Ensemble Climate Research Articles

Assessing the impacts of climate change and dam development on potential flood hazard and damages in the Cambodian floodplain of the lower mekong basin

Study RegionMekong River Basin Study focusClimate change has become a global environmental and socio-economical issue potentially affecting river hydrology and downstream flood characteristics. Climate change and upstream dam construction are the two main driving factors in altering the hydrology in the Mekong River Basin. Cambodian floodplain is located in the vulnerable flood zone to be affected by climate change and upstream dam development. This study investigated the potential impacts of future climate change and dam construction on flood hazards and agricultural damages in the Cambodian floodplain of the Mekong River. Climate outputs from MRI-AGCM3.2S under different sea surface temperature (SST) scenarios and a large ensemble climate dataset (d4PDF) under 4 K increasing scenario were used as input to a fully distributed rainfall-runoff-inundation (RRI) model for future projection of flood in the Lower Mekong Basin. New hydrological insights for the regionThe results indicated that the changes in extreme flood events (25-year flood) showed an increase of agricultural flood damages under climate change impact by 18–28% under different SST scenarios. These increases would be only 5–13% under integrated impacts from future climate change and dam construction. Under the 4 K increasing scenario, the agricultural damages for extreme flood events of 10-year, 50-year, and 100-year return periods would increase by 32%, 38%, and 39% for climate change impact alone and 17%, 29%, and 31% for combined climate change and dam development scenario, respectively. The climate change impacts would have more influential on increasing the magnitude of extreme floods than the reduction caused by dam reservoir constructions in the future projection in the Cambodian floodplain of the Mekong River Basin. The impact of dam constructions tends to have greater impact on reducing smaller extreme events; however, their impacts are relatively smaller for larger extreme flood events (i.e., 100-year flood).

Exploring adaptation responses of maize to climate change scenarios in southern central Rift Valley of Ethiopia

In this study, we assessed responses of adaptation options to possible climate change scenarios on maize growth and yield by using projections of 20 coupled ensemble climate models under two representative concentration pathways (RCPs) 4.5 and 8.5 by means of a DSSAT model. Growth and yield simulations were made across present and future climate conditions using the hybrid maize variety (Shone). Subsequently, simulated yields were compared with farmer’ average and on-farm trial yields. Results showed that on-farm trial yield (5.1–7.3 t ha−1) lay in between farmers’ average yield (2.9–5 t ha−1) and water-limited potential yield (6.3–10.6 t ha−1). Maize yields achieved in farmers’ fields are projected to decline towards mid-century and further towards the end of the century regardless of the adaptation options compared with baseline in low potential clusters. Results of a combination of adaptation options including February planting, use of 64 kg ha−1 N and conservation tillage provided yield advantage of 5.8% over the 30 cm till under medium GHGs emission scenario during mid-century period at Shamana. Mulching with 5 t ha−1 was projected to produce a 4–5% yield advantage in the Hawassa cluster during the mid-century period regardless of changes in tillage or planting window. Under a high GHGs emission scenario, over 13.4% yield advantage was projected in the Bilate cluster due to conservation tillage and June planting during the mid-century period. In the Dilla cluster, the use of 10 t ha−1 mulch, conservation tillage and early planting (February) would result in a 1.8% yield advantage compared with the control either in medium or high GHGs emission scenarios. Thus, the most promising and least risky practices among simulated strategies were the use of nitrogen and mulching in combination with tillage or planting date adjustment. However, adaptation options remained least promising and highly risky if not integrated with mulching or nitrogen use. Hence, the negative impacts of future climate change and subsequent yield gaps would be reduced by optimizing the application of nitrogen, mulch and their interaction with planting date and tillage in high and low potential areas of maize production.

Regional wave climate projections forced by EURO‐CORDEX winds for the Black Sea and Sea of Azov towards the end of the 21st century

AbstractWave phenomena impact high commercial value coastal and offshore activities, infrastructures and transportation. The knowledge of future wave conditions allows for consistent long‐term planning and decision‐making. The present study aims to provide robust, reliable projections of the potential future wave conditions of the Black Sea under the influence of climate change. For this purpose, an eight‐member dynamic wave climate ensemble that accurately represents the Black Sea's present wave climate has been produced, and future projections are assessed and analysed here. The wave climate ensemble was obtained by forcing the Simulating Waves Nearshore spectral wave model with eight regional wind fields from the EURO‐CORDEX. The future simulations of the regional wind fields are based on the RCP8.5 high‐emission scenario. The historical wave climate of the ensemble was evaluated against ERA5 reanalysis data. Projected changes in the mean wind and wave characteristics are examined by comparing historical and future simulations. In addition, the projected trends in the annual means during the future period of the wave simulations for significant wave height (Hs) and wave energy flux are also analysed. The projections for Hs and wind speeds are pronounced with significant decreases down to −10% across the basin, especially in the eastern region of the Black Sea. Changes in the Hs 99% percentiles of up to 16% are projected to occur. The projected changes in the annual mean of the wave energy flux are close to the projected changes in Hs means, while the seasonal changes (between −15% and 12%) are expected to be higher.

Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change

Abstract. Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 ∘C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2.9 ∘C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.

Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

AbstractExtreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.

Yield predictions of timothy (Phleum pratense L.) in Norway under future climate scenarios

The perennial forage grass timothy (Phleum pratense L.) is the most important forage crop in Norway. Future changesin the climate will affect growing conditions and hence the yield output. We used data from the Norwegian Value for Cultivation and Use testing to find a statistical prediction model for total dry matter yield (DMY) based on agro-climatic variables. The statistical model selection found that the predictors with the highest predictive power were growing degree days (GDD) in July and the number of days with rain (>1mm) in June–July. These predictors together explained 43% of the variability in total DMY. Further, the prediction model was combined with a range of climate ensembles (RCP4.5) to project DMY of timothy for the decades 2050–2059 and 2090–2099 at 8 locations in Norway. Our projections forecast that DMY of today’s timothy varieties may decrease substantially in South-Eastern Norway, but increase in Northern Norway, by the middle of the century, due to increased temperatures and changing precipitation patterns.

The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18

Abstract. This paper details the development and evaluation of the enhanced future FLows and Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The projections are derived from a range of hydrological models. For river flows, multiple models (Grid-to-Grid, PDM (Probability Distributed Model) and GR (Génie Rural; both four- and six-parameter versions, GR4J and GR6J)) are used to provide an indication of hydrological model uncertainty. For groundwater, two models are used, a groundwater level model (AquiMod) and a groundwater recharge model (ZOODRM: zooming object-oriented distributed-recharge model). A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge was produced using bias-corrected data from the UKCP18 regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the regional climate model (RCM) ensemble in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector and so has been developed with drought, low river flow and low groundwater level applications as the primary objectives. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose. Only a single climate model and one emissions scenario are used, so any applications should ideally contextualise the outcomes with other climate model–scenario combinations. The dataset can be accessed in Hannaford et al. (2022): https://doi.org/10.5285/1bb90673-ad37-4679-90b9-0126109639a9.

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.

Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso

We propose a new modeling framework for highly multivariate spatial processes that synthesizes ideas from recent multiscale and spectral approaches with graphical models. The basis graphical lasso writes a univariate Gaussian process as a linear combination of basis functions weighted with entries of a Gaussian graphical vector whose graph is estimated from optimizing an l 1 penalized likelihood. This article extends the setting to a multivariate Gaussian process where the basis functions are weighted with Gaussian graphical vectors. We motivate a model where the basis functions represent different levels of resolution and the graphical vectors for each level are assumed to be independent. Using an orthogonal basis grants linear complexity and memory usage in the number of spatial locations, the number of basis functions, and the number of realizations. An additional fusion penalty encourages a parsimonious conditional independence structure in the multilevel graphical model. We illustrate our method on a large climate ensemble from the National Center for Atmospheric Research’s Community Atmosphere Model that involves 40 spatial processes. Supplementary materials for this article are available online.

A case study of the July 2021 Henan extreme rainfall event: From weather forecast to climate risks

During 17–22 July 2021 a rainfall event in Henan Province of China has delivered over 820 mm precipitation around Zhengzhou with hourly intensity exceeded 201 mm. This paper presents a systematic case study of its driving processes, predictability, and future climate risks. The key aspects of this event are 1) geographically stationary and 2) temporally persistent. A sustained low-level moist easterly jetstream resulting from between typhoon In-Fa and a northward displaced subtropical high and orography in the region appear to have played a major role in the event. The event was, overall, well predicted in global operational 5-day weather forecast, though the details may not be very accurate. From a climate perspective, the large-scale low-level circulation pattern was like the August 1975 catastrophic floods in the same region, but opposite to the 2020 summer anomalous extreme Meiyu situation. Both weather patterns were rare during the past 47 years, together accounting for less than 10% of low-level daily weather patterns. The orographic features around Henan make it vulnerable for floods. A risk assessment based two sets of ensemble climate model simulations suggest the probability of such events occurring in the future will increase under the IPCC RCP8.5 scenario.

Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach

Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach

Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Response to: Saving Storage in Climate Ensembles

Response to: Saving Storage in Climate Ensembles

Rejoinder on ‘Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach’

Rejoinder on ‘Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach’

Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton

Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton

Demographic resilience may sustain significant coral populations in a 2°C-warmer world.

Projections of coral reefs under climate change have important policy implications, but most analyses have focused on the intensification of climate-related physical stress rather than explicitly modelling how coral populations respond to stressors. Here, we analyse the future of the Great Barrier Reef (GBR) under multiple, spatially realistic drivers which allows less impacted sites to facilitate recovery. Under a Representative Concentration Pathway (RCP) 2.6 CMIP5 climate ensemble, where warming is capped at ~2°C, GBR mean coral cover declined mid-century but approached present-day levels towards 2100. This is considerably more optimistic than most analyses. However, under RCP4.5, mean coral cover declined by >80% by late-century, and reached near zero under RCP ≥6.0. While these models do not allow for adaptation, they significantly extend past studies by revealing demographic resilience of coral populations to low levels of additional warming, though more pessimistic outcomes might be expected under CMIP6. Substantive coral populations under RCP2.6 would facilitate long-term genetic adaptation, adding value to ambitious greenhouse emissions mitigation.

TRANSLATE: standardized climate projections for Ireland

The TRANSLATE project was established in 2021 by Met Éireann, the Irish national meteorological service, to provide standardized future climate projections for Ireland. This paper outlines the principles and main methods that were used to generate the first set of such projections and presents selected results to the end of the 21st century. Two separate ensembles of dynamically downscaled CMIP5 projections were analyzed. These produce very consistent results, increasing confidence in both, and in the methods used. Future projected fields show plenty of detail (depending on local geography), but the change maps relative to the base period are much smoother, reflecting the global climate change signal. Future forcing uncertainty is represented by 3 different emission scenarios, while model response uncertainty is represented by sub-ensembles corresponding to different climate sensitivities. The resulting matrix of distinct climate ensembles is complemented by ensembles of temperature threshold-based projections, drawn from the same underlying simulations.

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.

Modelling future flood events under climate change scenarios in the Pungwe River Basin

Abstract This study sought to project future changes in hydroclimatic variables and to establish how climate change affects flood inundation extent in the Pungwe River Basin. Climate ensembles of 10 Regional Climate Models from the CORDEX project were selected. The historical rainfall and temperature time series and the downscaled climate data were input into the HBV model to generate streamflow for the 2022–2099 period. Flood extents for 50-, 100-and 1,000-year return periods were predicted using the HEC-RAS hydraulic model. By 2070, annual rainfall at all nine studied meteorological stations is predicted to reduce by a maximum of 61%. Temperature is expected to increase up to 1.5% over the same period. By the 2070s, simulations from HBV revealed that the peak flows for the Pungwe River Basin will increase by up to 100% and decrease by approximately 57% as projected by the model ensemble. The analyses also show that by 2070 climate change may cause a minimum of 2,784.4 km2 and a maximum of 8,235.6 km2 of flood extension. These results are essential for decision-making on flood hazard mapping and early warning systems, prompting a pathway for sustainable development.