, discarding approximately 34.87 % of the data. This is achieved by detecting and eliminating anomalies, considering station availability, and adhering to specific quality criteria. Finally, the developed prediction model has demonstrated its ability to optimally estimate urban temperatures, utilizing climate prediction data provided by government weather stations (SWS). The model performance indicators support this claim. For the linear regression model, a Mean Squared Error (MSE) of 2.177 and an R-squared (R2) of 0.960 are obtained, while for the neural network, an MSE of 1.284 and an R2 of 0.976 are achieved.

Evaluating the freeze-thaw vulnerability of soapstone monuments and geoheritage sites in the Parco del Paradiso (Chiavenna, Italy).

This study investigates the vulnerability of soapstone monuments and ancient quarries in the Parco del Paradiso, Chiavenna, to freeze-thaw cycles. It employs climate-based damage functions to assess the risk of weathering-induced damage. A crucial aspect of the assessment involves the study of the homogeneity and trends in the selected climate data sources in the area of the case study. These data sources are ground-based weather stations, and in-situ installed temperature-humidity data loggers. The findings of this contribution underscore the significance of appropriately chosen reference climatic series and validation tests in ensuring the accuracy of the climate signal to analyze freeze-thaw statistics for specific cultural heritage sites. These insights are essential not only for supporting reliable assessment of the vulnerability of these invaluable heritage sites, which have played a vital role in local architecture and trade since Roman times, and for the successive development of long-term preventive conservation and maintenance strategies.

Water Balance, Climate Classification and Empirical Distribution of the Probability of Rain for the Region of Tangará da Serra – MT

Objective: The present work aimed to describe the Climatological Water Balance (BHC), carry out the Climate Classification (CC) and evaluate the Empirical Probability (EP) of rainfall occurrence for the region of Tangará da Serra – MT. Theoretical Frame: This study used the following main citations as a theoretical basis: National Water Agency (ANA) and National Institute of Meteorology (INMET), source of climate data; Thornthwaite & Mather (1955) for the climatological water balance methodology; Thornthwaite (1948) and Koppen (1900) for climate classification; and Thom (1966) for the methodology of probability of occurrence and return period of rain. Method: To carry out the study, a series of 31 years of climate data was used, covering the period from 1989 to 2019, obtained from the National Water Agency (ANA) and the National Institute of Meteorology (INMET). Filling gaps in the data was carried out using the Regional Weighting method, according to the methodology of Mello et al. (2017). The climatological water balance was estimated according to Thornthwaite & Mather (1955). The climate classification was obtained using the methodologies of Thornthwaite (1948) and Koppen (1900). The probability of occurrence and the period of return of rain were achieved using the methods of Thom (1966). Results and Conclusions: Through studies carried out, it was found that the average annual precipitation in Tangará is 1603 mm and the average annual air temperature is 24.5 °C. The months of May, June, July, August and September present a water deficit, while in the months of December, January, February, March and April there is a water surplus. According to Thornthwaite's methodology, the region has a humid climate, with moderate water deficiency in winter, megathermal and with potential evapotranspiration in the summer less than 48% of the annual potential evapotranspiration, whereas according to Koppen's methodology, the location was classified as megathermal or tropical with drought in the winter. The probability of rainfall less than the maximum average of 47.7 mm is 93.3% and its return time is approximately 15 years. Implications of the research: This research is classified as of great importance for the purposes of the Department of Agricultural Engineering of the State University of Mato Grosso, Campus of Nova Mutum and Tangará da Serra - MT, as well as for the entire academic and scientific community and interested rural producers. in climatic data, water balance and climate of the studied region. Originality and value: This study aimed to show the academic and scientific community and rural producers the behavior of climatic variables throughout the year in the municipality of Tangará da Serra – MT, where this information can be used for agricultural planning in the region and also serve as a basis for future studies.

Differing sensitivity of species distribution modelling algorithms to climate data source

The increasing availability of contemporary climate databases calls for comparative studies testing their performance in ecological modelling. We evaluated how contemporary climate data sources interact with the modelling algorithm and affect the predictive accuracy of Species Distribution Models (SDMs). We modelled the distribution of 37 woody plants in the Iberian Peninsula using three global climate databases: WorldClim (WC), CHELSA (CH) and MERRAclim (MER); a regional climate database (the Iberian Climate Atlas, ICA), and three modelling algorithms: Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and Partial Least Squared Regressions (PLSRs). The predictive capacity of the models was estimated by performing an external evaluation while controlling for extrapolation issues. The climate database source variable and its interaction with the modelling algorithm variable showed significant effects on a model's predictive performance. Post hoc tests revealed that MER produced the lowest discrimination capacity scores, especially using GLMs as algorithm. PLSR models were significantly better than GLMs when MER was used. GAMs and PLSR models with MER were significantly worse than some ICA and CH models but not worse than any of the WC models. These results indicate that contemporary climate data sources need to be considered as a seedbed of uncertainty in SDMs and that poorly flexible algorithms are unable to deal with suboptimal data. CH is a reliable global climate database and PLSR is a technique worth considering in SDMs. The best practice is to select the most accurate climate data available and to choose the algorithm based on the purpose of the study and on context-dependent technical details.

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BackgroundThe ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.MethodsA series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.ResultsThe performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller’s calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.ConclusionsThis comprehensive analysis suggests that there is no single ‘best practice’ climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.Graphical abstract

Assessment of Outer Space Events on Troposphere and Climate Change over Iraq

The aim of the study is to investigate the effects of space weather on the troposphere, where our climate exists. This work is useful to give us an idea of ​​the interaction between solar activity and some meteorological parameters. The sunspot number (SSN) data were extracted from the World Data Center for the production, preservation, and dissemination of the international sunspot number (SILSO), top net solar radiation (TSR) and temperature 2 meters from the ERA5 model of the Copernicus Climate Change Service (C3S) from the Climate Data Store with 0.25 grid Resolution, providing a rich source of climate data for researchers. This study was conducted from 2008 to 2021 (solar cycle 24 and the beginning of 25) over Iraq located within latitude (38°N-28°S) and longitude (38°W- 49°E). The results that have been reached were a decrease in solar activity in solar cycle 24, accompanied by an increase in the length of the solar cycles, and there was a direct proportion between TSR and temperature. TSR and temperature had almost similar spatial distribution over Iraq. The northern regions received the least amounts, while from the middle toward the southeast regions, they received the highest amount of TSR and temperature over Iraq. There was a reverse regression between sunspot numbers with TSR and positive regression with temperature, but the correlation was not significant.

Choice of climate data influences predictions for current and future global invasion risks for two Phelsuma geckos

Invasion risks may be influenced either negatively or positively by climate change, depending on the species. These can be predicted with species distribution models, but projections can be strongly affected by the source of the environmental data (climate data source, Global Circulation Models GCM and Shared Socio-economic Pathways SSP). We modelled the distribution of Phelsuma grandis and P. laticauda, two Malagasy reptiles that are spreading globally. We accounted for drivers of spread and establishment using socio-economic factors (e.g., distance from ports) and two climate data sources, i.e., Climatologies at High Resolution for the Earth’s and Land Surface Areas (CHELSA) and Worldclim. We further quantified the degree of agreement in invasion risk models that utilised CHELSA and Worldclim data for current and future conditions. Most areas identified as highly exposed to invasion risks were consistently identified (e.g. in Caribbean and Pacific Islands). However, projected risks differed locally. We also found notable differences in quantitative invasion risk (3% difference in suitability scores for P. laticauda and up to 14% for P. grandis) under current conditions. Despite both species native distributions overlapping substantially, climate change will drive opposite responses on invasion risks by 2070 (decrease for P. grandis, increase for P. laticauda). Overall, projections of future invasion risks were the most affected by climate data source, followed by SSP. Our results highlight that assessments of current and future invasion risks are sensitive to the climate data source, especially in islands. We stress the need to account for multiple climatologies when assessing invasion risks.

Evaluation and uncertainty assessment of weather data and model calibration on daily streamflow simulation in a large-scale regulated and snow-dominated river basin

The choice of proper input data and suitable calibration schemes is challenging in the hydrological modeling of higher-latitude watersheds with their unique hydro-climatic conditions. Based on the Soil and Water Assessment Tool (SWAT) and Sequential Uncertainty Fitting Program (SUFI2) of the SWATCUP tool, our study revisits various climate data and calibration schemes, and develops a more automatic calibration framework to thoroughly assess model performance in simulating Snow Water Equivalent (SWE) and resulting streamflow in a cold region watershed, the Peace River Basin (PRB) in western Canada. We evaluated the effects of (1) the observed and two widely used gridded climate data sources; (2) unrealistic snow parameter combinations arising from SUFI2-SWATCUP parameter sampling; and (3) ‘calibration schemes’ including the choice of (i) objective function, (ii) number of simulations in each calibration iterations, (iii) regional versus global parameterization and calibration schemes, and (iv) single-variable (streamflow) versus multi-variable calibration (streamflow and SWE). Model results showed that the gridded ERA5 data was the best climate input for driving the SWAT model in PRB. A modified SUFI2-SWATCUP method to automatically remove unrealistic snow parameter combinations outperformed the standard SUFI2-SWATCUP, and it significantly reduced the total number of simulations. Compared with single-variable calibration using streamflow, an automatic multi-variable calibration using streamflow and SWE was shown to effectively limit the uncertainty of snow-related parameters and provide better prediction of snow-affected spring streamflow. The framework developed in our study can help reduce uncertainties in the hydrological modeling of large-scale, regulated, and snow-dominated watersheds in higher latitudes and can help the development of reliable strategies for water resources planning and management.

Datos climáticos y prácticas recomendadas para proyectar cambios en la distribución de especies

Climate change is a complex phenomenon that affects all socio-environmental systems, manifesting itself uniquely in different regions. To comprehensively understand and project the extent of its impact, it is crucial to engage in a collaborative effort involving specialists from various disciplines. The international scientific community has made significant progress in developing diverse datasets for studying climate variability and change, along with the formulation of suitable methodologies for their analysis. However, there is a tendency within certain disciplines to rely solely on a single source of climate data, either for the sake of simplicity or due to its compatibility with desired resolution or availability requirements. In this context, climatologists express their apprehension regarding the uncritical utilization of climate databases. It is from this concern that the present article aims to provide a comprehensive description of the scope and limitations associated with the available databases. Furthermore, the article addresses the issue of uncertainty in climate projections and offers guidance on how to effectively employ climate data in conducting experiments pertaining to species distribution, while considering the inherent uncertainty associated with such data. Emphasis is placed on the significance of conducting multiple experiments utilizing independent climate projections, as well as utilizing statistical tools to draw conclusions based on a range of potential solutions. By adopting this approach, researchers can more effectively assess the impact of climate change and its implications for species distribution patterns.

Quantifying uncertainty and sensitivity in climate risk assessments: Varying hazard, exposure and vulnerability modelling choices

Open-source climate risk assessment platforms allow for accessible and efficient estimation of current and future climate risk by combining information about hazard, exposure and vulnerability. Such assessments require making a number of choices, such as which hazard data source to use, and the data and approach taken to represent the exposure and vulnerability. As these choices are, to some extent, subjective, when assessing risk and informing adaptation decisions, alternative options should be considered to understand the uncertainty and sensitivity of risk to uncertain input data and assumptions. We present a novel approach to quantify the uncertainty and sensitivity of risk estimates, using the CLIMADA open-source climate risk assessment platform. This work builds upon a recently developed extension of CLIMADA, which uses statistical modelling techniques to model and stochastically simulate alternative realisations of climate risk, allowing for a richer quantification of climate model ensemble uncertainty. Here, we further analyse the propagation of hazard, exposure and vulnerability uncertainties by varying a number of input factors based on a discrete, scientifically justified set of options. We explore the uncertainty and sensitivity of risk to these variations, using the PAWN (PiAnosi and WagNer) method for global sensitivity analysis, allowing for an attribution of uncertainty to different drivers of the total uncertainty budget. We demonstrate the approach through an application to assess heat-stress risk to outdoor physical working capacity in the UK. In this application, we demonstrate how the risk estimated across plausible input settings better captures uncertainty and extreme outcomes (important for decision making); that all uncalibrated/non-bias-adjusted climate data sources underestimate risk in the recent past (highlighting the need for data calibration); and that when a global warming level framing is used it is the choice of global warming level that this risk is most sensitive to (2 °C or 4 °C warmer than pre-industrial), particularly in the south of the UK. This highlights the importance of mitigating climate change to reduce heat-stress risk.

Using CHIRPS Satellite-Based Data for Spatio-Temporal Variability of Rainfall in Dashti Hawler District – Kurdistan Region of Iraq 1982 – 2019

Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.

Climate data for field trials: onsite micro stations versus ClimateNA

Ecological studies in diverse environmental fields require accurate climate data for point locations that are often distant from reliable public weather stations. “Onsite” micro weather stations can be established directly at research locations, but purchase, establishment, and maintenance costs and data gaps can limit their feasibility. Alternatively, climate data for point locations can be predicted from ClimateNA, a publicly available software package, but the prediction accuracy in remote and mountainous locations is uncertain. We compared ClimateNA predictions with observations from onsite weather stations located at 11 interior spruce provenance trials in British Columbia, Canada. We found that ClimateNA predictions were highly accurate for temperature variables but moderate for precipitation variables when compared with onsite weather data. Growth response functions developed with the two data sources showed similar shapes for temperature variables. Our results suggest that (1) temperature variables can be accurately predicted at remote and mountainous locations using ClimateNA; (2) precipitation variables are more accurately predicted with ClimateNA than with onsite weather stations, which are considerably affected by random factors; and (3) response functions provide an effective, independent tool to assess alternative sources of climate data. Our results recommend the use of ClimateNA over onsite weather stations, except where highly accurate precipitation data are required, in which case, high-quality onsite weather stations must be established and carefully maintained.

Here be dragons: important spatial uncertainty driven by climate data in forecasted distribution of an endangered insular reptile

AbstractThe effect of future climate change is poorly studied in the tropics, especially in mountainous areas, yet species living in these environments are predicted to be strongly affected. Newly available high‐resolution environmental data and statistical methods enable the development of forecasting models, but the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predictive studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies consider potential differences related to the source of climate data and/or do not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and data source (CHELSA vs. Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap, the uncertainty related to data source became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on the spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and data sources. We recommend the use of multiple approaches, including spatial overlap when assessing uncertainty in species distribution models.

Impact of Gridded Weather Data Sources and its Temporal Resolution on Crop Evapotranspiration and Effective Rainfall of Major Crops in Eastern Region of India

Synthetic climate data are being increasingly used as key input in number of climate change impact assessment studies, hydrological assessments, and water resource management projects. The present study quantified the impact of two-gridded climate data sources [India Meteorological Department (IMD) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC)] on estimation of evapotranspiration (ETc ) and effective rainfall (ER) of paddy (Kharif) and wheat (Rabi) crops across four agroecological sub-regions, and compared the results with observed station data. The effect of daily, decadal, and monthly temporal resolutions of climatic data were evaluated on ETc and ER using climatological model CROPWAT 8.0. Results showed that compared to daily resolution, the monthly temporal resolution estimated significantly higher (1.7-4.0%) ETc for wheat crop. Effect of temporal resolution of climate data on ER of both crops was insignificant, implying that the uncertainties in estimation of ER emanating from use of daily, decadal, or monthly temporal resolutions would be within acceptable limits. Across the locations, the CPC-based estimates of paddy ETc deviated from (-)1.3% to 21.6% of the station ETc , while the deviations for wheat ETc were in the range of (-)7.1% to 9.3 per cent. Significant variations from station etc were also observed for CPC (1.7 - 19.4%) and IMD [(-)34.3 - 31.1%] based ETc estimates of wheat ETc . The study recommends that the technological and policy research outputs based on gridded climate data products should be carefully analysed keeping in view the uncertainty accruing on account of use of gridded data products.