scholarly journals Re-Evaluating the Climate Factor in Agricultural Land Assessment in a Changing Climate—Saskatchewan, Canada

Land ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samantha Kerr ◽  
Yuliya Andreichuk ◽  
David Sauchyn
Keyword(s):  
Agricultural Land ◽  
Climate Model ◽  
Regional Climate ◽  
Arable Land ◽  
Wheat Yield ◽  
Maximum Temperature ◽  
Regression Equations ◽  
Climate Data ◽  
Climate Factor ◽  
Weather Variables

We established the statistical relationships between seasonal weather variables and average annual wheat yield (Hard Red Spring and Durum wheat: Triticum spp.) for the period of 1979–2016 for 296 rural municipalities (RMs) throughout six soil zones comprising the arable agricultural zone of Saskatchewan, Canada. Controlling climate variables were identified through Pearson’s product moment correlation analysis and used in stepwise regression to predict wheat yields in each RM. This analysis provided predictive regression equations and summary statistics at a fine spatial resolution, explaining up to 75% of the annual variance of wheat yield, in order to re-evaluate the climate factor rating in the arable land productivity model for the Saskatchewan Assessment and Management Agency (SAMA). Historical climate data (1885–2016) and Regional Climate Model (RCM) projections for the growing season (May–August) were also examined to put current climatic trends into longer-term perspective, as well as develop a better understanding of possible future climatic impacts on wheat yield in Saskatchewan. Historical trends demonstrate a decrease in maximum temperature and an increase in minimum temperature and precipitation throughout all soil zones. RCM projections also show a potential increase in temperatures and total precipitation by 5 °C and 10%, respectively. We recommended against a modification of the climate factor rating at this time because (1) any increase in wheat yield could not be attributed directly to the weather variables with the strongest trends, and (2) climate and wheat yield are changing more or less consistently across the zone of arable land, and one soil zone is not becoming more productive than another.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Projected Changes of Precipitation IDF Curves for Short Duration under Climate Change in Central Vietnam

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 33 ◽  
Author(s):  
Nguyen Tien Thanh ◽  
Luca Dutto Aldo Remo
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Data ◽  
Rcp Scenarios ◽  
Central Vietnam ◽  
Idf Curves ◽  
Projected Changes

In future years, extreme weather events are expected to frequently increase due to climate change, especially in the combination of climate change and events of El Niño–Southern Oscillation. This pays special attention to the construction of intensity–duration–frequency (IDF) curves at a tempo-spatial scale of sub-daily and sub-grid under a context of climate change. The reason for this is that IDF curves represent essential means to study effects on the performance of drainage systems, damps, dikes and reservoirs. Therefore, the objective of this study is to present an approach to construct future IDF curves with high temporo-spatial resolutions under climate change in central Vietnam, using the case of VuGia-ThuBon. The climate data of historical and future from a regional climate model RegCM4 forced by three global models MPI-ESM-MR, IPSL-CM5A-LR and ICHEC-EC-EARTH are used to re-grid the resolution of 10 km × 10 km grid spacing from 25 km × 25 km on the base of bilinear interpolation. A bias correction method is then applied to the finest resolution of a hydrostatic climate model for an ensemble of simulations. Furthermore, the IDF curves for short durations of precipitation are constructed for the historical climate and future climates under two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5, based on terms of correlation factors. The major findings show that the projected precipitation changes are expected to significantly increase by about 10 to 30% under the scenarios of RCP4.5 and RCP8.5. The projected changes of a maximum of 1-, 2-, and 3-days precipitation are expected to increase by about 30–300 mm/day. More importantly, for all return periods (i.e., 10, 20, 50, 100, and 200 years), IDF curves completely constructed for short durations of precipitation at sub-daily show an increase in intensities for the RCP4.5 and RCP8.5 scenarios.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals Climate Extremes over the Arabian Peninsula Using RegCM4 for Present Conditions Forced by Several CMIP5 Models

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 675 ◽  
Author(s):  
Almazroui
Keyword(s):  
Land Surface ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Coupled Model ◽  
Climate Extremes ◽  
Maximum Temperature ◽  
Cmip5 Models ◽  
Study Region ◽  
Temperature And Precipitation

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.

scholarly journals The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

Unmixing the regional climate response to recent historical land cover changes in Europe

2020 ◽  
Author(s):  
Bo Huang ◽  
Xiangping Hu ◽  
Geir-Arne Fuglstad ◽  
Xu Zhou ◽  
Wenwu Zhao ◽  
...  
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Energy Budget ◽  
Land Cover Changes ◽  
Climate Response ◽  
Statistical Regression ◽  
Cooling Effects

<p>Land cover changes (LCCs) influence the regional climate because they alter biophysical mechanisms like evapotranspiration, albedo, and surface roughness. Previous research mainly assessed the regional climate implications of individual land cover transitions, such as the effects of historical forest clearance or idealized large-scale scenarios of deforestation/afforestation, but the combined effects from the mix of recent historical land cover changes in Europe have not been explored. In this study, we use a combination of high resolution land cover data with a regional climate model (the Weather Research and Forecasting model, WRF, v3.9.1) to quantify the effects on surface temperature of land cover changes between 1992 and 2015. Unlike many previous studies that had to use one unrealistic large-scale simulation for each LCC to single out its climate effects, our analysis simultaneously considers the effects of the mix of historical land cover changes in Europe and introduces a new method to disentangle the individual contributions. This approach, based on a ridge statistical regression, does not require an explicit consideration of the different components of the surface energy budget, and directly shows the temperature changes from each land transition.</p><p>            From 1992 to 2015, around 70 Mha of land transitions occurred in Europe. Approximately 25 Mha of agricultural land was left abandoned, which was only partially compensated by cropland expansion (about 20 Mha). Declines in agricultural land mostly occurred in favor of forests (15 Mha) and urban settlements (8 Mha). Relative to 1992, we find that the land covers of 2015 are associated with an average temperature cooling of -0.12±0.20 °C, with seasonal and spatial variations. At a continental level, the mean cooling is mainly driven by agriculture abandonment (cropland-to-forest transitions). Idealized simulations where cropland transitions to other land classes are excluded result in a mean warming of +0.10±0.19 °C, especially during summer. Conversions to urban land always resulted in warming effects, whereas the local temperature response to forest gains and losses shows opposite signs from the western and central part of the domain (where forests have cooling effects) to the eastern part (where forests are associated to warming). Gradients in soil moisture and local climate conditions are the main drivers of these differences. Our findings are a first attempt to quantify the regional climate response to historical LCC in Europe, and our method allows to unmix the temperature signal of a grid cell to the underlying LCCs (i.e., temperature impact per land transition). Further developing biophysical implications from LCCs for their ultimate consideration in land use planning can improve synergies for climate change adaptation and mitigation.</p><p> </p>

Holocene Climatic Change in the Northern Midwest: Pollen-Derived Estimates

1984 ◽  
Vol 22 (3) ◽  
pp. 361-374 ◽  
Author(s):  
P. J. Bartlein ◽  
T. Webb ◽  
E. Fleri
Keyword(s):  
Maximum Temperature ◽  
Pollen Record ◽  
Regression Equations ◽  
Climate Data ◽  
The South ◽  
The North ◽  
Modern Pollen ◽  
Quantitative Estimates ◽  
Multiple Regression Models ◽  
July Temperature

Mapping of Holocene pollen data in the midwestern United States has revealed several broadscale vegetational changes that can be interpreted in climatic terms. These changes include (1) the early Holocene northward movement of the spruce-dominated forest and its later southward movement after 3000 yr B.P. and (2) the eastward movement of the prairie/forest border into southwestern Wisconsin by 8000 yr B.P. and its subsequent westward retreat after 6000 yr B.P. When certain basic assumptions are met, multiple regression models can be derived from modern pollen and climate data and used to transform the pollen record of these vegetational changes into quantitative estimates of temperature or precipitation. To maximize the reliability of the regression equations, we followed a sequence of procedures that minimize violations of the assumptions that underlie regression analysis. Reconstructions of precipitation during the Holocene indicated that from 9000 to 6000 yr B.P. precipitation decreased by 10 to 25% over much of the Midwest, while mean July temperature increased by 0.5° to 2.0°C. At 6000 yr B.P. precipitation was less than 80% of its modern values over parts of Wisconsin and Minnesota. After 6000 yr B.P. precipitation generally increased, while mean July temperature decreased in the north, and increased in the south. The time of the maximum temperature varies within the Midwest and is earlier in the north and later in the south.

scholarly journals Assessment of future scenarios for wind erosion sensitivity changes based on ALADIN and REMO regional climate model simulation data

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Gábor Mezősi ◽  
Viktória Blanka ◽  
Teodóra Bata ◽  
Zsuzsanna Ladányi ◽  
Gábor Kemény ◽  
...  
Keyword(s):  
Land Use ◽  
Wind Erosion ◽  
Climate Model ◽  
Model Simulation ◽  
Regional Climate ◽  
Reference Period ◽  
Regional Climate Model Simulation ◽  
Climate Factor ◽  
Simulation Data ◽  
Sensitivity Changes

AbstractThe changes in rate and pattern of wind erosion sensitivity due to climate change were investigated for 2021–2050 and 2071–2100 compared to the reference period (1961–1990) in Hungary. The sensitivities of the main influencing factors (soil texture, vegetation cover and climate factor) were evaluated by fuzzy method and a combined wind erosion sensitivity map was compiled. The climate factor, as the driving factor of the changes, was assessed based on observed data for the reference period, while REMO and ALADIN regional climate model simulation data for the future periods. The changes in wind erosion sensitivity were evaluated on potentially affected agricultural land use types, and hot spot areas were allocated. Based on the results, 5–6% of the total agricultural areas were high sensitive areas in the reference period. In the 21st century slight or moderate changes of wind erosion sensitivity can be expected, and mostly ‘pastures’, ‘complex cultivation patterns’, and ‘land principally occupied by agriculture with significant areas of natural vegetation’ are affected. The applied combination of multi-indicator approach and fuzzy analysis provides novelty in the field of land sensitivity assessment. The method is suitable for regional scale analysis of wind erosion sensitivity changes and supports regional planning by allocating priority areas where changes in agro-technics or land use have to be considered.

scholarly journals Spatial Correlation between Altitude & other Climatic Variables : A Climatic Model of Kashmir Region using Regression Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 4388-4391
Keyword(s):  
Spatial Resolution ◽  
Climate Model ◽  
Climatic Variables ◽  
Regression Equations ◽  
Climate Data ◽  
Annual Average ◽  
Average Wind Speed ◽  
Significance Level ◽  
Elevation Model ◽  
The Relationship

Kashmir has been encountering expanded temperature and change in precipitation system, which may antagonistically influence the significant environments in the nation differentially. In this study a effort has been put forward to deduce a relationship between altitude & other climatic variables such as Annual average temperature, Annual average precipitation, Annual average wind speed, Annual average solar radiation, Annual average max temperature, Annual average min temperature & Annual average vapor pressure. The data used for this study was provided by worldclim (version2.0), the spatial resolution of the data was about 1 km². The large study area & fine spatial resolution produced a vast amount of data points, which were analyzed with a significance level of the tests at 5%. Further for analysis the data was divided into zones categorized according to the elevation zones, this was mainly done to ease the work flow as the observation points were vast and also to reduce the standard error for each of the variables during analysis. Based on the climate data and the DEM (Digital Elevation Model) used for this study we deduced four multiple linear regression equations for four elevation zones, which describe the relationship between altitude & climatic variables. Although deducing a climate model which can precisely define relationship between the climatic variables & altitude is a challenge although an effort has been made to describe the relationship between elevation and climatic variables.

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