Mexican drought: an observational modeling and tree ring study of 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.

The Standardized Precipitation Index (SPI) is a well-established drought index that is based on transforming the interannual distribution of precipitation to a standard normal distribution. Because of its robust statistical basis, SPI is readily applicable to different regions making comparisons between locations and time windows possible. Nevertheless, the usability of SPI results is undermined by shortcomings that are partly resultant from data and model uncertainties. One such shortcoming is the inability of the existing SPI model to include change in variability of interannual precipitation from non-stationary normal – mostly caused by climate change. In addition, epistemic uncertainty in the form of incompleteness in station-wide precipitation records results in heterogeneity and inconsistency in SPI results. The effects of such epistemic uncertainty on the accuracy of estimations of long-term changes in drought frequency are mostly unknown. Given such deficiency, SPI’s procedure and subsequent results remain deterministic and inadequately informative. Here, we introduce modifications to the traditional SPI using Dempster-Shafer theory (DST) to enable modeling and propagation of variability and epistemic uncertainty with the regular SPI procedure. By generalizing the SPI model from a deterministic setting to an “uncertainty-driven setting” provided by DST, this work makes possible: (a) efficiently propagating data uncertainty in interpolation of station-wide precipitation and SPI, and (b) modeling the effects of shift in precipitation normals (due to e.g., climate change) on drought frequency. In addition, the significance of this shift may then be evaluated with respect to the epistemic uncertainty by measuring how much of the surrounding epistemic uncertainty this shift encloses (i.e., “probability of enclosing”). The latter is especially important due to large unknowns already associated with climate change modeling. We implement the model on summer extreme drought for the Okanagan Basin, BC, Canada. For a single general circulation model and scenario (CGCM3 A2) a maximum 7 % increase in summer extreme drought (for 2080s, as per current definition) is estimated with a maximum probability of enclosing of 36 %.

The impacts of 2 greenhouse gas emissions scenarios (one from the IPCC and one from Greenpeace International) on the occurrence of extreme daily temperature events are considered at several sites in the UK. For each site, a number of probability distributions were tested for goodness-offit to 1961-87 observed daily maximum and minimum temperature data using the KolmogorovSmirnov test. The parameters of the best-fitting distributions were then perturbed to take into account clunate change, both mean and variability. Probabilities of the occurrence of particular temperature threshold events were calculated for both present and future climates. Changes in climate variability were considered in 3 ways: (1) by assuming the present variance stays the same in the future; (2) by imposing standardised percent changes in variance; and (3) by imposing variance changes derived from the UK Meteorological Office high resolution GCM equilibrium climate change experiment. Results presented for 2 contrasting sites illustrate the importance of including changes in variability in climate change studies. Specific results depend on the site and threshold temperature chosen and on the distribution characteristics. However, for example, at Fortrose the 1961-87 mean maximum temperature in July is below 20°C. With increases in global-mean temperature, the probability of this threshold being exceeded increases, although the rate of increase depends on the variance change being considered. The largest rate of increase in probability occurs with a 20% per C increase in variance. The approach described here has been used in one component of a climate change scenario generator for the UK developed for the UK Ministry of Agriculture. Fisheries and Food.

<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.

Abstract. The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in extreme precipitation event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions are from either of them (mean or variability) to the change in precipitation extremes 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 and 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 central Europe the mean influences the increase in extremes more, 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, 24, 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.

An understanding of the energy exchange processes at the surface of the earth is necessary for studies of global climate change. If the climate becomes drier, as is predicted for northern mid-latitudes, it is important to know how major agricultural crops will play a role in the budget of heat and moisture. Thus, the energy balance components of sorghum [Sorghum bicolor (L.) Moench.] and sunflower (Helianthus annuus L.), two drought-resistant crops grown in the areas where summertime drying is forecasted, were compared. Soil water content and evapotranspiration (ET) rates also were determined. Net radiation was measured with net radiometers. Soil heat flux was analyzed with heat flux plates and thermocouples. The Bowen ratio method was used to determine sensible and latent heat fluxes. Sunflower had a higher evapotranspiration rate and depleted more water from the soil than sorghum. Soil heat flux into the soil during the daytime was greater for sorghum than sunflower, which was probably the result of the more erect leaves of sorghum. Nocturnal net radiation loss from the sorghum crop was greater than that from the sunflower crop, perhaps because more heat was stored in the soil under the sorghum crop. But daytime net radiation values were similar for the two crops. The data indicated that models of climate change must differentiate nighttime net radiation of agricultural crops. Sensible heat flux was not always less (or greater) for sorghum compared to sunflower. Sunflower had greater daytime values for latent heat flux, reflecting its greater depletion of water from the soil. Evapotranspiration rates determined by the energy balance method agreed relatively well with those found by the water balance method. For example, on 8 July (43 days after planting), the ET rates found by the energy-balance and water-balance methods were 4.6 vs. 5.5 mm/day for sunflower, respectively; for sorghum, these values were 4.0 vs. 3.5 mm/day, respectively. If the climate does become drier, the lower soil water use and lower latent heat flux of sorghum compared to sunflower suggest that sorghum will be better adapted to the climate change.

High spatial resolution of precipitation (P) and average air temperature (Tavg) datasets are ideal for determining the spatial patterns associated with large-scale atmospheric and oceanic indexes, and climate change and variability studies, however such datasets are not usually available. Those datasets are particularly important for Central America because they allow the conception of climate variability and climate change studies in a region of high climatic heterogeneity and at the same time aid the decisionmaking process at the local scale (municipalities and districts). Tavg data from stations and complementary gridded datasets at 50 km resolution were used to generate a high-resolution (5 km grid) dataset for Central America from 1970 to 1999. A highresolution P dataset was used along with the new Tavg dataset to study climate variability and a climate change application. Consistently with other studies, it was found that the 1970-1999 trends in P are generally non-significant, with the exception of a few small locations. In the case of Tavg, there were significant warming trends in most of Central America, and cooling trends in Honduras and northern Panama. When the sea surface temperature anomalies between the Tropical Pacific and the Tropical Atlantic have different (same) sign, they are a good indicator of the sign of P (Tavg) annual anomalies. Even with non-significant trends in precipitation, the significant warming trends in Tavg in most of Central America can have severe consequences in the hydrology and water availability of the region, as the warming would bring increases in evapotranspiration, drier soils and higher aridity.

&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;

Climate Change, Air Pollution, and COPD Outcomes: Too Many Factors to Be Considered, Even Barometric Pressure!

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.

Climatic variability and the modelling of crop yields