Ensemble Of Global Climate Models Research Articles

European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)

Abstract. In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale.

Combating food insecurity in a rapidly changing mountain climate environment: insights from Lesotho

This paper assesses the options that developing countries have in ensuring food security in an environment where key climate parameters are changing rapidly. Based on a case study of Lesotho, the paper utilizes the Global Climate Model ensemble to determine future precipitation and temperature projections using data from Climate Explorer. The results indicate that in Lesotho, maximum temperature is likely to continue to increase. Coupled with a significant increase in precipitation under both Representative Concentration Pathway (RCP) 4.5 (p = 0.0008) and RCP 8.5 (p = 0.0001) scenarios and a significant increase of evaporation under the two scenarios for RCP 4.5 (p = 0.0008) and RCP 8.5 (p = 0.0103), the country’s preparedness for hazards arising from climate change is rendered uncertain. Despite this reality, we suggest that uncertainty could be reduced by reinforcing existing innovative measures that could improve the productive capacity of subsistence farmers, so that they meet their own food requirements, while preventing further environmental deterioration. While some measures will be based on the intensification of government-led social support mechanisms, others will depend on the support rendered to “tried and tested” traditional practices such as machobane and fato-fato, which have a long tradition in the country. However, on their own, these measures are insufficient to cope with rapidly changing climatic conditions, unless they are coupled with national research development initiatives, improved early warning systems, and enhancement of environmental monitoring capabilities, the implementation of which requires careful land use planning.

Assessing Property Level Economic Impacts of Climate in the US, New Insights and Evidence from a Comprehensive Flood Risk Assessment Tool

Hurricanes and flood-related events cause more direct economic damage than any other type of natural disaster. In the United States, that damage totals more than USD 1 trillion in damages since 1980. On average, direct flood losses have risen from USD 4 billion annually in the 1980s to roughly USD 17 billion annually from 2010 to 2018. Despite flooding’s tremendous economic impact on US properties and communities, current estimates of expected damages are lacking due to the fact that flood risk in many parts of the US is unidentified, underestimated, or available models associated with high quality assessment tools are proprietary. This study introduces an economic-focused Environmental Impact Assessment (EIA) approach that builds upon an our existing understanding of prior assessment methods by taking advantage of a newly available, climate adjusted, parcel-level flood risk assessment model (First Street Foundation, 2020a and 2020b) in order to quantify property level economic impacts today, and into the climate adjusted future, using the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathways (RCPs) and NASA’s Global Climate Model ensemble (CMIP5). This approach represents a first of its kind—a publicly available high precision flood risk assessment tool at the property level developed completely with open data sources and open methods. The economic impact assessment presented here has been carried out using residential buildings in New Jersey as a testbed; however, the environmental assessment tool on which it is based is a national scale property level flood assessment model at a 3 m resolution. As evidence of the reliability of the EIA tool, the 2020 estimated economic impact (USD 5481 annual expectation) was compared to actual average per claim-year NFIP payouts from flooding and found an average of USD 5540 over the life of the program (difference of less than USD 100). Additionally, the tool finds a 41.4% increase in average economic flood damage through the year 2050 when environmental change is included in the model.

Analysis of CMIP6 atmospheric moisture fluxes and the implications for projections of future change in mean and heavy rainfall

AbstractMotivated by the wide range of projections of regional rainfall (or precipitation, pr) based on the CMIP5 ensemble of global climate models, we investigate the atmospheric moisture budget of idealized rising‐CO2 (1pctCO2) simulations from 10 models submitted to CMIP6. We use the new CMIP6 standard variables, the vertically integrated eastward and northward moisture flux, and provide a multi‐model assessment of these, their convergence (conv), along with water vapour path (prw). Seasonal climatologies of a 20‐year base climate for pr, prw, flux, and conv match well those from the ECMWF's Reanalysis 5. In addition, composites ‘H’ of quantities for months in the top decile of monthly rainfall, calculated at each model grid point, produce a field of heavy rainfall, on average double that of the mean. Spatial correlation coefficients (r) between the pr and conv fields are typically .7 in each season. This rises to .9 for the corresponding H fields, due to the important link between moisture convergence and heavy rainfall. Fields of standardized change (Δ), or change per degree of global warming, were calculated. The ensemble‐mean Δpr is only weakly correlated with the base climate pr, and likewise for conv. However, the prw change is highly correlated with the base prw, pointing to the importance of the “thermodynamic” change in that variable. Globally, the net Δconv is highly correlated with Δpr, around .9. This rises to .95 for the heavy‐rain composites. Relationships between changes in pr and other components hold at grid points also, when the individual model results are correlated. Clearly, there is a dependence in the change in pr with that in conv, particularly for heavy rainfall. Assessment of a larger ensemble from CMIP6, using the new flux variables, would improve the evaluation of these processes, and potentially allow more confident projections in future rainfall changes.

Combining Emergent Constraints for Climate Sensitivity

AbstractA method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

Assessing the Vulnerability of Military Installations in the Coterminous United States to Potential Biome Shifts Resulting from Rapid Climate Change.

Climate-change impacts to Department of Defense (DoD) installations will challenge military mission and natural resource stewardship efforts by increasing vulnerability to flooding, drought, altered fire regimes, and invasive species. We developed biome classifications based on current climate for the coterminous United States using the Holdridge Life Zone system to assess potential change on DoD lands. We validated classifications using comparisons to existing ecoregional classifications, the distribution of major forest types, and tree species in eastern North America. We projected future life zones for mid- and late-century time periods under three greenhouse gas emission scenarios (low-B1, moderate-A1B, and high-A2) using an ensemble of global climate models. To assess installation vulnerability (n = 529), we analyzed biome shifts using spatial cluster analysis to characterize interregional variation, and identified representative installations for subsequent landscape-level analyses. Although mean annual temperatures are expected to increase, installations located in the Northeast, Lake States, and western Great Plains are likely to experience the largest proportional increases in temperature. Accordingly, forest and grassland communities at these installations managed to support a wide range of training, and environmental objectives may be adversely affected by altered disturbance regimes, heat, and moisture stress. However, precipitation is projected to increase in the Northeast and Lake States mitigating some effects of increased temperatures on biological communities. Given the uncertain response to climate change in different ecoregions, additional environmental and stewardship attributes are needed within a decision-support framework to understand vulnerabilities and provide appropriate responses.

Potential evapotranspiration models evaluation, modelling, and projection under climate scenarios, Kesem sub-basin, Awash River basin, Ethiopia

Evapotranspiration is a complex process in the hydrological cycle that influences the quantity of surface and groundwater. This study strives to evaluate potential evapotranspiration (PET) estimating models, modeling, and project potential evapotranspiration under climate scenarios. For models evaluation, the modified Hargreaves???Samani, Thornthwait, and Blaney???Criddle were evaluated against the FAO 56 Penman???Monteith method using the relative error (Re), normalized root-mean-squared error, and Pearson correlation (r). Multiple linear regression technique has been used to develop a PET estimation model using six climate parameters (minimum and maximum temperatures, relative humidity, wind speed, solar radiation, and sun hours). Outputs of 17 global climate models ensemble were used for RCP 4.5 and 8.5 emission scenarios to predict future PET. The correlation of climate parameters with PET reveals solar radiation, and Tmax had strong correlation (r) (0.73 and 0.8) at Shola Gebeya and (0.65 and 0.8) at Aleltu Agriculture, respectively, than other considered parameters. The models evaluation shows the modified Hargreaves???Samani equation performed better than others at both Shola Gebeya and Aleltu Agriculture stations. The result of multiple linear regression model shows the input variables used in the modeling were sufficient, implying that this model can be successfully used in estimating PET. Overall, the estimated PET using the multiple linear regression models under RCP 4.5 and 8.5 emission scenarios shows an increasing trend???this is pillar information for the water users in the study catchment.

Future changes in the wintertime cyclonic activity over the CORDEX-CORE southern hemisphere domains in a multi-model approach

Changes in the characteristics of austral winter (June–July–August) synoptic activity in three domains (Africa, Australia and South America) of the extratropical Southern Hemisphere projected with the Regional Climate Model version 4 (RegCM4) are presented. The model is nested in three global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5) under the Representative Concentration Pathway 8.5. The model grid spacing is 25 km and the simulations cover the period 1970–2100. Synoptic activity is analyzed using both Eulerian and Lagrangian approaches. The Eulerian analysis shows an increase of the synoptic activity south of 40° S in the RegCM4 and GCMs ensembles for the future (2080–2099) compared to the present (1995–2014), but this signal does not necessarily indicate an increase in the cyclone frequency since it includes cyclonic and anticyclonic features. The Lagrangian analysis, however, indicates a decrease in the frequency of cyclones, with a positive tendency towards stronger systems, although the latter is not statistically significant at 95% confidence level. Lifetime, traveled distance and mean speed of the cyclones do not present statistically significant changes in the future climate. On the other hand, a significant increase in both intensity and extension of areas affected by precipitation associated with cyclones is found. As a consequence, there is a statistically significant trend of individual cyclones to produce more rainfall in the future.

Uncovering the shortcomings of a weather typing method

Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

Potential shifts in the aboveground biomass and physiognomy of a seasonally dry tropical forest in a changing climate

Seasonally dry tropical forests (SDTFs) account for one-third of the interannual variability of global net primary productive (NPP). Large-scale shifts in dry tropical forest structure may thus significantly affect global CO2 fluxes in ways that are not fully accounted for in current projections. This study quantifies how changing climate might reshape one of the largest SDTFs in the world, the Caatinga region of northeast Brazil. We combine historical data and future climate projections under different representative concentration pathways (RCPs), together with spatially explicit aboveground biomass estimates to establish relationships between climate and vegetation distribution. We find that physiognomies, aboveground biomass, and climate are closely related in the Caatinga—and that the region’s bioclimatic envelope is shifting rapidly. From 2008–2017, more than 90% of the region has shifted to a dryer climate space compared to the reference period 1950–1979. An ensemble of global climate models (based on IPCC AR5) indicates that by the end of the 21st century the driest Caatinga physiognomies (thorn woodlands to non-vegetated areas) could expand from 55% to 78% (RCP 2.6) or as much as 87% (RCP8.5) of the region. Those changes would correspond to a decrease of 30%–50% of the equilibrium aboveground biomass by the end of the century (RCP 2.6 and RCP8.5, respectively). Our results are consistent with historic vegetation shifts reported for other SDTFs. Projected changes for the Caatinga would have large-scale impacts on the region’s biomass and biodiversity, underscoring the importance of SDTFs for the global carbon budget. Understanding such changes as presented in this study will be useful for regional planning and could help mitigate their negative social impacts.

Assessment of CMIP6 Cloud Fraction and Comparison with Satellite Observations

AbstractThe seasonal and regional variations of cloud fractions are compared across two generations of global climate model ensembles, specifically, the Coupled Model Intercomparison Project‐5 (CMIP5) and CMIP6, through the historical period in terms of skills and multimodel agreement. We find a wider spread of historical cloud fraction changes in the CMIP6 than was simulated by the CMIP5. The global mean cloud fractions of CMIP6 increased by about 4.5% from the CMIP5, which attributed to greater changes in the northern hemisphere than in the southern hemisphere. The CMIP6 cloud fractions in recent years are validated with the CALIPSO_CLOUSAT observations to understand the cloud fraction uncertainties in CMIP6 models. The CMIP6 ensemble mean of cloud fractions compares well with the observations with a mean difference of 0.5% in lower altitudes. The CMIP6 cloud fractions are higher than the observations at higher latitudes in both hemispheres in the upper troposphere, and the biases vary from one model to another. The spatial difference between the ensemble and observations is further revealed over the tropics: where the model displays a 3% higher bias. In addition, we observed a significant trend occuring in the northern hemisphere since the mid‐20th century using calculations of cloud fraction trends based on the robust regression technique. Finally, we reduce the differences between the model and observations by applying a simple regression technique. The results exemplify that the model and modified observations compare well, with the root mean square value decreased by nearly 28%, and the correlation increased significantly.

Contrasting regional and global climate simulations over South Asia

Two ensembles of climate simulations, one global and one regional, are used to investigate model errors and projected climate change in seasonal mean temperature and precipitation over South Asia. The global ensemble includes ten global climate models (GCMs). In the regional ensemble all ten GCMs are downscaled by a regional climate model—RCA4 over South Asia at 50 km resolution. Our focus is on the Indian Summer Monsoon season (June–August) and we show that RCA4 can reproduce, reduce or amplify large-scale GCM biases depending on regions and GCMs. However, the RCA4 bias pattern in precipitation is similar across the simulations, regardless of forcing GCM, indicating a strong RCA4 imprint on the simulated precipitation. For climate change, the results indicate, that RCA4 can change the signal projected by the GCM ensemble and its individual members. There are a few RCA4 simulations with a substantial reduction of projected warming by RCA4 compared to the driving GCMs and with a large regional increase in precipitation absent in the GCMs. We also found that in a number of subregions warm RCA4 biases are related to stronger warming and vice versa, while there is no such dependency in the GCM ensemble. Neither the GCM nor the RCA4 ensemble shows any significant dependency between projected changes and biases for precipitation. Our results implicate that using only RCMs and excluding GCMs, a commonly established approach, can significantly change the message on future regional climate change.

Climate change projected to reduce prescribed burning opportunities in the south-eastern United States

Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria (a ‘burn window’) are met. Here, we evaluate the potential impacts of projected climatic change on prescribed burning in the south-eastern United States by applying a set of burn window criteria that capture temperature, relative humidity and wind speed to projections from an ensemble of Global Climate Models under two greenhouse gas emission scenarios. Regionally, the percentage of suitable days for burning changes little during winter but decreases substantially in summer owing to rising temperatures by the end of the 21st century compared with historical conditions. Management implications of such changes for six representative land management units include seasonal shifts in burning opportunities from summer to cool-season months, but with considerable regional variation. We contend that the practical constraints of rising temperatures on prescribed fire activities represent a significant future challenge and show that even meeting basic burn criteria (as defined today) will become increasingly difficult over time, which speaks to the need for adaptive management strategies to prepare for such changes.

Adverse weather conditions for UK wheat production under climate change

Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Future Changes in Rice Bioclimatic Growing Conditions in Portugal

Rice is a historically important crop in Portugal. This crop development and production strongly depend on atmospheric conditions in the growing season. Given the strong dependence of climatic conditions, climate change may pose a significant risk for future rice production. In the present study, a high spatial resolution bioclimatic characterization over the main rice producing region in Portugal was performed for the recent past (1950–2000) and for the future (2041–2060) under four different anthropogenic forcing scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). This zoning is performed by using eight bioclimatic indices, based on temperature and precipitation, using a very high resolution gridded dataset (Worldclim). For the future period, an 11-member global climate model ensemble was used, also taking into account model/scenario uncertainties and bias. Additionally, a new index was developed to incorporate the main features of temperature and precipitation at each rice field level. Under recent past climates, a clear north–south gradient in temperature and precipitation is apparent, with the regions of Tejo and Sado presenting higher temperatures and lower precipitation than the Mondego and Vouga regions. Additionally, there is a coastal–inland effect due to the Atlantic Ocean influence. Under anthropogenic climate change, all indices point to annual higher temperatures and lower precipitations across all rice producing regions, accompanied by increased seasonality. Furthermore, the rise of summertime temperatures may substantially increase water demands, which, when unmitigated, may bring physiological problems in the crop development. We conclude that climate change may negatively impact the viability of rice production in Portugal, particularly taking into account the national grown varieties. Thus, adequate and timely planning of suitable adaptation measures are needed to ensure the sustainability of this historically important food sector.

Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

AbstractAtmospheric rivers (ARs) are narrow jets of integrated water vapor transport that are important for the global water cycle and also have large impacts on local weather and regional hydrology. Uniformly weighted multi‐model averages have been used to describe how ARs will change in the future, but this type of estimate does not consider skill or independence of the climate models of interest. Here, we utilize information from various model averaging approaches, such as Bayesian model averaging (BMA), to evaluate 21 global climate models from the Coupled Model Intercomparison Project Phase 5. Model ensemble weighting strategies are based on model independence and AR performance skill relative to ERA‐Interim reanalysis data and result in higher accuracy for the historic period, for example, root mean square error for AR frequency (in % of time steps) of 0.69 for BMA versus 0.94 for the multi‐model ensemble mean. Model weighting strategies also result in lower uncertainties in the future estimates, for example, only 20–25% of the total uncertainties seen in the equal weighting strategy. These model averaging methods show, with high certainty, that globally the frequency of ARs is expected to have average relative increases of ~50% (and ~25% in AR intensity) by the end of the century.

Projections of Selenga river runoff in the XXI century and uncertainty estimates

Abstract. The Selenga River, which originates in Mongolia, contributes nearly 50 % of the total inflow into Lake Baikal. Since 1996, the longest low-flow period has been observed. This paper focuses on the spatially distributed process-based modeling to assess possible runoff changes under climate projections in the XXI century using an ensemble of global climate models (GCMs) from ISI-MIP2 (Inter-Sectoral Impact Model Intercomparison Project, phase 2) and RCP-scenarios (Representative Concentration Pathway) as inputs. The ECOMAG hydrological model was applied to simulate possible runoff changes in the Selenga River basin. According to the simulations, the low-flow runoff into Lake Baikal will be continued throughout the XXI century under all the RCP-scenarios. Furthermore, as hydrological projections for the future are characterized by significant climate projections uncertainty, ANOVA (analyses of variance) test was used to quantify the sources of this uncertainty. The calculations showed that the contribution of the model uncertainty (differences between GCMs) is much higher than the contribution of the scenario uncertainty (variability of RCP-scenarios) although it increases at the end of the century.

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models’ agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.

Future changes in extreme weather and pyroconvection risk factors for Australian wildfires

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

Application of Regional Climate Models for Updating Intensity-duration-frequency Curves under Climate Change

Global Climate Models (GCMs) are currently the most powerful tools for accessing changes in the hydrological regime at the watershed scale due to climate change and variability. GCMs, however, have limitations due to their coarse spatial and temporal resolutions. Regional Climate Models (RCMs) are often referred to as suitable alternatives due to their higher resolution of the long-term climate projections. It is expected that RCMs are better for simulating extreme conditions than the GCMs. This present work, investigate the difference in updated IDF (Intensity-Duration-Frequency) relationships developed using GCMs and RCMs. The IDF updating method implemented with the IDF_CC tool for Canada has been used for comparison. The analyses are conducted using 369 selected Environment and Climate Change Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2020-2100), are based on multi-model ensembles of (i) the RCMs from the NA-CORDEX (North-American Coordinated Regional Climate Downscaling Experiment) project (ensemble 1) (ii) a sub-set of six GCMs from the GCMs available in the IDF_CC tool used as drivers for the RCMs (ensemble 2) and (iii) all 24 GCMs from the IDF_CC tool database (ensemble 3). One representative concentration pathway (RCP), RCP 8.5, is used in the analysis. The RCMs from the NA-CORDEX project selected for this study use six GCMs as drivers to produce the future predictions for the North American continent, including Canada. Two metrics are applied for the comparison of results: (i) the difference in projected precipitation using the multi-model ensemble median; and (ii) the difference in uncertainty range. The uncertainty range is defined in this study as the percentage projected change in future, 25 to 75 quantiles obtained using the RCMs a GCMs ensembles. The regional models from the NA-CORDEX project generated lower extreme precipitation projections than the GCMs for the stations located in the Canadian prairies (provinces of Alberta, Saskatchewan, Manitoba). Stations located at the East and West coasts of Canada show a smaller difference in the projected extremes obtained using GCMs and RCMs. The use of RCMs shows increase in uncertainty when compared to GCMs. This result indicates that even when using regional climate models, it’s advisable to extend the analyses and include as many as possible models from different climate centers.