Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Causes of Robust Seasonal Land Precipitation Changes

Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive are used to calculate the zonal-mean change in seasonal land precipitation for the second half of the twentieth century in response to a range of external forcings, including anthropogenic and natural forcings combined (ALL), greenhouse gas forcing, anthropogenic aerosol forcing, anthropogenic forcings combined, and natural forcing. These simulated patterns of change are used as fingerprints in a detection and attribution study applied to four different gridded observational datasets of global land precipitation from 1951 to 2005. There are large differences in the spatial and temporal coverage in the observational datasets. Yet despite these differences, the zonal-mean patterns of change are mostly consistent except at latitudes where spatial coverage is limited. The results show some differences between datasets, but the influence of external forcings is robustly detected in March-May, December-February, and for annual changes for the three datasets more suitable for studying changes. For June-August and September-November, external forcing is only detected for the dataset that includes only long-term stations. Fingerprints for combinations of forcings that include the effect of greenhouse gases are similarly detectable to those for ALL forcings, suggesting that greenhouse gas influence drives the detectable features of the ALL forcing fingerprint. Fingerprints of only natural or only anthropogenic aerosol forcing are not detected. This, together with two-fingerprint results, suggests that at least some of the detected change in zonal land precipitation can be attributed to human influences

Observed global changes in sector-relevant climate extremes indices—an extension to HadEX3

DATA AVAILABILITY STATEMENT : The gridded dataset are available at www.metoffice.gov.uk/hadobs/hadex3 and at www.climdex.org. In addition, a version is available on the CEDA archive (https://dx.doi.org/10.5285/2bfbdba03d9b423f99cadf404ca2daab). The underlying station indices will be made available on www.climdex.org where we are allowed to do so. For some collections we are not allowed to make the underlying station data public under terms of their licence.Please read abstract in the article.PLAIN LANGUAGE SUMMARY : To be able to assess changes in extreme temperature and rainfall events across the globe, data sets which capture characteristics of these extreme events are required. The use of indices for these characteristics further enables both data sharing and the comparison of events across the world. Extreme events have impacts across human health, our infrastructure and the natural environment. So far there has not been a global product which presents indices which are relevant for different sectors of our society, including health, agriculture and water resources. In this work we present an extension to an existing data set of extremes indices, HadEX3, by including indices defined by the World Meteorological Organization which were developed with sector specific applications in mind. We have used the same approach and methodology, and where possible the same underlying daily temperature and rainfall observations. The temperature indices show changes consistent with global scale warming, with heat wave characteristics showing increases in the number, duration and intensity of these extreme events in most places. The data files are available for use by interested researchers in their work.The Met Office Hadley Centre Climate Programme funded by DSIT and by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China under the International Science Partnerships Fund (ISPF); Australian Research Council Grant; the Horizon 2020 LANDMARC project; the RED-CLIMA (Red Española e Iberoamericana sobre Variabilidad Climática y Servicios Climáticos en Ecosistemas Terrestres y Marinos: RED-CLIMA) Project from the Consejo Superior de Investigaciones Científicas LINCGLOBAL CSIC from Spain; National Institute of Science and Technology for Climate Change Phase 2; the National Coordination for Higher Education and Training (CAPES).https://agupubs.onlinelibrary.wiley.com/journal/23335084hj2024Geography, Geoinformatics and MeteorologySDG-13:Climate actio

Glycogen metabolism influences embryonic segmentation and appendages outgrowth during Tribolium castaneum embryogenesis

Embryonic segmentation of arthropods is a dynamic biological process that produces an organism by pattern formation, morphogenesis and specification of corresponding cells. The involved genes have mostly been identified by their phenotypes in systematic mutagenesis screens in few model systems, such as Drosophila melanogaster. The candidate gene approach based on RNAi was used to compare the function of those genes in other arthropods but was not able to detect novel and unsuspected functions. The detection of novel gene functions by a hypothesis independent screen has been a major endeavor to fill this gap. In this work, a large collection of knock-down experiments was analyzed for cuticle phenotypes indicating a function in embryogenesis and a subset was re-screened to confirm the phenotypes. However, respective systematic searches may have remained incomplete, because interference of some patterning genes caused embryonic death before cuticle formation. Therefore, such "empty egg phenotypes" without any recognizable cuticle structures had been screened for defects in red flour beetle Tribolium castaneum embryogenesis and two glycogen metabolism genes had been shown to produce patterning defects. Here, I showed that RNAi targeting two genes involved in glucose synthesis Tc-GlyS and Tc-AGBE led to segmentation and appendage defects ultimately leading to empty egg phenotypes. Indeed, loss of Tc-GlyS and Tc-AGBE resulted in malformed and asymmetrically absent stripes of segment polarity genes indicating issues with maintaining segmental boundaries. More specifically, the defects first affected posterior abdominal segments but later extended to the entire trunk while the growth zone and pair-rule gene expression appeared to remain intact. Tc-dac and Tc-Sp8 expression marked medial portions of the appendages and were strongly affected while Tc-Dll and Tc-Sp8 expression marked distal parts and remained intact in RNAi phenotypes. I tested the hypothesis that this effect of glycogen metabolism enzymes was via the Glycogen synthase kinase 3 (GSK-3), which is known to be required for Wnt, Hh and Notch pathways, as well. I found that the effect was not exclusively by one of these pathways. Taken together, I found that Tc-GlyS and Tc- AGBE functions were required for segmental boundary maintenance, axis elongation and appendages outgrowth thereby linking metabolism and patterning formation.2022-02-2

Human influence on Canadian temperatures

Canada has experienced some of the most rapid warming on Earth over the past few decades with a warming rate about twice that of the global mean temperature since 1948. Long-term warming is observed in Canada’s annual, winter and summer mean temperatures, and in the annual coldest and hottest daytime and nighttime temperatures. The causes of these changes are assessed by comparing observed changes with climate model simulated responses to anthropogenic and natural (solar and volcanic) external forcings. Most of the observed warming of 1.7 °C increase in annual mean temperature during 1948–2012 [90% confidence interval (1.1°, 2.2 °C)] can only be explained by external forcing on the climate system, with anthropogenic influence being the dominant factor. It is estimated that anthropogenic forcing has contributed 1.0 °C (0.6°, 1.5 °C) and natural external forcing has contributed 0.2 °C (0.1°, 0.3 °C) to the observed warming. Up to 0.5 °C of the observed warming trend may be associated with low frequency variability of the climate such as that represented by the Pacific decadal oscillation (PDO) and North Atlantic oscillation (NAO). Overall, the influence of both anthropogenic and natural external forcing is clearly evident in Canada-wide mean and extreme temperatures, and can also be detected regionally over much of the country.We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank Chao Li, Elizabeth Bush and Emma Watson and three reviewers for their comments that have helped to improve the manuscript.FacultyReviewe

FRACTIONAL LÉVY PROCESSES AND NOISES ON GEL′FAND TRIPLE

In this paper, we construct a class of infinitely divisible distributions on Gel′fand triple. Based on this construction, we define Lévy processes on Gel′fand triple and give their Lévy–Itô decompositions. Then, we construct the general Lévy white noises on Gel′fand triple. By using the Riemann–Liouville fractional integral method, we define the general fractional Lévy noises on Gel′fand triple and investigate their distribution properties. </jats:p

Climate change attribution with large ensembles

&amp;lt;p&amp;gt;The large sample sizes from single-model large ensembles are beneficial for a robust attribution of climate changes to anthropogenic forcing. This presentation will review examples using large ensembles in two types of attribution:&amp;amp;#160; standard detection and attribution of spatio-temporal changes and extreme event attribution. First, increases in extreme precipitation have been attributed to anthropogenic forcing at large scales (global and hemispheric). We present results from a study that used three large ensembles, including two Earth System Models and one Regional Climate Model, to find a robust detection of a combined anthropogenic and natural forcing signal in the intensification of extreme precipitation at the continental scale and some regional scales in North America. Second, we use six large ensembles to assess the robustness of the attribution of extreme temperature and precipitation events. An event attribution framework is used and each large ensemble is treated as a perfect model. Robustness of the attribution is defined based on consistent agreement between the different models on a significant change in the probability of an event with the inclusion of anthropogenic forcing. We demonstrate that the attribution of extreme temperature events is robust. Meanwhile, the attribution of extreme precipitation events becomes robust in many regions under additional warming, but uncertainties pertaining to changes in atmospheric dynamics hinder attribution confidence in other regions. We also demonstrate that smaller ensembles bring larger uncertainty to event attribution.&amp;lt;/p&amp;gt;</jats:p