Ensemble Of Climate Models Research Articles

State-of-the-art climate models reduce dominant dynamical uncertainty in projections of extreme precipitation

Extreme precipitation can lead to severe environmental and economic impacts. Thus, future changes in extreme precipitation and their uncertainties are of major interest. Changes in extreme precipitation can be decomposed into thermodynamic (temperature-related) and dynamic (vertical velocity related) contributions with a scaling approach for extreme precipitation. Applying this approach to the global climate model ensembles CMIP5 and CMIP6, we decompose projection uncertainties of extremes in daily precipitation into uncertainties of thermodynamic and dynamic changes. We analyze regional patterns of the total uncertainties in extreme precipitation projections, as well as the thermodynamic and dynamic contributions to these uncertainties. Total uncertainties relative to the projected multi model mean are dominated by the dynamical contributions, and are large over the tropics and subtropics, but smaller over the high and mid-latitudes. Uncertainties in the thermodynamic contribution are generally small. From CMIP5 to CMIP6, uncertainties in thermodynamic and dynamic changes are slightly reduced in the high and mid-latitudes, while there is a substantial reduction of the uncertainties of the dynamic changes in the tropics and subtropics.

Regional ensemble of CMIP6 global climate models for Sakha (Yakutia) Republic, Northern Eurasia

Future climate projections based on multi-model ensemble approach are seen as more reliable, but not all models are equally performant at reproducing climate features at a regional scale. An optimal regional GCM ensemble was developed for Sakha (Yakutia) Republic based on error statistics and spatial correlation metrics. Historical Coupled Model Intercomparison Project, version 6 (CMIP6) simulations from 48 global climate models (GCMs) were used to evaluate model quality compared to mean annual air temperature (MAAT) reanalysis data for 1961–1990, 1971–2000 and 1981–2010 reference periods, and the MAAT change between 1961-1990 and 1981–2010, ΔT81-61. The best-performing reanalysis, GHCN-CAMS, was validated using observational data. This five-member ensemble includes CESM2-WACCM, CMCC-ESM2, CNRM-CM6-1-HR, INM-CM5-0, MPI-ESM1-2-HR models, weighted by Pearson's coefficient of spatial correlation between observed and modeled ΔT81-61 fields. Model weighting based on spatial correlation metrics improved the performance of the developed multi-model regional ensemble, which can be used in projecting future climate under different climate change scenarios.

By-degree Health and Economic Impacts of Lyme Disease, Eastern and Midwestern United States

Lyme disease (LD) is the most common vector-borne disease in the United States (U.S.). This paper assesses how climate change may influence LD incidence in the eastern and upper Midwestern U.S. and the associated economic burden. We estimated future Ixodes scapularis habitat suitability and LD incidence with a by-degree approach using variables from an ensemble of multiple climate models. We then applied estimates for present-day and projected habitat suitability for I. scapularis, present-day presence of Borrelia burgdorferi, and projected climatological variables to model reported LD incidence at the county level among adults, children, and the total population. Finally, we applied an estimate of healthcare expenses to project economic impacts. We show an overall increase in LD cases with regional variation. We estimate an increase in incidence in New England and the upper Midwestern U.S. and a concurrent decrease in incidence in Virginia and North Carolina. At 3°C of national warming from the 1986–2015 baseline climate, we project approximately 55,000 LD cases, a 38-percent increase from present-day estimates. At 6°C of warming, our most extreme scenario, we project approximately 92,000 LD cases in the region, an increase of 145 percent relative to current levels. Annual LD-related healthcare expenses at 3°C of warming are estimated to be $236 million (2021 dollars), approximately 38 percent greater than present-day. These results may inform decision-makers tasked with addressing climate risks, the public, and healthcare professionals preparing for treatment and prevention of LD.

Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.

Band-by-band spectral radiative kernels based on the ERA5 reanalysis

Radiative kernel is a widely adopted method for diagnosing radiation variability and climate feedback. However, most of the existing radiative kernels are broadband flux kernels and lack the spectral information. Motivated by the growing interest in the spectral changes of the Earth radiation budget, we generate a new set of band-by-band radiative kernels based on the fifth generation European Center for Medium-Range Weather Forecasts (ERA5) reanalysis, which can be used for analyzing the spectrally decomposed changes in the top of atmosphere, surface and atmospheric radiation. The radiative sensitivity quantified by the ERA5 band-by-band kernel is compared to another spectral kernel and validated in a spectral radiation closure test. The use and benefits of the new ERA5 kernels are then demonstrated in an analysis of spectral feedbacks of an ensemble of global climate models (GCMs).

Climate change signals of extreme precipitation return levels for Germany in a transient convection‐permitting simulation ensemble

AbstractThe increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection‐permitting (CP), multi‐global climate model ensemble of COSMO‐CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation‐based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation‐based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time‐slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi‐day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties.

Assessing the impact of climate change on a coastal karst aquifer in a semi-arid area

This study investigates the potential future state of a coastal karst aquifer located in the Salento peninsula of southern Italy, examining the impact of climate change. After a historical and future overview of the climate in the study area, an analysis of meteorological indices (SPIs and SPEIs) and groundwater levels (GWLs) during the historical period revealed a strong correlation between them. Then, straightforward linear regression models were applied to establish relationships between SPIs/SPEIs and GWLs for the analysed wells. To estimate future SPIs and SPEIs, an ensemble of regional climate models was used, and estimates of GWLs until the end of the century were obtained by incorporating these projections into the regression models. Two emission pathways, the RCP4.5 and the RCP8.5, were considered in the analysis. The results based on SPIs suggest no significant changes in future GWLs across all wells. However, when considering also temperature through the use of SPEIs, a general decline in groundwater levels is expected, with the decrease being more pronounced under the RCP8.5.

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

Atmospheric teleconnections between the Arctic and the Baltic Sea region as simulated by CESM1-LE

Abstract. This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing. The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas. The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, R2, was highest for winter: for T2 m, R2=0.64; for sea level pressure (SLP), R2=0.44; and for precipitation (PREC), R2=0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest R2=0.09 being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.

Effect of climate change on soil erosion rate in a tropical Indian catchment

Abstract This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

Northern Hemisphere Extratropical Cyclone Clustering in ERA5 Reanalysis and the CESM2 Large Ensemble

Abstract Extratropical cyclones are a dominant feature of the midlatitudes, and often occur as storm sequences. This phenomenon is known as cyclone clustering, which is common over regions like the eastern North Atlantic and western Europe. Here, intense clustered cyclones may lead to large cumulative socioeconomic impacts. There are several different approaches to quantify cyclone clustering, but a detailed evaluation on how clustering may change in a warmer climate is missing. We perform a cyclone clustering analysis for the Northern Hemisphere midlatitudes using the ERA5 reanalysis to characterize clustering during 1980–2020. Moreover, we use large ensemble simulations of the Community Earth System Model version 2 following the SSP3-7.0 scenario to compare clustering during 2060–2100 to 1980–2020. Our model simulations show significant enhancement in cyclone clustering over Europe for 3 and 4 cyclones within 7 days in the future decades, which is increasing by up to 25% on average during 2060–2100 compared to 1980–2020. In contrast, cyclone clustering decreases along the west coast of the United States and Canada by up to 24.3% and by 10.1% in the Gulf of Alaska for the same periods. In a warmer climate, clustered cyclones have lower minimum pressure and larger radii and depths compared to nonclustered events. Our findings suggest that change in future cyclone clustering depends on regions affected by global warming, with implications for the cumulative windstorm risk. Significance Statement Storm sequences like the one of December 1999 (Anatol, Lothar, and Martin) have led to large socioeconomic impacts in Europe. It is still unclear how such events will change under global warming. We analyze storm sequences in a reanalysis and a large climate model ensemble for recent (1980–2020) and future climate conditions (2060–2100). Our results show a significant enhancement of storm sequences over Europe for 3 and 4 storms within 7 days, while a decrease is found along the west coast of the United States, western Canada, and in the Gulf of Alaska in future decades. Our findings suggest that the characteristics of cyclone clustering may change in a warmer world, and thus also the associated impacts.

Characterizing Spatial Structure in Climate Model Ensembles

Abstract This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of “intermodel empirical orthogonal function” analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom.

Higher quantiles of sea levels rise faster in Baltic Sea Climate projections

Absolute sea levels in the Baltic Sea will rise under the influence of climate warming, similar to those in the world ocean. For extreme sea levels, there are indications that they will rise even faster than mean sea levels, but that topic is still controversially discussed and existing studies point into different directions. We analyzed a regional climate model ensemble for the Baltic Sea for future sea level changes. We find that the rate of change differs between high sea levels and the average: In the eastern part of the Baltic Sea, the 99th percentile of sea level was predicted to rise faster than the median. In the south-western part, the relation was opposite. Thus, our simulations predict a change not only in the sea level mean, but also in its distribution. This pattern was almost consistent between the individual ensemble members. We investigated the 99th percentile as a proxy for extreme sea levels, since their partially stochastic nature limits the predictive skill of our 20-member ensemble. Our findings imply that adapting coastal protection to mean sea level change only may be regionally insufficient.

Estimating the Incidence and Economic Cost of Lyme Disease Cases in Canada in the 21st Century with Projected Climate Change.

Lyme disease (LD) is emerging in Canada owing to the range expansion of the tick vector Ixodes scapularis (I. scapularis). Our objective was to estimate future LD incidence in Canada, and economic costs, for the 21st century with projected climate change. Future regions of climatic suitability for I. scapularis were projected from temperature output of the North American Coordinated Regional Climate Downscaling Experiment regional climate model ensemble using greenhouse gas Representative Concentration Pathways (RCPs) 4.5 and 8.5. Once regions became climatically suitable for ticks, an algorithm derived from tick and LD case surveillance data projected subsequent increasing LD incidence. Three scenarios (optimistic, intermediate, and pessimistic) for maximum incidence at endemicity were selected based on LD surveillance, and underreporting estimates, from the United States. Health care and productivity cost estimates of LD cases were obtained from the literature. Projected annual LD cases for Canada ranged from 120,000 to by 2050. Variation in incidence was mostly due to the maximum incidence at endemicity selected, with minor contributions from variations among climate models and RCPs. Projected annual costs were substantial, ranging from to a year by 2050. There was little difference in projected incidence and economic cost between RCPs, and from 2050 to 2100, because projected climate up to 2050 is similar for RCP4.5 and RCP8.5 (mitigation of greenhouse gas emissions captured in RCP4.5 does not impact climate before the 2050s) and by 2050 the most densely populated areas of the study region are projected to be climatically suitable for ticks. Future incidence and economic costs of LD in Canada are likely to be substantial, but uncertainties remain. Because densely populated areas of Canada are projected to become endemic under conservative climate change scenarios, mitigation of greenhouse gas emissions is unlikely to provide substantial health co-benefits for LD. https://doi.org/10.1289/EHP13759.

Applying ensemble climate models to predict the fate of marginal coral reefs already existing at thermal and turbidity limits in arid tropical Australia

AbstractMarine fauna, including coral reefs, exist under particular oceanographic and meteorological (metocean) processes that maintain water quality within the range limits to which they have adapted over millennia. Climate‐induced changes to these metocean processes could alter ambient marine water quality to ranges beyond those limits and at rates faster than species can adapt. Extreme (or marginal) coral reefs, such as those in arid tropical regions, already exist at the limits of their ranges for water quality parameters such as temperature and turbidity. Here, we apply projected anomalies from ensemble climate models to the metocean processes that drive turbidity in the Exmouth Gulf region of north Western Australia where habitats of significant environmental value exist. We also apply projected sea surface temperature anomalies to look at how a combined effect of turbidity and temperature might impact important habitats. We find that turbidity is predicted to increase in some parts of the Gulf and decrease in others due to differing metocean drivers of turbidity throughout the region. Temperature anomalies reveal year‐round increases in temperature consistent with current summer marine heat wave events (>2.5°C above mean temperatures). Climate models used in the predictions varied between themselves underscoring the importance of model choice and of using ensembles.

Wind energy potential of weather systems affecting South Africa’s Eastern Cape Province

AbstractAs a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production.

Evaluating the Present and Future Heat Stress Conditions in the Grand Duchy of Luxembourg

The impact of elevated air temperature and heat stress on human health is a global concern. It not only affects our well-being directly, but also reduces our physical work capacity, leading to negative effects on society and economic productivity. Climate change has already affected the climate in Luxembourg and, based on the results of regional climate models, extreme heat events will become more frequent and intense in the future. To assess historical conditions, the micro-scaleRayManPro 3.1 model was used to simulate the thermal stress levels for different genders and age classes based on hourly input data spanning the last two decades. For the assessment of future conditions, with a special emphasis on heat waves, a multi-model ensemble of regional climate models for different emission scenarios taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX) was used. For both, the past and future conditions in Luxemburg, an increase in the heat stress levels was observed. Small differences for different age groups and genders became obvious. In addition to the increase in the absolute number of heat waves, an intensification of higher temperatures and longer durations were also detected. Although some indications of the adaptation to rising air temperatures can be observed for high-income countries, our results underscore the likelihood of escalating heat-related adverse effects on human health and economic productivity unless more investments are made in research and risk management strategies.

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Combined impacts of climate and land-use change on future water resources in Africa

Abstract. Africa depends on its water resources for hydroelectricity, inland fisheries and water supply for domestic, industrial and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover have also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the land-use and land cover change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET) and river flow respond to both CC and LULCC scenarios across the entire African continent. We set up a Soil and Water Assessment Tool (SWAT+) model for Africa and calibrated it using the hydrological mass balance calibration (HMBC) methodology detailed in Chawanda et al. (2020a). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC with an up to 7 % decrease, while the Limpopo River will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18 % increase) and without LULCC (over 20 % decrease). The projected changes have implications for the agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa.

The evolution of a non-autonomous chaotic system under non-periodic forcing: A climate change example.

In this article, we approach the problem of measuring and interpreting the mid-term climate of a non-autonomous chaotic dynamical system in the context of climate modeling. To do so, we use a low-dimensional, conceptual model for the Earth system with different timescales of variability and subjected to non-periodic external forcing. We introduce the concepts of an evolution set and its distribution, which are dependent on the starting state of the system, and explore their links to different types of initial condition uncertainty and the rate of external forcing. We define the convergence time as the time that it takes for the evolution distribution of one of the dependent variables to lose memory of its initial condition. We suspect a connection between convergence times and the classical concept of mixing times, but the precise nature of this connection needs to be explored. These results have implications for the design of influential climate and Earth system model ensembles and raise a number of issues of mathematical interest.