Ensemble Of Climate Projections Research Articles

Assessment of the impacts of climate change on the construction of homogeneous climatic regions and ensemble climate projections using CMIP6 data over Pakistan

In the 21st century, the global environment faces significant threats due to climate change, ranging from floods and droughts to heat waves and other extreme weather events. In recent decades, Pakistan has experienced severe flooding and prolonged droughts, compounding challenges related to livelihoods, public health, and population displacement. The availability of precise and robust climate change information is paramount in addressing and mitigating the adverse impacts on a region's environment and its communities. The present study intends to assess the effects of climate change on the development of homogenous climate regions (HCRs) and then developed ensemble climate projections under the shared socioeconomic pathways (SSPs) using CMIP6 data. In the first step, HCRs were developed by integrating the cluster analysis approach and L-moment technique, identifying five distinct climatic regions. HCRs were developed using both observed data and ensemble baseline data which allowed us to thoroughly evaluate the ensemble data's performance. Subsequently, we extended our analysis to the future period (2015–2044) using ensemble climate data according to SSP2–4.5 and SSP5–8.5. Our findings indicate that, under both SSPs, certain stations exhibit discordant climatic trends in the future due to climate change. Ensemble climate projections were developed over each region for both SSPs for the duration of 2015–2044. A significant increase of 2–3.20 °C has been noted in the summer season in zone 3 and zone 5 (a region that has one of the world's largest glaciers) in maximum as well as minimum temperatures. Temperature has upward trend in the other region as well while precipitation has a mixed trend in all regions. The insights gleaned from this study hold significance and offer valuable guidance for decision-makers involved in water management, agricultural planning, and disaster management.

Quantifying overheating risk in English schools: A spatially coherent climate risk assessment

Climate adaptation decision making can be informed by a quantification of current and future climate risk. This is important for understanding which populations and/or infrastructures are most at risk in order to prioritise adaptation action. When assessing the risk of overheating in buildings, many studies use advanced building models to comprehensively represent the vulnerability of the building to overheating, but often use a limited representation of the meteorological (hazard) information which does not vary realistically in space. An alternative approach for quantifying risk is to use a spatial risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way, allowing for risk to be compared across different locations. Here we present a novel application of an open-source CLIMADA-based spatial risk assessment framework to an ensemble of climate projections to assess overheating risk in ∼20,000 schools in England. In doing so, we demonstrate an approach for bringing together the advantages of open-source spatial risk assessment frameworks, data science techniques, and physics-based building models to assess climate risk in a spatially consistent way, allowing for the prioritisation of adaptation action in this vulnerable young population. Specifically, we assess the expected number of days each school overheats (internal operative temperature exceeds a high threshold) in a school-year based on three global warming levels (recent past, 2 °C and 4 °C warmer than pre-industrial). Our results indicate an increase in this risk in future warmer climates, with the relative frequency of overheating at internal temperatures in excess of 35 °C increasing more than at 26 °C. Indeed, this novel demonstration of the approach indicates that the most at-risk schools could experience up to 15 school days of internal temperature in excess of 35 °C in an average year if the climate warms to 2 °C above pre-industrial. Finally, we demonstrate how the spatial consistency in the output risk could enable the prioritisation of high risk schools for adaptation action.

On the assessment of the wave modeling uncertainty in wave climate projections

Abstract This study investigates the epistemic uncertainty associated with the wave propagation modeling in wave climate projections. A single-forcing, single-scenario, seven-member global wave climate projection ensemble is used, developed using three wave models with a consistent numerical domain. The uncertainty is assessed through projected changes in wave height, wave period, and wave direction. The relative importance of the wave model used and its internal parameterization are examined. The former is the dominant source of uncertainty in approximately two-thirds of the global ocean. The study reveals divergences in projected changes from runs of different models and runs of the same model with different parameterizations over 75% of the ensemble mean change in several ocean regions. Projected changes in the wave period shows the most significant uncertainties, particularly in the Pacific Ocean basin, while the wave height shows the least. Over 30% of global coastlines exhibit significant uncertainties in at least two out of the three wave climate variables analyzed. The coasts of western North America, the Maritime Continent and the Arabian Sea show the most significant wave modeling uncertainties.

Cascading climate effects in deep reservoirs: Full assessment of physical and biogeochemical dynamics under ensemble climate projections and ways towards adaptation

AbstractWe coupled twenty-first century climate projections with a well-established water quality model to depict future ecological changes of Rappbode Reservoir, Germany. Our results document a chain of climate-driven effects propagating through the aquatic ecosystem and interfering with drinking water supply: intense climate warming (RCP8.5 scenario) will firstly trigger a strong increase in water temperatures, in turn leading to metalimnetic hypoxia, accelerating sediment nutrient release and finally boosting blooms of the cyanobacterium Planktothrix rubescens. Such adverse water quality developments will be suppressed under RCP2.6 and 6.0 indicating that mitigation of climate change is improving water security. Our results also suggested surface withdrawal can be an effective adaptation strategy to make the reservoir ecosystem more resilient to climate warming. The identified consequences from climate warming and adaptation strategies are relevant to many deep waters in the temperate zone, and the conclusion should provide important guidances for stakeholders to confront potential climate changes.

Assessing present and future risk of water damage using building attributes, meteorology, and topography*

Abstract Weather-related risk makes the insurance industry inevitably concerned with climate and climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise to compensations for the induced physical damages and business interruptions. In this work, we establish a nationwide, building-specific risk score for water damage associated with pluvial flooding in Norway. We fit a generalised additive model that relates the number of water damages to a wide range of explanatory variables that can be categorised into building attributes, climatological variables, and topographical characteristics. The model assigns a risk score to every location in Norway, based on local topography and climate, which is not only useful for insurance companies but also for city planning. Combining our model with an ensemble of climate projections allows us to project the (spatially varying) impacts of climate change on the risk of pluvial flooding towards the middle and end of the 21st century.

Avoidable heat risk under scenarios of carbon neutrality by mid-century

To prevent anthropogenic warming of the climate system above dangerous thresholds, governments are required by the Paris Agreement to peak global anthropogenic CO2 emissions and to reach a net zero CO2 emissions level (also known as carbon neutrality). Growing concerns are being expressed about the increasing heat stress caused by the interaction of changes in temperature and humidity in the context of global warming. Although much effort has been made to examine future changes in heat stress and associated risks, gaps remain in understanding the quantitative benefits of heat-risk avoidance from carbon-neutral policies, limited by the traditional climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Here we quantify the avoided heat risk during 2040–2049 under two scenarios of global carbon neutrality by 2060 and 2050, i.e., moderate green (MODGREEN) and strong green (STRGREEN) recovery scenarios, relative to the baseline scenario (FOSSIL), based on multi-model large ensemble climate projections from a new climate model intercomparison project (CovidMIP) that endorsed by CMIP6. We show that global population exposure to extreme heat stress increases by approximately four times its current level during 2040–2049 under the FOSSIL scenario, whereas the heat exposure could be reduced by as much as 12 % and 23 % under the MODGREEN and STRGREEN scenarios, respectively. Moreover, global mean heat-related mortality risk is mitigated by 14 % (24 %) under the MODGREEN (STRGREEN) scenario during 2040–2049 relative to the FOSSIL scenario. Additionally, the aggravating heat risk could be mitigated by around a tenth by achieving carbon neutrality 10 years earlier (2050 versus 2060). In terms of spatial pattern, this heat-risk avoidance from low-carbon policies is typically greater in low-income countries. Our findings assist governments in advancing early climate change mitigation policy-making.

Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin

AbstractThe genesis of floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels, threatening cities and communities along the riverbanks. For better understanding the mechanisms (origin and composition) of flood events in large and complex basins, we capture and share the story behind major historic and projected streamflow peaks in the Rhine River basin. Our analysis is based on hydrological simulations with the mesoscale Hydrological Model forced with both meteorological observations and an ensemble of climate projections. The spatio‐temporal analysis of the flood events includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, areal extents of events, and the above‐average runoff from major sub‐basins up to 10 days before a streamflow peak. We introduce and assess the analytical setup by presenting the flood genesis of the two well‐known Rhine floods that occurred in January 1995 and May 1999. We share our extensive collection of event‐based Rhine River flood genesis, which can be used in‐ and outside the scientific community to explore the complexity and diversity of historic and projected flood genesis in the Rhine basin. An interactive web‐based viewer provides easy access to all major historic and projected streamflow peaks at four locations along the Rhine. The comparison of peak flow genesis depending on different warming levels elucidates the role of changes in snow cover and precipitation characteristics in the (pre‐)Alps for flood hazards along the entire channel of the Rhine. Furthermore, our results suggest a positive correlation between flood magnitudes and areal extents of an event. Further hydro‐climatological research is required to improve the understanding of the climatic impact on the Rhine and beyond.

Risk-based versus storyline approaches for global warming impact assessment on basin-averaged extreme rainfall: a case study for Typhoon Hagibis in eastern Japan

Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,…etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.

Where are the coexisting parallel climates? Large ensemble climate projections from the point of view of chaos theory.

We review the recent results of large ensemble climate projections considering them to be the simulations of chaotic systems. The quick spread of an initially localized ensemble in the first weeks after initialization is an appearance of the butterfly effect, illustrating the unpredictability of the dynamics. We show that the growth rate of uncertainty (an analog of the Lyapunov exponent) can be determined right after initialization. The next phase corresponds to a convergence of the no longer localized ensemble to the time-dependent climate attractor and requires a much longer time. After convergence takes place, the ensemble faithfully represents the climate dynamics. Concerning a credible simulation, the observed signal should then wander within the spread of the converged ensemble all the time, i.e., to behave just as any of the ensemble members. As a manifestation of the chaotic-like climate dynamics, one can imagine that beyond the single, observed time-dependent climate, a plethora of parallel climate realizations exists. Converged climate ensembles also define the probability distribution by which the physical quantities of the different climate realizations occur. Large ensemble simulations were shown earlier to be credible in the sense formulated. Here, in addition, an extended credibility condition is given, which requires the ensemble to be a converged ensemble, valid also for low-dimensional models. Interestingly, to the best of our knowledge, no low-order physical or engineering systems subjected to time-dependent forcings are known for which a comparison between simulation and experiment would be available. As illustrative examples, the CESM1-LE climate model and a chaotic pendulum are taken.

Downscaling ensemble climate projections to urban scale: Brussels's future climate at 1.5 °C, 2 °C, and 3 °C global warming

There is an increasing need to obtain climate projections for cities using an ensemble approach for uncertainty estimation. Yet, current-day computational resources are too limited to dynamically downscale GCM ensembles to urban scale. Here, a recently developed and validated statistical-dynamical computationally-cheap method is employed to downscale ten EURO-CORDEX climate projections over Brussels (Belgium) covering the period 1971–2100. Results show that, under the Paris agreement, summer mean projected temperature in Brussels will rise by 3.6 °C to 4.1 °C [+ − 0.7 °C] on average. The Urban Heat Island (UHI) intensity does not increase under future global warming with even a slight decrease under heatwave (HW) conditions by 0.1 °C (±0.1 °C). However, the number of HW days is projected to be 30.6% and 158.9% higher for the 2 °C and 3 °C Global Warming Levels (GWL), respectively, as compared to 1.5 °C GWL. The heat stress during HW periods also follows the same trend: compared to a 1.5 °C GWL, the number of extreme heat stress days at 2 °C (3 °C) GWL will increase by 29% (91%) on average inside the city. The results can be used in support of adaptation measures, which should be considered for future resilience of the city of Brussels.

Negative Storm Surges in the Elbe Estuary—Large-Scale Meteorological Conditions and Future Climate Change

Negative storm surges in the Elbe estuary can affect shipping as well as shoreline infrastructure. The significant reduction of water level caused by strong offshore winds can lead to extreme low water events, which endanger waterfront structures. The current study analyses the large-scale meteorological conditions inducing such situations and possible future changes due to climate change. The analysis is based on tide gauge data from Cuxhaven, atmospheric reanalysis data and an objective weather classification approach. It is found that south-easterly wind directions in combination with strong gales favour extreme low water events at Cuxhaven. Furthermore, the analysis of a single model large ensemble of climate projections shows a significant decrease in the frequency of such conditions for the far future (2071–2100). Regarding future global mean sea level rise the simulation results of a sensitivity study indicate that water levels during such extreme events approximately follow the development of the mean sea level rise. Therefore, our study suggests that both meteorological conditions and mean sea levels in a warmer future climate will be less favourable for the occurrence of extreme low water events in the Elbe estuary.

Future changes in extreme storm surge based on a maximum potential storm surge model for East Asia

ABSTRACT We analyzed tropical cyclones (TC) based on the theory of Maximum Potential Intensity (MPI) and Maximum Potential Surge (MPS) for a long-term assessment of extreme TC intensity and storm surge heights. We investigated future changes in the MPI fields and MPS for different global warming levels based on 150-year continuous scenario projections (HighResMIP) and large ensemble climate projections (d4PDF/d2PDF). Focusing on the Western North Pacific Ocean (WNP), we analyzed future changes in the MPI and found that it reached a maximum in the latitudinal range of 30–40°N in September. We also analyzed future changes in the MPS in major bays of East Asia and along the Pacific coast of Japan. Future changes in the MPS were projected, and it was confirmed that changes in the MPS are larger in bays where large storm surge events have occurred in the past.

En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Climate refugia on the Great Barrier Reef fail when global warming exceeds 3°C.

Increases in the magnitude, frequency, and duration of warm seawater temperatures are causing mass coral mortality events across the globe. Although, even during the most extensive bleaching events, some reefs escape exposure to severe stress, constituting potential refugia. Here, we identify present‐day climate refugia on the Great Barrier Reef (GBR) and project their persistence into the future. To do this, we apply semi‐dynamic downscaling to an ensemble of climate projections released for the IPCC's recent sixth Assessment Report. We find that GBR locations experiencing the least thermal stress over the past 20 years have done so because of their oceanographic circumstance, which implies that longer‐term persistence of climate refugia is feasible. Specifically, tidal and wind mixing of warm water away from the sea surface appears to provide relief from warming. However, on average this relative advantage only persists until global warming exceeds ~3°C.

Volume, evolution, and sedimentation of future glacier lakes in Switzerland over the 21st century

Abstract. Ongoing climate change and associated glacier retreat is causing rapid environmental change, including shifts in high-alpine landscapes. Glacier lakes, which can form in topographical depressions left behind by glacier retreat, are prominent features within such landscapes. Whilst model-based estimates for the number and area of future glacier lakes exist for various mountain regions across the world, the exact morphology and temporal evolution remain largely unassessed. Here, we leverage a recently released, measurement-based estimate for the subglacial topography of all glaciers in the Swiss Alps to provide an estimate about the number, size, time of emergence, and sediment infill of future glacier lakes. The topographical information is based on 2450 km of measured ice thickness profiles, whilst the temporal evolution of glaciers is obtained from a glacier evolution model forced with an ensemble of climate projections. We estimate that up to 683 potential lakes with an area >5000 m2 and a depth >5 m could emerge across the Swiss Alps if glaciers were to disappear completely, with the potential to hold a total water volume of up to 1.16 [1.05, 1.32] km3 (numbers and 95 % confidence interval). For a middle-of-the-road climate scenario, we estimate that about 10 % (0.12 [0.04, 0.18] km3) and 48 % (0.56 [0.26, 0.67] km3) of this volume could be realized by 2050 and 2100, respectively. In a first-order assessment, we also estimate that ca. 45 % of the newly emerging glacier lakes (260 out of 570) will be transient features, i.e. will disappear again before the end of the century owing to refilling with sediments released by glacial erosion and proglacial sediment transport.

The effect of climate change on wind-wave directional spectra

Through assessment of wind-wave directional spectra, we provide a comprehensive explanation of the projected 21st Century changes in the global wind-wave climatology under a high green-house emissions scenario. Using a seven-member wave climate projection ensemble, we estimate wave climate changes by comparing present and projected climatologies. Clustering techniques are applied to define regional patterns of change with homogenous behavior. The underlying mechanisms behind the changes are also explored by exploiting the relationship between the wave generation area and the effective energy flux propagating toward a target location. A robust transition from positive to negative trends in Southern Ocean westerly swells is observed around 45°S. The increasing signal found in the southernmost swells propagates north beyond 30°N, contributing significantly to the projected changes in tropical regions such as the tropical southern Atlantic and tropical southeastern Pacific. Results highlight the great complexity of the Pacific Ocean due to the convergence of multiple wave systems with different geneses. In the northern basins, the combined effect of the ice melting and a poleward shift of the storm track drives an increase of the northernmost westerly swells. This is countered by a decreasing trend projected in the main wave systems propagating in the North Atlantic Ocean. A poleward shift of trade-induced waves due to the Hadley cell expansion can also be observed globally, causing a clear dipole change pattern in the tropical southern Atlantic and tropical Indian oceans. • The effect of climate change on wave climate is assessed based on directional spectra. • The climate change signals of the main swell systems are described. • Sixteen ocean regions with a similar pattern of change are found.

Ensemble Climate and Streamflow Projections for the Assiniboine River Basin, Canada

The Assiniboine River Basin (ARB) is subject to an exceptionally variable precipitation regime of the Canadian Prairies, ranging between record droughts and unprecedented flooding in just the past decade. To assess the impact of a changing climate on hydroclimate variability in the ARB, we used the bias–corrected simulations from the Canadian Regional Climate Model (CanRCM4) to drive MESH model for two 60–year periods, a historical baseline (1951–2010) and future projection (2041–2100), under the Representative Concentration Pathway (RCP) 8.5 to simulate ARB flows at eight hydrometric stations. The precipitation is projected to increase in every season (~10–38%) except for summer (~−1–−5%). Minimum winter and maximum summer temperatures have the largest seasonal trends, increasing by 2–3 °C in the near future (2021–2050) and 5–6 °C in the far future (2051–2080). These climate changes produce higher winter river flows while peak runoff shifts by several weeks to earlier in the year. There is a shift in the magnitude and timing of extreme water levels. The ensemble of climate projections from a single model and one RCP to the variability and uncertainty in the future hydrology supports adaptation planning in the industrial sectors of Saskatchewan’s economy.

Climate-associated major food crops production change under multi-scenario in China

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011–2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36–91.18% and 62.96–64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12–68.93% and 40.44–41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09–53.94% and 35.86–37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Effectiveness of using representative subsets of global climate models in future crop yield projections

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040–2069 and 2070–2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9–4.7% for canola and 1.5–2.2% for spring wheat, and covers 61.8–91.1% and 66.1–80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Increasing importance of heat stress for cattle farming under future global climate scenarios

In the last decades, livestock species have been severely affected by heat stress because of increasing temperatures, which has threatened animal welfare and decreased production. Based on thermal comfort indices and ensemble climate projections, we analyzed the current and future global spatiotemporal patterns of the heat exposure of cattle in 10 agroclimatic zones. The results show that ~7% of the global cattle population is currently exposed to dangerous heat conditions. This percentage is projected to increase to ~48% before 2100 under a scenario of growing emissions. Tropical agroclimatic zones are expected to face an early increase in the exposure to intense heat before 2050. Heat exposure was negatively correlated with the socioeconomic variables, showing that poor and livestock-dependent tropical countries are the most affected. Our results demonstrate the near-future consequences of heat stress on livestock, emphasizing the limited time available to implement effective abatement strategies.