Model Projections Research Articles

Impact of climate change on major floods flowing into the Georges River estuary, Australia

Coastal flooding induced by storm surges and heavy rainfall is one of the most frequent climate-related natural hazards along the southeast Australian coast, home to more than 55% of the Australian population. Flooding in this densely populated region is a threat to public safety, coastal infrastructure, ecological systems and the economy. Although climate change is expected to cause an increase in major floods, few studies have quantified the potential changes in flood severity. This study quantifies the changes in flood peak discharge flowing to the Georges River estuary in Australia due to climate-change. An event-based hydrological model, Watershed Bounded Network Model (WBNM), was used to predict flood discharge. This hydrological model was forced by rainfall data obtained from the New South Wales and Australian Capital Territory Regional Climate Modelling Project version 1.5 (NARCliM1.5) for both historical and the Representative Concentration Pathway 8.5 (RCP8.5) conditions. Model calibration for the floods of March 1978 and March 2022 achieved a general agreement between the predicted and observed hydrographs, with an overall average 14% error in the peak values, further demonstrating that the modelling approach is generally reliable in projections of flood severity. Using high resolution climate model projections, the present study observed an increase of 22% in the model ensemble average from historical conditions to the RCP8.5 scenario for the 20-year average recurrence interval (ARI) 24 h extreme rainfall. This heightened extreme rainfall consequently resulted in the changes in flood discharge with an average rise of 55%. This study provides specific assessment of climate-generated risks for densely-populated regions, especially those on Australian east coast. Global studies have suggested that extreme precipitation events will increase under climate change. This study supports and enhances these assertions by using high resolution downscaling to quantify the specific changes within a large catchment.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Enhanced autumn phenology model incorporating agricultural drought

The impacts of various drought types on autumn phenology have yet to be extensively explored. We address the influence of pre-season agricultural and meteorological droughts on autumn phenology in the Northern Hemisphere. To this end, enhanced autumn phenology models incorporating drought factors was developed, contributing to a deeper understanding of these complex interactions. The study reveals that there was no significant trend of advancement or delay in the End of Season (EOS) across the Northern Hemisphere based on SIF estimates from 2001 to 2020. The cumulative and delayed impacts of pre-season agricultural drought on EOS were found to be more pronounced than those associated with meteorological drought. The analysis of various evaluation indexes shows that the performance of the Cooling Degree Days (CDD) model incorporating the Standardized Soil Moisture Drought Index (CDDSSMI) in simulating EOS in the Northern Hemisphere is >14 % higher than that of the standard CDD model. Additionally, the performance of the CDD model with the Standardized Precipitation Index (CDDSPI) in simulating EOS in the Northern Hemisphere is improved by >5.6 % compared to the standard CDD model. A comparison of future EOS projections across various models reveals that the CDD model significantly overestimates EOS in different scenarios (SSP245 and SSP585). The CDDSSMI model projects EOS approximately 7 days earlier than the CDD model, and the CDDSPI model projects EOS approximately 5 days earlier than the CDD model. This study highlights the diverse impacts of drought types on plant autumn phenology and underscores the significance of parameterizing drought impacts in autumn phenology models.

Assessing the population consequences of disturbance and climate change for the Pacific walrus

Climate change and anthropogenic disturbance are increasingly affecting wildlife at a global scale. Predicting how varying types and degrees of disturbance may interact to influence population dynamics is a key management challenge. Population consequences of disturbance (PCoD) models provide a framework to link effects of anthropogenic disturbance on an individual’s behavior and physiology to population-level changes. In the present study, we develop a Pacific walrus (Odobenus rosmarus divergens) PCoD model to encompass the population-level effects of both anthropogenic disturbance and climate change. As the Arctic becomes increasingly ice-free, walruses spend more time at coastal (vs. ice-based) haulouts, from which they must expend more energy to reach foraging areas and where they have an elevated risk of mortality. Concurrently, sea ice loss is increasing the anthropogenic footprint in the Arctic (e.g. fisheries, shipping, energy exploration), which creates additional disturbance. We applied the PCoD model to 4 scenarios (ranging from optimistic to pessimistic) which incorporate different global sea ice model projections along with varying degrees of anthropogenic disturbance. All scenarios indicated a decline in Pacific walrus vital rates by the end of the 21st century, but our results demonstrated that the intensity of that decline could be mitigated by global efforts to reduce carbon emissions, along with local management and conservation efforts to protect important coastal haulouts and foraging grounds. In summary, we introduce a flexible PCoD modeling framework in a novel context which will prove useful to researchers studying species threatened by rapid environmental change.

Optimisation of real‐scene 3D building models based on straight‐line constraints

AbstractDue to the influence of repeated textures or edge perspective transformations on building facades, building modelling based on unmanned aerial vehicle (UAV) photogrammetry often suffers geometric deformation and distortion when using existing methods or commercial software. To address this issue, a real‐scene three‐dimensional (3D) building model optimisation method based on straight‐line constraints is proposed. First, point clouds generated by unmanned aerial vehicle (UAV) photogrammetry are down‐sampled based on local curvature characteristics, and structural point clouds located at the edges of buildings are extracted. Subsequently, an improved random sample consensus (RANSAC) algorithm, considering distance and angle constraints on lines, known as co‐constrained RANSAC, is applied to further extract point clouds with straight‐line features from the structural point clouds. Finally, point clouds with straight‐line features are optimised and updated using sampled points on the fitted straight lines. Experimental results demonstrate that the proposed method can effectively eliminate redundant 3D points or noise while retaining the fundamental structure of buildings. Compared to popular methods and commercial software, the proposed method significantly enhances the accuracy of building modelling. The average reduction in error is 59.2%, including the optimisation of deviations in the original model's contour projection.

Predictive Modeling of Future Trends in US Healthcare Data and Outcomes

Predictive modeling has great potential to help guide healthcare policymaking and planning through forecasting future trends in domains such as disease prevalence, resource utilization, and costs. However, past research in this area has been limited by mostly examining small, narrow datasets that only captured specific illnesses or geographic regions. This study aimed to leverage more sophisticated predictive analytics to generate informed estimations of the most consequential healthcare trends anticipated in the United States throughout the next decade. The analysis drew upon an extensive collection of over 50 million longitudinal electronic health records spanning a 5-year timeframe, comprehensive national public health statistics from the same period, and Medicare claims encompassing 72 million beneficiaries. Advanced machine learning techniques, including neural networks and Bayesian additive regression trees, were applied to identify nonlinear relationships and temporal patterns across 500 variables related to patient demographics, medical diagnoses, therapeutic procedures, reimbursement amounts, and clinical outcomes. Models were trained using data from 2010 to 2015 then utilized to project trends and forecasts for the years 2020 to 2025. Five-fold cross-validation testing was conducted to evaluate the accuracy and generalizability of the predictive models. The model projections indicate that chronic disease prevalence nationwide will rise by approximately 40% by the conclusion of 2025, primarily fueled by growing epidemics of obesity and an increasingly aging American population. Additionally, heart disease and stroke are estimated to maintain their positioning as leading causes of death, but cases of dementia and Alzheimer's disease specifically are projected to climb even more sharply at over a 50% increase. Healthcare costs on the whole are anticipated to rise on average between 4-6% annually, and costs may potentially double for elderly patients presenting with multiple morbidities. As outpatient and home-based care options expand further, inpatient hospital facility utilization may drop marginally between 10-15%. Improved management of chronic medical conditions within local community settings could reduce preventable hospital readmissions from 25-30%. Primary care, nursing, and mental healthcare roles are likely to face looming staffing shortages as well. Telehealth adoption is forecasted to surge by approximately 45% as virtual visit formats help address access obstacles. By 2025, biologics and gene therapies could account for over 25% of total drug spending pertaining to oncology and rare disease treatment. Larger Medicaid, Medicare, and ACA commercial coverage markets may motivate higher rates of health insurance enrollment over the next few years.

Improving Students Critical Thinking Skills Using the Door Model in Elementary School Students

The problems in this study are the low level of mobility, critical thinking skills and learning outcomes of fifth grade students. Lack of motivation, lack of critical thinking skills, and low interest in learning affect learning outcomes. The solution to overcome these problems is to use the PINTU model (project based learning, investigative group, and numbered heads together) in learning. This study aims to describe teacher activities and analyze changes in student activity, critical thinking skills, and student learning outcomes in the natural science content material on the properties and shapes of objects. By using qualitative and quantitative methods of classroom action research (CAR), this research took place in three sessions in class V SDN Habau Hulu (consisting of 10 students). The types of data presented include data on teacher and student activities, critical thinking skills through observation, questionnaires, and student learning outcomes through tests and observations. Indicators of research success and learning completeness were 85% of students achieving KKM scores ? 70. The learning results showed that the number of sessions per session increased along with the acquisition of teacher activity in the implemented learning model, obtained points in Session I of 21 points, Session II of 26 points, and Session II of 28 points. Third Session The level of student activity was 70% in Session I, 80% in Session II, and 100% in Session III. Students’ critical thinking abilities were 60% in Session I, 70% in Session II, and 83% in Session III, increasing to 90%.Meanwhile, the completeness of student learning outcomes in the first session was 40%, the second session was 60%, and the third session was 90%.

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Deformation Estimation of Textureless Objects from a Single Image.

Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacturers. This work proposes a solution for the deformation estimation of textureless plastic objects using only a single RGB image. This solution encompasses a unique image dataset of five deformed parts, a novel method for generating mesh labels, sequential deformation, and a training model based on graph convolution. The proposed sequential deformation method outperforms the prevalent chamfer distance algorithm in generating precise mesh labels. The training model projects object vertices into features extracted from the input image, and then, predicts vertex location offsets based on the projected features. The predicted meshes using these offsets achieve a sub-millimeter accuracy on synthetic images and approximately 2.0 mm on real images.

Emergence of lake conditions that exceed natural temperature variability

Lake surface temperatures are projected to increase under climate change, which could trigger shifts in the future distribution of thermally sensitive aquatic species. Of particular concern for lake ecosystems are when temperatures increase outside the range of natural variability, without analogue either today or in the past. However, our knowledge of when such no-analogue conditions will appear remains uncertain. Here, using daily outputs from a large ensemble of SSP3-7.0 Earth system model projections, we show that these conditions will emerge at the surface of many northern lakes under a global warming of 4.0 °C above pre-industrial conditions. No-analogue conditions will occur sooner, under 2.4 °C of warming, at lower latitudes, primarily due to a weaker range of natural variability, which increases the likelihood of the upper natural limit of lake temperature being exceeded. Similar patterns are also projected in subsurface water, with no-analogue conditions occurring first at low latitudes and occurring last, if at all, at higher latitudes. Our study suggests that global warming will induce changes across the water column, particularly at low latitudes, leading to the emergence of unparalleled climates with no modern counterparts, probably affecting their habitability and leading to rearrangements of freshwater habitats this century.

Resetting tropospheric OH and CH4 lifetime with ultraviolet H2O absorption.

The decay of methyl chloroform, a banned ozone-depleting substance, has provided a clear observational metric of mean tropospheric hydroxyl radical (OH) abundance. Almost all current global chemistry models calculate about 15% too much OH and thus too rapid methane loss. Methane is a short-lived climate forcer, critical to achieving global warming targets, and this error affects our model projections of climate change. New observations of water vapor absorption in the ultraviolet region (290 to 350 nanometers) imply reductions in sunlight with key photolysis rates decreasing by 8 to 12% in the near-surface tropical atmosphere. Incorporation of this new mechanism in a chemistry-transport model reduces OH and methane loss by only 4%, but combined with other proposed mechanisms, such as tropospheric halogen chemistry (7%), we may be able to resolve this conundrum.

A sustainable blue economy may not be possible in Tanzania without cutting emissions

Balancing blue growth with the conservation of wild species and habitats is a key challenge for global ocean management. This is exacerbated in Global South nations, such as Tanzania, where climate-driven ocean change requires delicate marine spatial planning (MSP) trade-offs to ensure climate resilience of marine resources relied upon by coastal communities. Here, we identified challenges and opportunities that climate change presents to the near-term spatial management of Tanzania's artisanal fishing sector, marine protected areas and seaweed farming. Specifically, spatial meta-analysis of climate modelling for the region was carried out to estimate the natural distribution of climate resilience in the marine resources that support these socially important sectors. We estimated changes within the next 20 and 40 years, using modelling projections forced under global emissions trajectories, as well as a wealth of GIS and habitat suitability data derived from globally distributed programmes. Multi-decadal analyses indicated that long-term climate change trends and extreme weather present important challenges to the activity of these sectors, locally and regionally. Only in few instances did we identify areas exhibiting climate resilience and opportunities for sectoral expansion. Including these climate change refugia and bright spots in effective ocean management strategies may serve as nature-based solutions: promoting adaptive capacity in some of Tanzania's most vulnerable economic sectors; creating wage-gaining opportunities that promote gender parity; and delivering some economic benefits of a thriving ocean where possible. Without curbs in global emissions, however, a bleak future may emerge for globally valuable biodiversity hosted in Tanzania, and for its coastal communities, despite the expansion of protected areas or curbs in other pressures. Growing a sustainable ocean economy in this part of the Global South remains a substantial challenge without global decarbonization.

Large-ensemble assessment of the Arctic stratospheric polar vortex morphology and disruptions

Abstract. The stratospheric polar vortex (SPV) comprises strong westerly winds during winter in each hemisphere. Despite ample knowledge on the SPV's high variability and its frequent disruptions by sudden stratospheric warmings (SSWs) in the Northern Hemisphere (NH), questions on how well current climate models can simulate these dynamics remain open. Specifically the accuracy in reproducing SPV morphology and the differentiation between split and displacement SSW events are crucial to assess the models in this regard. In this study, we evaluate the capability of climate models to simulate the NH SPV by comparing large ensembles of historical simulations to ERA5 reanalysis data. For this, we analyze geometric-based diagnostics at three pressure levels that describe SPV morphology. Our analysis reveals that no model exactly reproduces SPV morphology of ERA5 in all diagnostics at all altitudes. Concerning the SPV morphology as stretching (aspect ratio) and location (centroid latitude) parameters, most models are biased to some extent, but the strongest deviations can be found for the vortex-splitting parameter (excess kurtosis). Moreover, some models underestimate the variability of SPV strength. Assessing the reliability of the ensembles in distinguishing SSWs subdivided into SPV displacement and split events, we find large differences between the model ensembles. In general, SPV displacements are represented better than splits in the simulation ensembles, and high-top models and models with finer vertical resolution perform better. A good performance in representing the morphological diagnostics does not necessarily imply reliability and therefore a good performance in simulating displacements and splits. Assessing the model biases and their representation of SPV dynamics is needed to improve credibility of climate model projections, for example, by giving stronger weightings to better performing models.

Long-term changes in tropospheric temperature in India: Insights from radiosonde measurements, reanalysis data and CMIP6 model projections

The continuous rise in greenhouse gases (GHGs) in the atmosphere is one of the major factors responsible for the global warming and enhanced tropospheric temperature. In this context, we examine the long-term spatiotemporal changes in the tropospheric temperature (1000–100 hPa) in India using radiosonde measurements and the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Application Version 2 (MERRA2) and National Center for Environmental Prediction (NCEP) for the period 1980–2020. We also investigate the future projections of tropospheric temperature in the Shared Socio-economic Pathways (SSPs) 2–4.5 and 5–8.5 by using the Coupled Model Intercomparison Project 6 (CMIP6) results for the period 2015–2100 over India. Our analysis shows a significant positive trend (0.01–0.04 °C/yr) in the annual mean temperature in the upper troposphere (500–100 hPa) in India during the period 1980–2020. The warming trends are also noticeable in the middle and lower troposphere of India. Among the regions, the northeast, north central and northwest India exhibit significant warming of about 0.01–0.06 °C/yr in the upper troposphere (500–100 hPa). On the other hand, most regions show a decline in temperature at the tropopause (100 hPa), which is stronger in the interior peninsular, north central and east coast of India, at about −0.05 ± 0.01°C/yr. All reanalysis data show consistent warming and cooling trends in the respective regions as observed from the radiosondes, albeit slight differences in their values. The future high emission scenario of SSP5–8.5 shows a warming trend of about 0.04–0.08 °C/yr at 1000–200 hPa by the end of the century, which is highest at 250 hPa. The increasing trend in tropospheric temperature is a serious concern, which calls for adaptation and mitigation measures to alleviate the impacts of this accelerated warming in India.

Climate change impact on Mediterranean viticultural regions and site-specific climate risk-reduction strategies

The global increase in extreme weather and climate events may dramatically impact agriculture, food safety, and socioeconomic dynamics. The Mediterranean basin is already exposed to extreme climatic events, severely challenging viticulture, a pivotal Mediterranean agro–industry. This study aims to understand better how climate is expected to evolve in six viticulturally important Mediterranean regions in Portugal, Italy, Turkey and Morocco, using a 4–member ensemble of climatic model projections under Representative Concentration Pathways (RCP) 4.5 and 8.5 for 2041–2070, and using the 1981–2010 period as a baseline. By comparing the main specific challenges these locations will face, we comparatively define the best strategies to reduce the impacts of climate change at the national and regional levels. Projections show increases in overall temperatures, up to + 3.6°C than the historical baseline, whilst precipitation projections indicate decreases that could reach 36% of the overall annual precipitation. Biological effective degree days, consecutive dry days, growing season length, tropical nights, or very heavy precipitation days, also show challenging prospects for viticulture in these countries. A screening of the adaptative strategies already undertaken in the studied countries suggests that growers are taking reactive rather than preventive strategies. Moreover, the discussion of the most suitable strategies in this study is region–specific, i.e., prioritised by the specific needs of each location. The conclusions drawn herein may support local growers, improving their decision–making based on the most adequate adaptive strategies to their conditions, thus optimising their sustainable production under changing climates.

Evolving Arctic maritime hazards: Declining sea ice and rising waves in the Northwest Passage

The ongoing and projected retreat of Arctic sea ice has garnered international interest toward the utilization of Arctic maritime corridors for shipping, tourism, and development. Yet, with potential for increasing traffic in Arctic regions, it's important to consider additional environmental variables affected by climate change which may threaten maritime operations. Here, we use four climate model projections to produce ocean wave simulations and investigate the future magnitude and seasonality of sea ice risk coupled with wave hazards. Analyzing the potential 5 mo shipping season spanning July to November along the Northwest Passage maritime route between 2020 and 2070, our results show a substantial decline in sea ice risk over the analysis time period, resulting in near open-water conditions along the route for a 5 mo period by 2070. However, as seasonal ice coverage retreats, there is a significant upward trend in wave heights along the route during July and November, with the timing of the greatest wave height shifting away from September toward later in the season. This result is pertinent as the possibility of seasonally unprecedented extreme waves coupled with subfreezing late fall temperatures makes for an especially hazardous environment, thus emphasizing the importance of considering the interaction between evolving sea ice and interdependent hazards when predicting the risks and challenges faced by Arctic maritime operations.

Exploring adaptation strategies for smallholder farmers in dryland farming systems and impact on pearl millet production under climate change in West Africa

Understanding and identifying appropriate adaptation optons for cropping systems and management practices at spatial and temporal scales is an important prerequisite for scaling. Pearl millet (Pennisetum glaucum (L) R. Br.) could be regarded as a risk-reducing measure crop under climate change when coupled with tactical agronomic management practices. In this study, we assess the impacts of adaptation strategies such as cultivar type, planting windows, and fertilizer strategies on pearl millet production under rainfed farming systems over Nigeria and Senegal using the Agricultural Production Systems Simulator (APSIM) model. The impact of climate change on millet yield was evaluated using a validated APSIM-millet module that utilized yield data collected through participatory research and extension approach (PREA) in contrasting environments. The climate model projections for the mid-century period (2040–2069) were compared against a baseline period of 1980–2009 for both locations. During the simulation, two millet varieties (improved local and dual-purpose) with two sowing regimes were considered comparing traditional farmers’ sowing window (dry sowing) and agronomic sowing window (planting based on the onset of the rainfall) at three different fertilizer levels [low (23 kg N ha−1), medium (40.5 kg N ha−1), and high (68.5 kg N ha−1) respectively]. The performance of the APSIM-millet module was found to be satisfactory as indicated by the low Root Means Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) values. The range for grain yield was between 17.7% and 25.8%, while for AGB it was between 18.6% and 21.4%. The results showed that farmers’ sowing window simulated slightly higher grain yield than the agronomic sowing window for improved local millet cultivar indicating yield increased by 8–12%. However, the projected changes in the mid-century (2040–2069) resulted in a decline in yield against baseline climate for both varieties and sowing windows, indicating the negative impact of climate change (CC) on yield productivity. The comparison between dual-purpose millet and improved local millet indicates that disseminating the improved millet variety and implementing early sowing could be an effective adaptation strategy in reducing risks and losses caused by climate change. Similarly, low magnitude impacts simulated on grain yield (< −8% in Nigeria compared to > −8% in Senegal) even though both locations are in the same agroecological zone.

Recent and near‐term future changes in impacts‐relevant seasonal hydroclimate in the world's Mediterranean climate regions

AbstractChange over recent decades in the world's five Mediterranean Climate Regions (MCRs) of quantities of relevance to water resources, ecosystems and fire are examined for all seasons and placed in the context of changes in large‐scale circulation. Near‐term future projections are also presented. It is concluded that, based upon agreement between observational data sets and modelling frameworks, there is strong evidence of radiatively‐driven drying of the Chilean MCR in all seasons and southwest Australia in winter. Observed drying trends in California in fall, southwest southern Africa in fall, the Pacific Northwest in summer and the Mediterranean in summer agree with radiatively‐forced models but are not reproduced in a model that also includes historical sea surface temperature (SST) forcing, raising doubt about the human‐origin of these trends. Observed drying in the Mediterranean in winter is stronger than can be accounted for by radiative forcing alone and is also outside the range of the SST‐forced ensemble. It is shown that near surface vapour pressure deficit (VPD) is increasing almost everywhere but that, surprisingly, this is contributed to in the Southern Hemisphere subtropics to mid‐latitudes by a decline in low‐level specific humidity. The Southern Hemisphere drying, in terms of precipitation and specific humidity, is related to a poleward shift and strengthening of the westerlies with eddy‐driven subsidence on the equatorward side. Model projections indicate continued drying of Southern Hemisphere MCRs in winter and spring, despite ozone recovery and year‐round drying in the Mediterranean. Projections for the North American MCR are uncertain, with a large contribution from internal variability, with the exception of drying in the Pacific Northwest in summer. Overall the results indicate continued aridification of MCRs other than in North America with important implications for water resources, agriculture and ecosystems.

Potential climate change effects on the distribution of urban and sylvatic dengue and yellow fever vectors

ABSTRACT Climate change may increase the risk of dengue and yellow fever transmission by urban and sylvatic mosquito vectors. Previous research primarily focused on Aedes aegypti and Aedes albopictus. However, dengue and yellow fever have a complex transmission cycle involving sylvatic vectors. Our aim was to analyze how the distribution of areas favorable to both urban and sylvatic vectors could be modified as a consequence of climate change. We projected, to future scenarios, baseline distribution models already published for these vectors based on the favorability function, and mapped the areas where mosquitoes’ favorability could increase, decrease or remain stable in the near (2041–2060) and distant (2061–2080) future. Favorable areas for the presence of dengue and yellow fever vectors show little differences in the future compared to the baseline models, with changes being perceptible only at regional scales. The model projections predict dengue vectors expanding in West and Central Africa and in South-East Asia, reaching Borneo. Yellow fever vectors could spread in West and Central Africa and in the Amazon. In some locations of Europe, the models suggest a reestablishment of Ae. aegypti, while Ae. albopictus will continue to find new favorable areas. The results underline the need to focus more on vectors Ae. vittatus, Ae. luteocephalus and Ae. africanus in West and Central sub-Saharan Africa, especially Cameroon, Central Africa Republic, and northern Democratic Republic of Congo; and underscore the importance of enhancing entomological monitoring in areas where populations of often overlooked vectors may thrive as a result of climate changes.

Projected data assimilation using sliding window proper orthogonal decomposition

Prediction of the state evolution of complex high-dimensional nonlinear systems is challenging due to the nonlinear sensitivity of the evolution to small inaccuracies in the model. Data Assimilation (DA) techniques improve state estimates by combining model simulations with real-time data. Few DA techniques can simultaneously handle nonlinear evolution, non-Gaussian uncertainty, and the high dimension of the state. We recently proposed addressing these challenges using a POD technique that projects the physical and data models into a reduced-dimensional subspace. Proper Orthogonal Decomposition (POD) is a tool to extract spatiotemporal patterns (modes) that dominate the observed data. We combined the POD-based projection operator, computed in an offline fashion, with a DA scheme that models non-Gaussian uncertainty in lower dimensional subspace. If the model parameters change significantly during time evolution, the offline computation of the projection operators ceases to be useful. We address this challenge using a Sliding Window POD (SW-POD), which recomputes the projection operator based on a sliding subset of snapshots from the entire evolution. The physical model projection is updated dynamically in terms of modes and number of modes, and the data model projection is also chosen to promote a sparse approximation. We test the efficacy of this technique on a modified Lorenz'96 model (L96) with a time-varying forcing and compare it with the time-invariant offline projected algorithm. In particular, dynamically determined physical and data model projections decrease the Root Mean Squared Error (RMSE) and the resampling rate.