Impact of Climate Change on Apricot Yield in Turkey in the Near Future

2021 ◽  
Author(s):  
M. Tufan Turp ◽  
Nazan An ◽  
Gökhan Özertan ◽  
M. Levent Kurnaz
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Prunus Armeniaca ◽  
Soil Conditions ◽  
Climate Projections ◽  
Worst Case ◽  
Black Sea Region ◽  
Main Producer

<p>Apricot (<em>Prunus armeniaca</em> L.) is one of the most important export crops in Turkey and Turkey is the leader for both fresh and dried apricots production in the world. Apricot can be grown in all regions of Turkey with climate and vegetation diversities, except in the Eastern Black Sea Region and in the high plateaus of the East Anatolian Region. Malatya is the main producer province, which has good ecological and soil conditions in terms of apricot cultivation with the highest quality in Turkey. However, it is possible to talk about irregularities and decreases in apricot yield due to climate change in the region. Therefore, this study aims to observe climate change impacts on apricot yield in the main producer city, Malatya. Hereunder, climate projections were made at a 10 km horizontal resolution for the future period of 2021-2050 under the “worst-case” scenario (RCP8.5) using a regional climate model (RegCM4.4) for Malatya province considering 13 sub-regions. A statistical model, panel data method-multiple regression model, is designed to observe the effect of climate change and variability on the yield. Results indicate that adverse impacts of climate change on biological development of apricot lead to production irregularities and significant losses in yield in Malatya.</p><p> </p><p>Acknowledgement: This research has been supported by Boğaziçi University Research Fund Grant Number 16763.</p>

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>

An overview of the EUCP project – towards improved European Climate Predictions and Projections

2020 ◽  
Author(s):  
Jason A. Lowe ◽  
Carol McSweeney ◽  
Chris Hewitt
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Public And Private ◽  
The People ◽  
Local Actors ◽  
Model Ensembles ◽  
Public And Private Sector

<p>There is clear evidence that, even with the most favourable emission pathways over coming decades, there will be a need for society to adapt to the impacts of climate variability and change. To do this regional, national and local actors need up-to-date information on the changing climate with clear accompanying detail on the robustness of the information. This needs to be communicated to both public and private sector organisations, ideally as part of a process of co-developing solutions.</p><p>EUCP is an H2020 programme that began in December 2017 with the aim of researching and testing the provision of improved climate predictions and projections for Europe for the next 40+ years, and drawing on the expertise of researchers from a number of major climate research institutes across Europe. It is also engaging with users of climate change information through a multiuser forum (MUF) to ensure that what we learn will match the needs of the people who need if for decision making and planning.</p><p>The first big issue that EUCP seeks to address is how better to use ensembles of climate model projections, moving beyond the one-model-one-vote philosophy. Here, the aim is to better understand how model ensembles might be constrained or sub-selected, and how multiple strands of information might be combined into improved climate change narratives or storylines. The second area where EUCP is making progress is in the use of very high-resolution regional climate simulations that are capable of resolving aspects of atmospheric convection. Present day and future simulations from a new generation of regional models ae being analysed in EUCP and will be used in a number of relevant case studies. The third issue that EUCP will consider is how to make future simulations more seamless across those time scales that are most relevant user decision making. This includes generating a better understanding of predictability over time and its sources in initialised forecasts, and also how to transition from the initialised forecasts to longer term boundary forced climate projections.</p><p>This presentation will provide an overview of the challenges being addressed by EUCP and the approaches the project is using.</p><p><br><br></p><p> </p>

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Climate Change Explorer: Extracting localized data for developing Climate Services

2021 ◽  
Author(s):  
Christine Nam ◽  
Bente Tiedje ◽  
Susanne Pfeifer ◽  
Diana Rechid ◽  
Daniel Eggert
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Climate Projections ◽  
Climate Data ◽  
Climate Services ◽  
Mitigation And Adaptation ◽  
Data Formats

<p>Everyone, politicians, public administrations, business owners, and citizens want to know how climate changes will affect them locally. Having such knowledge offers everyone the opportunity to make informed choices and take action towards mitigation and adaptation.</p><p> </p><p>In order to develop locally relevant climate service products and climate advisory services, as we do at GERICS, we must extract localized climate change information from Regional Climate Model ensemble simulations.</p><p> </p><p>Common challenges associated with developing such services include the transformation of petabytes of data from physical quantities such as precipitation, temperature, or wind, into user-applicable quantities such as return periods of heavy precipitation, e.g. for legislative or construction design frequency. Other challenges include the technical and physical barriers in the use and interpretation of climate data, due to large data volume, unfamiliar software and data formats, or limited technical infrastructure. The interpretation of climate data also requires scientific background knowledge, which limit or influence the interpretation of results.</p><p> </p><p>These barriers hinder the efficient and effective transformation of big data into user relevant information in a timely and reliable manner. To enable our society to adapt and become more resilient to climate change, we must overcome these barriers. In the Helmholtz funded Digital Earth project we are tackling these challenges by developing a Climate Change Workflow.</p><p> </p><p>In the scope of this Workflow, the user can <span>easily define a region of interest and extract </span><span>the</span><span> relevant </span><span>climate data </span><span>from the simulations available </span><span>at</span><span> the Earth System Grid Federation (ESGF). Following which, </span><span>a general overview of the projected changes, in precipitation </span><span>for example, for multiple climate projections is presented</span><span>. It conveys the bandwidth, </span><span>i.e. </span><span>the minimum/maximum range by an ensemble of regional climate model projections. </span><span>We implemented the sketched workflow in a web-based tool called </span><span>The Climate Change Explorer. </span><span>It</span> addresses barriers associated with extracting locally relevant climate data from petabytes of data, in unfamilar data formats, and deals with interpolation issues, using a more intuitive and user-friendly web interface.</p><p> </p><p>Ultimately, the Climate Change Explorer provides concise information on the magnitude of projected climate change and the range of these changes for individually defined regions, such as found in GERICS ‘Climate Fact Sheets’. This tool has the capacity to also improve other workflows of climate services, allowing them to dedicate more time in deriving user relevant climate indicies; enabling politicians, public administrations, and businesses to take action.</p>

Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER

2021 ◽  
Author(s):  
Sebastian Bathiany ◽  
Diana Rechid ◽  
Susanne Pfeifer ◽  
Juliane El Zohbi ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Climate Change ◽  
Elevated Temperatures ◽  
Climate Model ◽  
Regional Climate ◽  
Weather Conditions ◽  
Climate Projections ◽  
Public Attention ◽  
Compound Events ◽  
Climate Model Simulations ◽  
The Individual

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.</p><p>In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.</p>

Pedoclimatic comparison of three viticultural areas of Italy devoted to high-quality Aglianico and Cabernet Sauvignon production

2020 ◽  
Author(s):  
Eugenia Monaco ◽  
Roberto De Mascellis ◽  
Giuliana Barbato ◽  
Paola Mercogliano ◽  
Maurizio Buonanno ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Regional Climate ◽  
Mediterranean Area ◽  
Climatic Conditions ◽  
Cabernet Sauvignon ◽  
Climate Projections ◽  
Italian Regions ◽  
Berry Quality ◽  
Rcp 8.5

<p>In the Mediterranean area, the expected increase in temperature coupled with the decrease in rainfall, as well as the increase in the frequency of extreme events (heatwaves and drought, IPCC, 2019), will severely affect the survival of current vineyard areas. Cultivar thermal requirement and soil water availability could be not satisfied, leading to a limitation in yield and berry quality also due to constraints in the achievement of optimal grape maturity.</p><p>In this context, the understanding of how the spatial viticultural suitability will change under climate change is of primary interest in order to identify the best adaptation strategies to guarantee the resilience of current viticultural areas. Moreover, the improvement of knowledge of climate, soil, and their interaction for each specific cultivar will be fundamental because the terroir system is based on this interaction able to influence the plant status (e.g., water).</p><p>In this study, different pedo-climatic conditions (past, present, and future) in three Italian sites at different latitudes (from center to southern), were compared for two red varieties of grapevine: Aglianico (indigenous cv) and Cabernet Sauvignon (international cv).</p><p>Grapevine adaptation to future climate in each experimental farm in Campania, Molise, and Sicily Italian regions has been realized through the use of bioclimatic indexes (e.g., Amerine & Winkler for Aglianico 2110 GDD). The climatic evaluation was performed using Regional Climate Model COSMO-CLM at high-resolution (8km x 8km) climate projections RCP4.5 and RCP 8.5 (2010-2100) and Reference Climate (RC, 1971-2005).</p><p>Results have shown how climate change will affect the cultivation of Aglianico and Cabernet Sauvignon, considering both the climate and bioclimatic needs of cultivars themselves in the current viticultural areas.</p><p>Finally, coupled with the climatic evaluation, a pedological survey to characterize the soils, and the analysis of satellite images (Sentinel2 ) coupled with stemwood anatomical analysis has been performed to reconstruct the past eco-physiological behavior.</p>

The Sensitivity of Regional Climate Projections to SSP-Based Land Use Changes in the North American CORDEX Domain

2020 ◽  
Author(s):  
Melissa Bukovsky ◽  
Linda Mearns ◽  
Jing Gao ◽  
Brian O'Neill
Keyword(s):  
Climate Change ◽  
Land Use ◽  
North American ◽  
Land Surface ◽  
Agricultural Land ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Rcp Scenarios ◽  
The North

<p>In order to assess the combined effects of green-house-gas-induced climate change and land-use land-cover change (LULCC), we have produced regional climate model (RCM) simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LULCCs that are consistent with particular Shared Socioeconomic Pathways (SSPs).  In standard, existing NA-CORDEX simulations, land surface characteristics are held constant at present day conditions.  These new simulations, in conjunction with the NA-CORDEX simulations, will help us assess the magnitude of the changes in regional climate forced by LULCC relative to those produced by increasing greenhouse gas concentrations.     </p><p>Understanding the magnitude of the regional climate effects of LULCC is important to the SSP-RCP scenarios framework.  Whether or not the pattern of climate change resulting from a given SSP-RCP pairing is sensitive to the pattern of LULCC is an understudied problem.  This work helps address this question, and will inform thinking about possible needed modifications to the scenarios framework to better account for climate-land use interactions.</p><p>Accordingly, in this presentation, we will examine the state of the climate at the end of the 21<sup>st</sup> century with and without SSP-driven LULCCs in RCM simulations produced using WRF under the RCP8.5 concentration scenario.  The included LULCC change effects have been created following the SSP3 and SSP5 narratives using an existing agricultural land model linked with a new long-term spatial urban land model. </p>

scholarly journals Evaluation of Agriculture-Related Climate Indices in Hindcast COSMO-CLM Simulations over Central Europe

2020 ◽  
Vol 4 (1) ◽  
pp. 27
Author(s):  
Huan Zhang ◽  
Merja H. Tölle
Keyword(s):  
Regional Climate Model ◽  
Central Europe ◽  
Climate Model ◽  
Crop Yields ◽  
Regional Climate ◽  
Growing Season ◽  
Horizontal Resolution ◽  
Climate Indices ◽  
Climate Projections ◽  
Growing Season Length

High horizontal resolution regional climate model simulations serve as forcing data for crop and dynamic vegetation models, for generating possible scenarios of the future effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM15 (CCLM) from 1979 to 2015, and the first year was considered as a spin-up period. The model was driven with hourly ERA5 data, which were the latest climate reanalysis product by ECMWF, and directly downscaled to a 3 km horizontal resolution over Central Europe. The land-use classes were described by ECOCLIMAP, and the soil type and depth were described by HWSD. The evaluation was carried out in terms of temperature, precipitation, and climate indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm and dry summer bias found in its parent model, it reproduces the main features of the recent past climate of Central Europe, including the seasonal mean climate patterns and probability density distributions. Furthermore, the model reproduced climate indices for temperature like growing season length, growing season start date, number of summer days. The results highlighted the possibility of directly downscaling ERA5 data with regional climate models, avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate projections of future changes in agricultural climate indices.

scholarly journals Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

2013 ◽  
Vol 26 (10) ◽  
pp. 3394-3414 ◽  
Author(s):  
C. Adam Schlosser ◽  
Xiang Gao ◽  
Kenneth Strzepek ◽  
Andrei Sokolov ◽  
Chris E. Forest ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Massachusetts Institute Of Technology ◽  
Adaptation To Climate Change ◽  
Intergovernmental Panel ◽  
Near Surface ◽  
Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

scholarly journals Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

2016 ◽  
Vol 11 (2) ◽  
pp. 670-678 ◽  
Author(s):  
N. S Vithlani ◽  
H. D Rank
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Extremes ◽  
Global Climate Models ◽  
Climate Indices ◽  
Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

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