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

2021 ◽  
Author(s):  
Sebastian Bathiany ◽  
Diana Rechid ◽  
Klaus Goergen ◽  
Patrizia Ney ◽  
Alexandre Belleflamme

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the 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 to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products.</p> <p> </p> <p>The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages.</p> <p> </p> <p>In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe.</p> <p> </p> <p>We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.</p> <p> </p>

2021 ◽  
Author(s):  
Raju Kalita ◽  
Dipangkar Kalita ◽  
Atul Saxena

Abstract We have used Mann-Kendall trend test and Sen’s slope estimator method to find out significant changes in extreme climate indices for daily temperature as well as precipitation over the period 1979 to 2020 in Cherrapunji. In the present study, a total of 24 precipitation and temperature based extreme climate indices were calculated using RClimDex v 1.9-3. Among 24 indices, 7 were derived from number of days above nn mm rainfall (Rnn) according to Indian Meteorological Department (IMD) convention and the rest were in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). It was observed that, among all the indices, consecutive dry days (CDD), summer days (SU25) and very light rainfall (VLR) days increased significantly with 0.54, 1.58 and 0.14 days/year respectively, while only consecutive wet days (CWD) decreased significantly with 0.36 days/year. A slight negative trend was also observed in case of tropical nights (TR20) and among the other precipitation indices as well. Again, the indices associated with daily maximum temperature increased significantly with annual change of 0.06 to 0.07 ⁰C/year. And for indices associated with daily minimum temperature, almost no change or a slight negative change was observed, except a significant positive trend in February and significant negative trend in November for TNN only. The analysis reveals that some of the extreme climate indices which explains the climatic conditions of Cherrapunji has changed a lot over the period of 42 years and if this trend continues then Cherrapunji will be under threat when it comes to climate change.


2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.


2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>


2020 ◽  
Author(s):  
Luc Yannick Andréas Randriamarolaza ◽  
Enric Aguilar ◽  
Oleg Skrynyk

<p>Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km<sup>2</sup> and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 – 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.</p><p>Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.</p><p>Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.</p><p>Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS<sup>*</sup> project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 – 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.</p><p>-------------------</p><p>*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).</p>


2012 ◽  
Vol 25 (3) ◽  
pp. 939-957 ◽  
Author(s):  
A. Amengual ◽  
V. Homar ◽  
R. Romero ◽  
S. Alonso ◽  
C. Ramis

Abstract Projections of climate change effects for the System of Platja de Palma (SPdP) are derived using a novel statistical technique. Socioeconomic activities developed in this settlement are very closely linked to its climate. Any planning for socioeconomic opportunities in the mid- and long term must take into account the possible effects of climate change. To this aim, daily observed series of minimum and maximum temperatures, precipitation, relative humidity, cloud cover, and wind speed have been analyzed. For the climate projections, daily data generated by an ensemble of regional climate models (RCMs) have been used. To properly use RCM data at local scale, a quantile–quantile adjustment has been applied to the simulated regional projections. The method is based on detecting changes in the cumulative distribution functions between the recent past and successive time slices of the simulated climate and applying these, after calibration, to the recent past (observed) series. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs, although the correction does not result in such clear improvement when dealing with very extreme rainfalls. Next, the corrected series are analyzed to quantify the climate change signal. An increase of the annual means for temperatures together with a decrease for the remaining variables is projected throughout the twenty-first century. Increases in weak and intense daily rainfalls and in high extremes for daily maximum temperature can also be expected. With this information at hand, the experts planning the future of SPdP can respond more effectively to the problem of local adaptation to climate change.


2021 ◽  
Author(s):  
Farhan Aziz ◽  
Nadeem Tariq ◽  
Akif Rahim ◽  
Ambreen Mahmood

<p>In recent years, extreme events and their severe damage have become more common around the world. It is widely known that atmospheric greenhouse gases have contributed to global warming. <br>A set of appropriate indicators describing the extremes of climate change can be used to study the extent of climate change. This study reveals the trends of temperature extreme indices on the spatial scale in the western part of Northwest Himalayas. The study is conducted at 13 climate stations lies at a different altitude of the study area.The Daily maximum and minimum temperature data during 2000--2018 of stations obtained from the Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA). The 12 extreme temperature indices (FD, SU, TXx , TXn., TNx, TNn, TN10p , TN90p, TX10p , TX90p, CSDI, WSDI) recommended by ETCCDI (Expert Team on Climate Change Detection and Indices) are used to study the variabilities in temperature extremes. These indices are characterized based on amplitude, persistence, and frequency. The analysis is performed by using R package of extremes “RClimDEX”. The analysis shows the frequency of summer days (Su) and warm spells (WSDI) have increasing trends in the Southwest region, whereas the frequency of cold spells and frost days have decreasing trends observed in the Northern region of the study areas. The maximum and minimum values of daily maximum temperature (TXX, TXN) increase in the foothill area of the region and decreasing trends in the high elevation region. The day and night get cool in the Northwest region, whereas the days and nights are showing warmer trends in low elevation regions of the study area. Overall, the study concludes that the Northwestern parts have cool trends while South West and South eastern parts have warm trends during the early 21st century.</p><p><strong>Key words:</strong>  Temperature Extremes, Northwest Himalayas, Trends, R-Climdex, Climate Change</p>


2015 ◽  
Vol 16 (6) ◽  
pp. 2421-2442 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Edwin P. Maurer ◽  
John T. Abatzoglou ◽  
Katherine C. Hegewisch

Abstract Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Khyati Kakkad ◽  
Michelle L. Barzaga ◽  
Sylvan Wallenstein ◽  
Gulrez Shah Azhar ◽  
Perry E. Sheffield

Health effects from climate change are an international concern with urban areas at particular risk due to urban heat island effects. The burden of disease on vulnerable populations in non-climate-controlled settings has not been well studied. This study compared neonatal morbidity in a non-air-conditioned hospital during the 2010 heat wave in Ahmedabad to morbidity in the prior and subsequent years. The outcome of interest was neonatal intensive care unit (NICU) admissions for heat. During the months of April, May, and June of 2010, 24 NICU admissions were for heat versus 8 and 4 in 2009 and 2011, respectively. Both the effect of moving the maternity ward and the effect of high temperatures were statistically significant, controlling for each other. Above 42 degrees Celsius, each daily maximum temperature increase of a degree was associated with 43% increase in heat-related admissions (95% CI 9.2–88%). Lower floor location of the maternity ward within hospital which occurred after the 2010 heat wave showed a protective effect. These findings demonstrate the importance of simple surveillance measures in motivating a hospital policy change for climate change adaptation—here relocating one ward—and the potential increasing health burden of heat in non-climate-controlled institutions on vulnerable populations.


2020 ◽  
Author(s):  
Sachidanand Kumar ◽  
Kironmala Chanda ◽  
Srinivas Pasupuleti

<p><strong>Abstract</strong></p><p>This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in ‘wet days’ (daily precipitation greater than 95<sup>th</sup> percentile) and ‘dry days’ (daily precipitation lower than 5<sup>th</sup> percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in ‘warm days’ (daily maximum temperature greater than 95<sup>th</sup> percentile), ‘warm nights’ (daily minimum temperature greater than 95<sup>th</sup> percentile), ‘cold days’ (daily maximum temperature lower than 5<sup>th</sup> percentile) and ‘cold nights’ (daily minimum temperature lower than 5<sup>th</sup> percentile) are also investigated for the aforementioned period. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in ‘warm days’ (‘cold days’) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in ‘warm nights’ and a decreasing trend in ‘cold nights’ in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.</p><p><strong>Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India</strong></p><p><strong>References:</strong></p><p>Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.</p>


2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Avit Kumar Bhowmik

This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices’ values. Both the tendency of methods’ over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures—the index of agreement (d) and the coefficient of variation of prediction (ρf)—imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.


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