Guidance for representing uncertainty on global temperature change maps

Previous assessments of the impacts of climate change on heat-related mortality use the "delta method" to create temperature projection time series that are applied to temperature-mortality models to estimate future mortality impacts. The delta method means that climate model bias in the modelled present does not influence the temperature projection time series and impacts. However, the delta method assumes that climate change will result only in a change in the mean temperature but there is evidence that there will also be changes in the variability of temperature with climate change. The aim of this paper is to demonstrate the importance of considering changes in temperature variability with climate change in impacts assessments of future heat-related mortality. We investigate future heat-related mortality impacts in six cities (Boston, Budapest, Dallas, Lisbon, London and Sydney) by applying temperature projections from the UK Meteorological Office HadCM3 climate model to the temperature-mortality models constructed and validated in Part 1. We investigate the impacts for four cases based on various combinations of mean and variability changes in temperature with climate change. The results demonstrate that higher mortality is attributed to increases in the mean and variability of temperature with climate change rather than with the change in mean temperature alone. This has implications for interpreting existing impacts estimates that have used the delta method. We present a novel method for the creation of temperature projection time series that includes changes in the mean and variability of temperature with climate change and is not influenced by climate model bias in the modelled present. The method should be useful for future impacts assessments. Few studies consider the implications that the limitations of the climate model may have on the heat-related mortality impacts. Here, we demonstrate the importance of considering this by conducting an evaluation of the daily and extreme temperatures from HadCM3, which demonstrates that the estimates of future heat-related mortality for Dallas and Lisbon may be overestimated due to positive climate model bias. Likewise, estimates for Boston and London may be underestimated due to negative climate model bias. Finally, we briefly consider uncertainties in the impacts associated with greenhouse gas emissions and acclimatisation. The uncertainties in the mortality impacts due to different emissions scenarios of greenhouse gases in the future varied considerably by location. Allowing for acclimatisation to an extra 2 degrees C in mean temperatures reduced future heat-related mortality by approximately half that of no acclimatisation in each city.

The goal of property testing is to quickly distinguish between objects which satisfy a property and objects that are ϵ-far from satisfying the property. There are now several general results in this area which show that natural properties of combinatorial objects can be tested with constant query complexity, depending only on ϵ and the property, and not on the size of the object being tested. The upper bound on the query complexity coming from the proof techniques are often enormous and impractical. It remains a major open problem if better bounds hold.Hoppen, Kohayakawa, Moreira, and Sampaio conjectured and Klimosova and Kral' proved that hereditary permutation properties are strongly testable, i.e., can be tested with respect to Kendall's tau distance. The query complexity bound coming from this proof is huge. Even for testing a single forbidden subpermutation it is of Ackermann-type in 1/ϵ. We give a new proof which gives a polynomial bound in 1/ϵ for testing a single forbidden subpermutation.Maybe surprisingly, for testing with respect to the rectangular distance, we prove there is a universal (not depending on the property), polynomial in 1/ϵ query complexity bound for two-sided testing hereditary properties of sufficiently large permutations. We further give a nearly linear bound with respect to a closely related metric which also depends on the smallest forbidden subpermutation for the property. Finally, we show that several different permutation metrics of interest are related to the rectangular distance, yielding similar results for testing with respect to these metrics.

Abstract. Dansgaard–Oeschger (D–O) warming events are comparable in magnitude and rate to the anticipated 21st century warming. As such, they provide a good target for evaluation of the ability of state-of-the-art climate models to simulate rapid climate changes. Despite the wealth of qualitative information about climate changes during the D–O events, there has been no attempt to date to make quantitative reconstructions globally. Here we use frequency-corrected Tolerance-weighted Weighted Averaging Partial Least Squares regression (fxTWA-PLS) to reconstruct mean temperature of the coldest month, mean temperature of the warmest month, and a plant-available moisture index across multiple D–O events between 50 and 30 ka based on available pollen records across the globe. The reconstruction of plant-available moisture is corrected for the impact of changing atmospheric CO2 concentrations on plant water use efficiency. These reconstructions show that the largest warming occurred in northern extratropics, especially Eurasia, while western North America and the southern extratropics were characterised by cooling. The change in winter temperature was significantly larger than the change in summer temperature in the northern extratropics and the tropics, indicating that the D–O warming events were characterised by reduced seasonality, but there was no significant difference between the summer and winter temperature changes in the southern extratropics. The antiphasing between northern and southern extratropical changes, and the west-east pattern of cooling and warming in North America were generally consistent across the eight D–O events examined, although coherency is greatest during the strongest events. There was no globally consistent pattern between changes in moisture and changes in temperature. These reconstructions can be used to evaluate the spatial patterns of changes in temperature and moisture in the transient simulations of the D–O events planned as part of the Palaeoclimate Modelling Intercomparison Project.

The recent publication of the summary for policy makers by Working Group I of the Intergovernmental Panel on Climate Change (IPCC) [1] has injected a renewed sense of urgency to address climate change. It is therefore timely to review the notion of preventing 'dangerous anthropogenic interference with the climate system' as put forward in the United Nations Framework Convention on Climate Change (UNFCCC). The article by Danny Harvey in this issue [2] offers a fresh perspective by rephrasing the concept of 'dangerous interference' as a problem of risk assessment. As Harvey points out, identification of 'dangerous interference' does not require us to know with certainty that future climate change will be dangerous—an impossible task given that our knowledge about future climate change includes uncertainty. Rather, it requires the assertion that interference would lead to a significant probability of dangerous climate change beyond some risk tolerance, and therefore would pose an unacceptable risk.

Maize crop (Zea mays) is one of the staple foods in the East African (EA) region. However, the suitability of its production area is threatened by projected climate change. The Multimodel Ensemble (MME) from eight Coupled Model Intercomparison Project 5 (CMIP5) models was used in this paper to show climate change between the recent past (1970-2000) and the future (2041-2060), i.e., the mid-twenty-first century. The climatic suitability of maize crop production areas is evaluated based on these climate datasets and the current maize crop presence points using Maximum entropy models (MaxEnt). The MME projection showed a slight increase in precipitation under both RCP4.5 and RCP8.5 in certain places and a reduction in most of southern Tanzania. The temperature projection showed that the minimum temperature would increase by 0.3 to 2.95°C and 0.3 to 3.2°C under RCP4.5 and 8.5, respectively. Moreover, the maximum temperature would increase by 1.0 to 3.0°C and 1.2 to 3.6°C under RCP4.5 and 8.5 respectively. The impacts of these projected changes in climate on maize production areas are the reduction in the suitability of the crop, especially around central and western Tanzania, mid-northern and western Uganda, and parts of western Kenya by 20-40%, and patches of EA will experience a reduction of as high as 40-60%, especially in northern Uganda, and western Kenya. The projected changes in temperature and precipitation present a significant negative change in maize crop suitability. Thus, food security and the efforts towards the elimination of hunger in EA by the mid-twenty-first century will be hampered significantly. We recommend crop diversification to suit the new future environments, modernizing maize farming programs through the adoption of new technologies including irrigation, and climate-smart agricultural practices, etc.

Climate change and land use/land cover (LULC) changes influence streamflow by altering precipitation patterns, evaporation, and hydrological processes. Disentangling their combined effects is critical for effective water management under increasing climatic and environmental pressures. This meta‐analysis integrates quantitative and qualitative approaches to assess the impacts of precipitation, temperature, and LULC changes on streamflow using published data sets, multiple linear regression, and Random Forest models. Precipitation emerges as the dominant driver, showing significant variability and a direct linear correlation with streamflow. Temperature impacts are inconsistent, while LULC changes demonstrate nuanced effects. Conversions to agriculture generally increase streamflow, whereas transitions to forests reduce it. Multiple linear regression revealed that precipitation alone explains nearly half of the variance in streamflow, with LULC changes contributing an additional but smaller percentage. In contrast, temperature changes have minimal influence. Variability in LULC conversions correlates with residuals, underscoring diverse impacts across land use types. The Random Forest model, which allows the consideration of non‐linear dependencies, achieved R 2 values of 0.7, confirming precipitation as the most critical predictor, followed by temperature and LULC changes. Including catchment area and climate zone added no significant improvement. These findings highlight the combined importance of precipitation, temperature and LULC changes in shaping streamflow dynamics. While comprehensive, the meta‐analysis may overlook local factors such as micro‐climate variations or land management practices. The variability in model predictive power underscores the challenge of modeling nonlinear relationships between climate, LULC changes, and streamflow. The results offer critical insights for sustainable water resource management and predictive hydrological modeling.

The objective is to analyse temperature changes and their future projection in Aceh. The activities consist of collecting past and future temperature data, preparing materials for processing, and analyzing past and future temperature data (climate change projections). The data used are monthly average temperature data from data global climate model, e.g., csiromk3.6-hist-1986-2005-tas, csiromk3.6-rcp45- 2016-2035-tas, csiromk3.6-rcp45-2046-2065-tas, csiromk3.6-rcp45-2081-2100- tas, csiromk3.6-rcp85-2016-2035-tas, csiromk3.6-rcp85-2046-2065-tas, and csiromk3.6-rcp85-2081-2100-tas. The study began with reading climate data in NetCDF format using GRADS software, data processing using CDO software, providing a coordinate system using QGIS software, making climate change projection maps using ArcGIS software, and making climate change graphs using spreadsheet programs. Two scenarios, i.e., RCP4.5 and RCP8.5 are used to analyse the projected temperature changes in the short-term (2016 – 2035), medium-term (2046-2065), and long-term (2081-2100). The results show that the RCP4.5 projection shows a lower change in temperature rise than the RCP 8.5. A change in a temperature rise of up to 5°C was found in the RCP8.5 scenario.

Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study

The participating member nations in Paris at the 2015 convention of the United Nations Framework Convention on Climate Change (UNFCCC) resolved to maintain the rise in global average temperature to a level much less than 2.0 °C compared to pre-industrial levels. It was also committed that the parties would continue with all-out endeavor to limit warming to 1.5 °C. For a country like India with a primarily agrarian economy this leads to two key questions. Firstly, what does the global rise of mean annual temperature (1.5 °C and 2.0 °C) mean at the regional scale? Secondly, what are the implications of keeping warming at or below 1.5 °C for different sectors and in particular on agriculture and water resources? To address these questions we have examined the annual and seasonal impacts of 1.5 °C and 2 °C global temperature rise (GTR) on temperature and rainfall change over all the states of India under two Representative concentration pathways, RCP 8.5 and RCP 4.5, using all Coupled Model Inter Comparison Project CMIP5 Models. Rainfall is projected to increase over all the states with very low change in the western part of the country and highest change in the North eastern and southern region of the country under RCP 8.5. 35% of the country is projected to witness a temperature change equal to or lesser than global mean temperature of 1.5 °C and 2.0 °C whereas 65% is expected to show a greater rise in temperature. The most severe temperature change is expected to be witnessed by the presently colder Northern most states of India such as Jammu and Kashmir, Himachal Pradesh and Uttaranchal (2.0 °C to 2.2 °C at 1.5 °C and 2.5 °C to 2.8 °C at 2.0 °C) in both RCPs. There are opportunities and threats due to climate change and it is imperative for researchers and policy makers to recognize these in the context of the scenarios of 1.5 °C and 2.0 °C global temperature changes. It is essential for the current national and state action plan on climate change and adaptation to be more sensitive in strategizing an efficient response to the different scenarios at the global level (3 °C, 2 °C and 1.5 °C) in order to take more informed policy decisions at global level in synergy with the regional analysis to be able to develop strategies that benefit the local populace.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 45:57-70 (2010) - DOI: https://doi.org/10.3354/cr00910 Changing temperature and rainfall gradients in the British Uplands T. P. Burt1, J. Holden2,* 1Department of Geography, Durham University, Durham DH1 3LE, UK 2School of Geography, University of Leeds, Leeds LS2 9JT, UK *Corresponding author. Email: j.holden@leeds.ac.uk ABSTRACT: Climate change is likely to be experienced differently at higher elevations than in lowland areas. Observations of recent change can increase understanding of how changes will be realised. We examined changes in temperature lapse rates and rainfall gradients between pairs of stations, lowland and upland, across the UK. Seasonal and annual changes were investigated, because there may well be shorter-term changes which are not reflected in the overall annual average. Significant changes in both temperature and rainfall were observed, but the pattern of changes was spatially and seasonally variable across the UK. In general, temperature changes were stronger in winter than in summer, and minima rose more than maxima in the uplands, whereas the changes were similar between minima and maxima in the lowlands. Most (but not all) upland stations showed significant increases in rainfall totals in winter that were greater than those experienced in the nearby lowlands. However, variations in seasonal trends of lapse rates and rainfall gradients are sufficiently large to warrant concern about existing climate change projections and the downscaling of regional and global climate models. Specifically, analysis at an annual time scale may mask important seasonal patterns. For countries where the data network is dominated by lowland stations but a significant proportion of the landscape is upland, this poses a problem for estimating temperature change over a large proportion of the landscape. Given the lack of a consistent pattern of changes, we advocate an extension of climate monitoring in the uplands in order to detail ongoing climate change in the different upland areas. KEY WORDS: Lapse rates · Precipitation · Time series · Long-term records Full text in pdf format PreviousNextCite this article as: Burt TP, Holden J (2010) Changing temperature and rainfall gradients in the British Uplands. Clim Res 45:57-70. https://doi.org/10.3354/cr00910 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 45. Online publication date: December 30, 2010 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2010 Inter-Research.

Global climate has been changing and in fact expected to change in more shocking degree. Therefore, the objective of the current study is to understand climate change signal, downscale climate change scenario for Bale highlands using CORDEX_GCM Models and identify the occurrence of climate change signal in Bale highlands. This study was aimed to make in-depth statistical analysis on past and future climatic trend, rainfall patterns and temperature trends as well as changes for various time scales over Bale highlands. Climate data recorded at meteorological station located in Bale highlands were obtained from National Meteorological Agency Bale Robe Meteorological Branch Directorate while model output of the two RCP4.5 and RCP8.5 scenarios was downloaded from CORDEX Africa Group Data library. Various statistical techniques were employed on rainfall and temperature to analyze and interpret the past and future climatic condition for rainy season over the Bale highlands. The results revealed that the climate change signal was appear over Bale Highlands. The change in temperature over Bale highlands under RCP4.5 and RCP8.5 emission scenarios shown as an increase for the period of near century, mid-century and end century. Changes of temperature under RCP8.5 was higher than the change of temperature in RCP4.5 scenario for all three periods (near, mid and end Century). Rainfall change for near, mid and end centuries under RCPs scenarios were variable.

Lagoons are highly valued coastal environments providing unique ecosystem services. However, they are fragile and vulnerable to natural processes and anthropogenic activities. Concurrently, climate change pressures, are likely to lead to severe ecological impacts on lagoon ecosystems. Among these, direct effects are mainly through changes in temperature and associated physico-chemical alterations, whereas indirect ones, mediated through processes such as extreme weather events in the catchment, include the alteration of nutrient loading patterns among others that can, in turn, modify the trophic states leading to depletion or to eutrophication. This phenomenon can lead, under certain circumstances, to harmful algal blooms events, anoxia, and mortality of aquatic flora and fauna, or to the reduction of primary production, with cascading effects on the whole trophic web with dramatic consequences for aquaculture, fishery, and recreational activities. The complexity of eutrophication processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on fragile lagoon environments. In this context, a novel framework combining Machine Learning (ML) and biogeochemical models is proposed, leveraging the potential offered by both approaches to unravel and modelling environmental systems featured by compounding pressures. Multi-Layer Perceptron (MLP) and Random Forest (RF) models are used (trained, validated, and tested) within the Venice Lagoon case study to assimilate historical heterogenous WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the biogeochemical model SHYFEM-BFM for 2049, and 2099 timeframes under RCP 8.5 are integrated to evaluate Chl-a variations under future bio-geochemical conditions forced by climate change projections. Annual and seasonal Chl-a predictions were performed out by classes based on two classification modes established on the descriptive statistics computed on baseline data: i) binary classification of Chl-a values under and over the median value, ii) multi-class classification defined by Chl-a quartiles. Results from the case study showed as the RF successfully classifies Chl-a under the baseline scenario with an overall model accuracy of about 80% for the median classification mode, and 61% for the quartile classification mode. Overall, a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) can be observed, with an opposite rising fashion for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2099 a strong increase in Chl-a is also expected during the spring one. The proposed novel framework represents a valuable approach to strengthen both eutrophication modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models.

The population cycles of rodents at northern latitudes have puzzled people for centuries, and their impact is manifest throughout the alpine ecosystem. Climate change is known to be able to drive animal population dynamics between stable and cyclic phases, and has been suggested to cause the recent changes in cyclic dynamics of rodents and their predators. But although predator-rodent interactions are commonly argued to be the cause of the Fennoscandian rodent cycles, the role of the environment in the modulation of such dynamics is often poorly understood in natural systems. Hence, quantitative links between climate-driven processes and rodent dynamics have so far been lacking. Here we show that winter weather and snow conditions, together with density dependence in the net population growth rate, account for the observed population dynamics of the rodent community dominated by lemmings (Lemmus lemmus) in an alpine Norwegian core habitat between 1970 and 1997, and predict the observed absence of rodent peak years after 1994. These local rodent dynamics are coherent with alpine bird dynamics both locally and over all of southern Norway, consistent with the influence of large-scale fluctuations in winter conditions. The relationship between commonly available meteorological data and snow conditions indicates that changes in temperature and humidity, and thus conditions in the subnivean space, seem to markedly affect the dynamics of alpine rodents and their linked groups. The pattern of less regular rodent peaks, and corresponding changes in the overall dynamics of the alpine ecosystem, thus seems likely to prevail over a growing area under projected climate change.

Planning of mitigation and adaptation strategies to a changing climate can benefit from a good understanding of climate change impacts on human life and local society, which leads to an increasing requirement for reliable projections of future climate change at regional scales. This paper presents an ensemble of high-resolution regional climate simulations for the province of Ontario, Canada, developed with the Providing Regional Climates for Impacts Studies (PRECIS) modeling system. A Bayesian statistical model is proposed through an advance to the method proposed by Tebaldi et al. for generating probabilistic projections of temperature changes at gridpoint scale by treating the unknown quantities of interest as random variables to quantify their uncertainties in a statistical way. Observations for present climate and simulations from the ensemble are fed into the statistical model to derive posterior distributions of all the uncertain quantities through a Markov chain Monte Carlo (MCMC) sampling algorithm. Detailed analyses at 12 selected weather stations are conducted to investigate the practical significance of the proposed statistical model. Following that, maps of projected temperature changes at different probability levels are presented to help understand the spatial patterns across the entire province. The analysis shows that there is likely to be a significant warming trend throughout the twenty-first century. It also suggests that people in Ontario are very likely to suffer a change greater than 2°C to mean temperature in the forthcoming decades and very unlikely to suffer a change greater than 10°C to the end of this century.

Comparing and ranking information is an important topic in social and information sciences, and in particular on the web. Its objective is to measure the difference of the preferences of voters on a set of candidates and to compute a consensus ranking. Commonly, each voter provides a total order or a bucket order of all candidates, where bucket orders allow ties. In this work we consider the generalization of total and bucket orders to partial orders and compare them by the nearest neighbor and the Hausdorff Kendall tau distances. For total and bucket orders these distances can be computed in $\mathcal{O}(n \log n)$ time. We show that the computation of the nearest neighbor Kendall tau distance is NP-hard, 2-approximable and fixed-parameter tractable for a total and a partial order. The computation of the Hausdorff Kendall tau distance for a total and a partial order is shown to be coNP-hard. The rank aggregation problem is known to be NP-complete for total and bucket orders, even for four voters and solvable in $\mathcal{O}(n\log n)$ for two voters. It is NP-complete for two partial orders and the nearest neighbor Kendall tau distance. For the Hausdorff Kendall tau distance it is in $\mathbf{\Sigma_2^p}$ , but not in NP or coNP unless $\ensuremath{\mathbf{NP}} = \ensuremath{\mathbf{coNP}} $ , even for four voters.