scholarly journals Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Adrian M. Tompkins ◽  
Cyril Caminade ◽  
Anne E. Jones ◽  
Grigory Nikulin ◽  
...  
Keyword(s):  
Climate Change ◽  
Malaria Transmission ◽  
Climate Models ◽  
Climate Model ◽  
Eastern Africa ◽  
Future Climate Change ◽  
Climate Projections ◽  
Model Ensemble ◽  
East African Community ◽  
Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

scholarly journals Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention

2019 ◽  
Author(s):  
Sadie J. Ryan ◽  
Catherine A. Lippi ◽  
Fernanda Zermoglio
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Malaria Transmission ◽  
Climate Model ◽  
Population Projections ◽  
Eastern Africa ◽  
Parasite Development ◽  
Worst Case ◽  
Population At Risk ◽  
Recent Climate

AbstractBackgroundMalaria continues to be a disease of massive burden in Africa, and the public health resources targeted at surveillance, prevention, control, and intervention comprise large outlays of expense. Malaria transmission is largely constrained by the suitability of the climate for Anopheles mosquitoes and Plasmodium parasite development. Thus, as climate changes, we will see shifts in geographic locations suitable for transmission, and differing lengths of seasons of suitability, which will require changes in the types and amounts of resources.MethodsWe mapped the shifting geographic risk of malaria transmission, in context of changing seasonality (i.e. endemic to epidemic, and vice-versa), and the number of people affected. We applied a temperature-dependent model of malaria transmission suitability to continental gridded climate data for multiple future climate model projections. We aligned the resulting outcomes with programmatic needs to provide summaries at national and regional scales for the African continent. Model outcomes were combined with population projections to estimate the population at risk at three points in the future, 2030, 2050, and 2080, under two scenarios of greenhouse gas emissions (RCP4.5 and RCP8.5).ResultsGeographic shifts in endemic and seasonal suitability for malaria transmission were observed across all future scenarios of climate change. The worst-case regional scenario (RCP8.5) of climate change places an additional 75.9 million people at risk from endemic (10-12 months) exposure to malaria transmission in Eastern and Southern Africa by the year 2080, with the greatest population at risk in Eastern Africa. Despite a predominance of reduction in season length, a net gain of 51.3 million additional people will be put at some level of risk in Western Africa by midcentury.ConclusionsThis study provides an updated view of potential malaria geographic shifts in Africa under climate change for the more recent climate model projections (AR5), and a tool for aligning findings with programmatic needs at key scales for decision makers. In describing shifting seasonality, we can capture transitions between endemic and epidemic risk areas, to facilitate the planning for interventions aimed at year-round risk versus anticipatory surveillance and rapid response to potential outbreak locations.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Energy and indoor thermal comfort performance of a Swedish residential building under future climate change conditions

2020 ◽  
Vol 172 ◽  
pp. 02001
Author(s):  
Ambrose Dodoo
Keyword(s):  
Climate Change ◽  
Thermal Comfort ◽  
Residential Building ◽  
Adaptation Strategies ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Mean Annual Temperature ◽  
Indoor Thermal Comfort ◽  
Recent Climate

The latest climate change projections for Sweden suggest mean annual temperature increase of up to 5.5 °C by 2100, compared to 1961-1990 levels. In this study we investigate the potential impacts of climate change on the energy demand for space conditioning, overheating risk and indoor thermal comfort of a modern multi-storey residential building in Sweden. We explore climate change adaptation strategies to improve the building’s performance under the climate change conditions, including increased ventilation, solar shading, improved windows and mechanical cooling. The building is analysed under future climate projections for the 2050-2059 time frame, with representative concentration pathway (RCP) 2.6, 4.5 and 8.5 scenarios. The building’s performances under these future climates are compared to those under the historical climate of 1961-1990 and recent climate of 1981-2010. The results suggest that climate change will significantly influence energy performance and indoor comfort conditions of buildings in the Swedish context. Overheating hours and Predicted Percentage of Dissatisfied (PPD) increased significantly under the future climate scenarios. Furthermore space heating demand is reduced and cooling demand is increased for the studied building. However, effective adaptation strategies significantly improved the buildings’ energy and indoor climate performances under both current and future climate conditions.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

2019 ◽  
Vol 11 (17) ◽  
pp. 4764 ◽  
Author(s):  
Anna Sperotto ◽  
Josè Luis Molina ◽  
Silvia Torresan ◽  
Andrea Critto ◽  
Manuel Pulido-Velazquez ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Bayesian Networks ◽  
Climate Model ◽  
Future Climate Change ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Large Variability ◽  
Nutrient Loadings

With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)–regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041–2070) and long-term (2071–2100) periods with respect to the baseline (1983–2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM–RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Simulation of the mid-Pliocene Warm Period using HadGEM3-GC31-LL: Pliocene climate relative to the pre-industrial era, previous model versions, other climate models and proxy data

2021 ◽  
Author(s):  
Charles Williams ◽  
Daniel Lunt ◽  
Alistair Sellar ◽  
William Roberts ◽  
Robin Smith ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
High Sensitivity ◽  
Warm Period ◽  
Future Climate ◽  
Future Climate Change ◽  
Last Interglacial ◽  
Proxy Data ◽  
Global Mean

<p>To better understand the processes contributing to future climate change, palaeoclimate model simulations are an important tool because they allow testing of the models’ ability to simulate very different climates than that of today.  As part of CMIP6/PMIP4, the latest version of the UK’s physical climate model, HadGEM3-GC31-LL (hereafter, for brevity, HadGEM3), was recently used to simulate the mid-Holocene (~6 ka) and Last Interglacial (~127 ka) simulations and the results were compared to the preindustrial era, previous versions of the same model and proxy data (see Williams et al. 2020, Climate of the Past).  Here, we use the same model to go further back in time, presenting the results from the mid-Pliocene Warm Period (~3.3 to 3 ma, hereafter the “Pliocene” for brevity).  This period is of particular interest when it comes to projections of future climate change under various scenarios of CO<sub>2</sub> emissions, because it is the most recent time in Earth’s history when CO<sub>2</sub> levels were roughly equivalent to today.  In response, albeit due to slower mechanisms than today’s anthropogenic fossil fuel driven-change, during the Pliocene global mean temperatures were 2-3°C higher than today, more so at the poles.</p><p> </p><p>Here, we present results from the HadGEM3 Pliocene simulation.  The model is responding to the Pliocene boundary conditions in a manner consistent with current understanding and existing literature.  When compared to the preindustrial era, global mean temperatures are currently ~5°C higher, with the majority of warming coming from high latitudes due to polar amplification from a lack of sea ice.  Relative to other models within the Pliocene Modelling Intercomparison Project (PlioMIP), this is the 2<sup>nd</sup> warmest model, with the majority of others only showing up to a 4.5°C increase and many a lot less.  This is consistent with the relatively high sensitivity of HadGEM3, relative to other CMIP6-class models.  When compared to a previous generation of the same UK model, HadCM3, similar patterns of both surface temperature and precipitation changes are shown (relative to preindustrial).  Moreover, when the simulations are compared to proxy data, the results suggest that the HadGEM3 Pliocene simulation is closer to the reconstructions than its predecessor.</p>

Spatiotemporal Changes in Headwater Flow Contributions to Major Rivers of Germany under Changing Climate

2020 ◽  
Author(s):  
Akash Koppa ◽  
Thomas Remke ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Sebastian Müller ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Moisture ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Model Ensemble ◽  
Hydrologic Models ◽  
River Temperature

<p>Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.</p><p>Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:</p><p><strong>Climate Modeling:</strong> We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). <strong>Hydrologic Modeling:</strong> To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. <strong>Streamflow Routing:</strong> To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. <strong>River Temperature Modeling:</strong> A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.</p><p>The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project<strong><sup>1,2,3,4</sup></strong>. The newly created projections in this project will be compared against those created in the EDgE project.  The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.</p><p><strong>References</strong></p><p><strong>[1] </strong>Marx,<em> A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.</em></p><p><strong>[2]</strong><em> Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.</em></p><p><strong>[3]</strong> Samaniego,<em> L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.</em></p><p><strong>[4]</strong> Thober,<em> S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.</em></p><p> </p><p> </p><p> </p>

scholarly journals Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

2014 ◽  
Vol 18 (9) ◽  
pp. 3367-3392 ◽  
Author(s):  
S. G. Leibowitz ◽  
R. L. Comeleo ◽  
P. J. Wigington Jr. ◽  
C. P. Weaver ◽  
P. E. Morefield ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Future Climate Change ◽  
Climate Indices ◽  
Western Slope ◽  
Vulnerability To Climate Change ◽  
Basin Scale ◽  
Wet Climate ◽  
John Day

Abstract. Classification can allow for evaluations of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to evaluate vulnerability to potential future climate change at statewide and basin scales in the state of Oregon. The HL classification has five components: climate, seasonality, aquifer permeability, terrain, and soil permeability. We evaluate changes when the 1971–2000 HL climate indices are recalculated using 2041–2070 simulation results from the ECHAM (European Centre HAMburg) and PCM (Parallel Climate Model) climate models with the A2, A1b, and B1 emission scenarios. Changes in climate class were modest (4–18%) statewide. However, there were major changes in seasonality class for five of the six realizations (excluding PCM_B1): Oregon shifts from being 13% snow-dominated to 4–6% snow-dominated under these five realizations, representing a 56–68% reduction in snowmelt-dominated area. At the basin scale, simulated changes for the Siletz Basin, in Oregon's Coast Range, include a small switch from very wet to wet climate, with no change in seasonality. However, there is a modest increase in fall and winter water due to increased precipitation. For the Sandy Basin, on the western slope of the Cascades, HL climate class does not change, but there are major changes in seasonality, especially for areas with low aquifer permeability, which experiences a 100% loss of spring seasonality. This would reduce summer baseflow, but effects could potentially be mitigated by streamflow buffering effects provided by groundwater in the high aquifer permeability portions of the upper Sandy. The Middle Fork John Day Basin (MFJD), in northeastern Oregon, is snowmelt-dominated. The basin experiences a net loss of wet and moist climate area, along with an increase in dry climate area. The MFJD also experiences major shifts from spring to winter seasonality, representing a 20–60% reduction in snowmelt-dominated area. Altered seasonality and/or magnitude of seasonal streamflows could potentially affect survival, growth and reproduction of salmonids in these watersheds, with the greatest effects projected for the MFJD. A major strength of the HL approach is that results can be applied to similarly classified, ungaged basins. Information resulting from such evaluations can help inform management responses to climate change at regional and basin scales without requiring detailed modeling efforts.

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