Agrometeorological adaptation strategies to increasing climate variability and climate change

<p>Weather or climate extreme events disproportionately affect societies and ecosystems. Physical understanding of the impact of global climate change on the occurrence of such extreme events is therefore crucial. Here we separate changes in the occurrence of high-temperature and heavy-precipitation events in a part caused by climatic changes of the mean state and a part caused by climatic changes in variability. We extend the frequently used Probability Ratio (PR) framework, used to quantify changes in the occurrence of extreme events, such that it produces a 'PRmean' value for changes due to a change in mean climate and a 'PRvar' value for changes due to changes in climate variability. Large ensemble climate model simulations are used to quantify changes in extreme events in a 2C warmer world. It is found that the increased occurrence of high-temperature extremes is predominantly caused by the increase of mean temperatures, with a much smaller role for changes in variability (PRmean >> PRvar). The spatial differences are considerable, however, with the polar regions standing out as regions where changes in temperature variability do have a considerable limiting effect on extreme event occurrence. Changes in heavy-precipitation extremes are generally due to changes in both mean climate and variability (PRvar ≈ PRmean). Despite complex feedbacks in the global climate system, the ratio of PRmean to PRvar is largely independent of the event threshold and the climate scenario. These results help to quantify robustness of projected changes in climate extremes, given that projections of changes in the mean state are in many cases much better constrained than projections of changes in variability.</p>

The climate is changing most rapidly in the Arctic because of Arctic amplification, influencing migratory bird species that depend on the short, but productive Arctic summer climate. A potential increase in climate variability can lead to reduced reproductive success and even be a major source of mortality for these birds. Most studies so far, focus on mean changes in climate, telling part of the story. However, along with changes in the mean, changes in the variability of climate will occur. These two combined (changes in mean and variability) can lead to more/less frequent extreme events such as heatwaves, droughts and excessive snowfall or melt.Here we focus on changes in variability and extremes of Arctic bird-related climatic variables, such as temperature, precipitation, snow cover, primary productivity, solar radiation, and soil moisture. We investigate how strongly these climatic variables vary on a daily, monthly, annual and decadal basis. Furthermore, we infer changes in variability between four distinct climate states (0.5x, 1x, 2x & 4x CO2 level): will the variability and probability for extreme events change in warmer or colder climates? How will this potentially affect Arctic migratory birds? For example, snowfall and ground snow cover are expected to decrease in a warmer climate, resulting in more areas available for nesting. However, snowfall variability is projected to increase, making conditions more unpredictable on an annual basis.To this end, we carried out four long (500 years), steady-state runs (constant CO2 level), using the state-of-the-art Earth System Model EC-Earth3. We used two versions of the model (EC-Earth3-Veg & EC-Earth3-CC) and 4 CO2 levels: 0.5x, 1x, 2x & 4x CO2 concentration of the year 2022. The end result is 4,000 years of model output data, allowing us to study climate-related changes in climate variability of Arctic bird-related variables.

<strong class="journal-contentHeaderColor">Abstract.</strong> The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. While over Scandinavia or Mid-Europe the mean influences the increase in extremes more, reversely the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3 h, 24 h, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

<strong class="journal-contentHeaderColor">Abstract.</strong> The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. While over Scandinavia or Mid-Europe the mean influences the increase in extremes more, reversely the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3 h, 24 h, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

<strong class="journal-contentHeaderColor">Abstract.</strong> The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. While over Scandinavia or Mid-Europe the mean influences the increase in extremes more, reversely the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3 h, 24 h, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

<strong class="journal-contentHeaderColor">Abstract.</strong> The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. While over Scandinavia or Mid-Europe the mean influences the increase in extremes more, reversely the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3 h, 24 h, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

Daily outputs of the CSIRO Conformal Cubic Atmospheric Model (C-CAM) for the periods of 1961–1990 and 2065–2094 were used in the present study to derive changes in mean climate and in climatic variability, which were used by the stochastic weather generator LARS-WG to generate climatic change scenarios for 3 sites in southeast Australia. Climatic scenarios were coupled with the Agricultural Production System sIMulator (APSIM)-Wheat/Canola models to identify the influence of changes in climatic variability on wheat and canola production at 3 sites (Condobolin, Nhill and Wagga Wagga). Changes in climatic variability had negative effects on average wheat and canola yields at Wagga Wagga and Condobolin, and, in most cases, on the coefficients of variation (CV) of wheat yield, while the CV of canola yield experienced both positive (50% of the cases) and negative (50% of the cases) effects. Changes in climatic variability had positive (50% of the cases) or no (50% of the cases) effect on the average harvest index (HI) of wheat, whereas they had negative (33% of cases) or no (67% of cases) effect on the average HI of canola. Negative effects of changed climatic variability on the CV of HI for both crops were found. Our results demonstrate that the effects of changes in climatic variability on crop production vary across locations and impact indicators. Changes in climatic variability therefore need to be taken into account in agricultural impact assessment.

&amp;lt;p&amp;gt;The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. The mechanisms behind a change in either the mean or the variability can thereby be very different.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. Further, the results reveal a smaller influence of the level of warming and level of extremeness on the individual contributions then the seasonality or temporal aggregation (3h, 24h, 72h). These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.&amp;lt;/p&amp;gt;

Coffee (Coffea spp.) represents one of the most important sources of income and goods for the agricultural sector in Central America, Colombia, and the Caribbean region. The sustainability of coffee production at the global and regional scale is under threat by climate change, with a major risk of losing near to 50% of today’s suitable area for coffee by 2050. Rain-fed coffee production dominates in the region, and under increasing climate variability and climate change impacts, these production areas are under threat due to air temperature increase and changes in rainfall patterns and volumes. Identification, evaluation, and implementation of adaptation strategies for growers to cope with climate variability and change impacts are relevant and high priority. Incremental adaptation strategies, including proper soil and water management, contribute to improved water use efficiency (WUE) and should be the first line of action to adapt the coffee crop to the changing growing conditions. This research’s objective was to evaluate at field level over five years the influence of fertilization with calcium (Ca+2) and potassium (K+) on WUE in two coffee arabica varieties: cv. Castillo and cv. Caturra. Castillo has resistance against coffee leaf rust (CLR) (Hemileia vastatrix Verkeley and Brome), while Caturra is not CLR-resistant. WUE was influenced by yield changes during the years by climate variability due to El Niño–ENSO conditions and CLR incidence. Application of Ca+2 and K+ improved the WUE under such variable conditions. The highest WUE values were obtained with an application of 100 kg CaO ha−1 year−1 and between 180 to 230 kg K2O ha−1 year−1. The results indicate that adequate nutrition with Ca+2 and K+ can improve WUE in the long-term, even underwater deficit conditions and after the substantial incidence. Hence, an optimum application of Ca+2 and K+ in rain-fed coffee plantations can be regarded as an effective strategy to adapt to climate variability and climate change.

I discuss three aspects of research necessary for investigating possible effects of changes in climatic variability on crop yields. Additional information on changed variability effects is needed to further elucidate uncertainties in our knowledge of possible impacts of climate change on agriculture. First, sensitivity analyses of crop responses to shifting change in variability must be performed. Second, investigations of how climatic variability may change under perturbed (e.g., greenhouse gas-warmed) climate conditions should be undertaken. If one has some confidence in estimates of how variability may change, then a third research task is the formation of climate change scenarios that incorporate changes in climatic variability and their application to crop-climate models to determine crop responses. In this chapter, these research tasks are discussed regarding one climate variable, precipitation. I summarize two research projects that have been undertaken to investigate the sensitivity of the CERES-wheat (Triticum aestivum L.) crop model to changes in climatic variability, on daily to annual time scales, for sites in the central Great Plains. I also provide an example of determining possible changes in daily variability of precipitation through analysis of results from two regional climate model experiments, and then go on to describe an example of forming a climate change scenario that incorporates changes in daily precipitation variability estimated from the regional model runs. The CERES-wheat model exhibits considerable sensitivity to changes in precipitation variability, and variability of yields is positively correlated with variability in precipitation. Consideration of changes on both daily and interannual time scales is important. Estimates of changes in wheat yields simulated from a scenario that includes changes in variability are very different from changes in yield determined from a scenario that considers only mean changes in precipitation.

The shift toward renewable energy as part of Europe’s climate-neutral strategy increases the energy system's reliance on weather conditions. This study explores the impacts of changes in climate variability and extremes on Europe’s renewable electricity systems, affecting reliability. It uses a large ensemble approach integrating 1600 years of climate data under present-day and +2°C warming scenarios into a modeling framework for wind, solar, and hydropower production alongside electricity demand. The study assesses changes in mean states, variability, and extremes, identifying rare, high-impact events, e.g., energy droughts and multi-year low electricity production. The results reveal notable regional and seasonal variations in energy system dynamics under future warming scenarios. In the Nordic region, increased winter runoff leads to higher hydropower availability, reducing residual loads and shortening energy drought durations. In contrast, Iberia faces growing challenges with extended summer cooling demands, exacerbated by reduced wind and hydropower availability. Importantly, the analysis shows that changes in extremes differ significantly from mean trends, with deviations up to -20% (overestimation) or +4% (underestimation) in the most severe scenarios. Decadal variability analysis underscores the critical influence of natural climate modes like the Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation on energy production and demand. In the present-day ensemble, the AMV shows strong correlations with energy variables (0.93 for mean demand anomalies and &gt;0.73 for wind power). However, the +2°C warming scenario reduces the statistical significance of these correlations. This study highlights the importance of explicitly analyzing extremes, as mean trends alone may misrepresent (changes in) system risks. By explicitly accounting for both natural variability and climate change, it provides insights into extreme compound events, giving a foundation for robust, adaptive strategies to ensure energy system reliability in a changing climate.&amp;#xD;

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades.

The frequency of climate extremes will change in response to shifts in both mean climate and climate variability. These individual contributions, and thus the fundamental mechanisms behind changes in climate extremes, remain largely unknown. Here we apply the probability ratio concept in large-ensemble climate simulations to attribute changes in extreme events to either changes in mean climate or climate variability. We show that increased occurrence of monthly high-temperature events is governed by a warming mean climate. In contrast, future changes in monthly heavy-precipitation events depend to a considerable degree on trends in climate variability. Spatial variations are substantial however, highlighting the relevance of regional processes. The contributions of mean and variability to the probability ratio are largely independent of event threshold, magnitude of warming and climate model. Hence projections of temperature extremes are more robust than those of precipitation extremes, since the mean climate is better understood than climate variability.

Three methods were used to distinguish the characteristics of changes in climate variability and normalized difference vegetation index (NDVI) during the period from 1982 to 2000 in China. Great changes in climate variability and an increased trend in NDVI were observed. The changes in precipitation variability were greater than the changes in temperature variability in each month, which is attributed to changes in the monsoon system in East Asia. The abrupt changes in climate and NDVI were more significant in 1983 than in the other years due to the impacts of El Niño/Southern Oscillation (ENSO). Using these results, the influences of changes in climate variability on vegetation were studied in the whole nation, and eight regions were defined according to the vegetation division map of China. The results show that abrupt climate changes at a small scale cannot cause abrupt NDVI changes directly. At a nationwide level, over a longer time scale the persistence of above/below average temperature determines the changes in NDVI; at a shorter time scale, changes in the magnitude of precipitation influence NDVI significantly. Such regional climate variability affects vegetation in different ways owing to the diversity of vegetation types, climatic conditions and topography of the land.

Climatic variability and the modelling of crop yields