Regional Climate Model Projections Research Articles

Robust handling of extremes in quantile mapping – “Murder your darlings”

Abstract. Quantile mapping is a method often used for the bias adjustment of climate model data toward a reference, i.e. to construct a transformation of the model's distribution to that of the reference. The main moments of the distributions are typically well transformed by quantile mapping, but statistical uncertainty increases towards the extreme tails, making robust transformations challenging. Because of the limited data at the extreme tails, an empirical quantile mapping also needs to make some estimation or fit a parameterised function for data beyond the calibration data range. Here, the MIdAS bias adjustment platform is employed to explore different methods for handling the extreme tail; these approaches are evaluated using an indicator of extreme precipitation – the maximum daily precipitation amount per year. Different methodologies are evaluated for a large ensemble of regional climate model projections over Scandinavia. The sensitivity of the empirical quantile mapping to the tails of the distribution is demonstrated, and it is found that the behaviour is significantly different within and outside of the calibration period, causing severe issues with the temporal consistency of the time series. The sensitivity is identified to be due to differences in the activated features of the bias adjustment within the calibration period (where the empirical transfer function is applied) and outside of that period (where the extrapolation method is likely applied). This means that the bias adjustment method is, in a sense, different between different time periods. Furthermore, finding a robust parameterisation for the tail is not straightforward. We identify a two-step solution that works well for this problem: We refer to the first step as “Murder your darlings”. By excluding data from the tail data in the calibration period, the extrapolation feature is activated for all time periods, even the calibration period. In the second step, applying an outlier-insensitive method for linear regression works well for finding an extrapolation parameterisation for the tail.

Physically-based modelling of UK river flows under climate change

This study presents the model setup and results from the first calibrated, physically-based, spatially-distributed hydrological modelling of combined land cover and climate change impacts on a large sample of UK river catchments. The SHETRAN hydrological model was automatically calibrated for 698 UK catchments then driven by the 12 regional climate model projections from UKCP18, combined with urban development and natural flood management scenarios. The automatic calibration of SHETRAN produces a median Nash-Sutcliffe efficiency value of 0.82 with 581 catchments having a value greater than 0.7. 24 summary metrics were calculated to capture changes to important aspects of the flow regime. The UKCP18 realisations in SHETRAN indicate that a warming climate will cause river flows, on average, to decrease. These decreases are simulated to be greatest in the south and east of the UK, with droughts becoming longer and more severe. While high flows also decrease on average, an increased number of extremes are exhibited, implying a greater number of extreme flood events in the future, particularly in the north and west of the UK. In the urban development scenarios, for flood events there is an increase in flow with the increased urbanization, with the 1 in 3-year peak flow event showing the greatest increase. The natural flood management scenarios consider the effect of increasing woodland and adding surface water storage ponds. The inclusion of these features produces a complex response but overall, the modelling shows a reduction in low, median, and high flows, although the more extreme the flow event the smaller the percentage change in flow. Simulated timeseries and summary metric datasets are freely available on the CEDA archive.

Transferability and Explainability of Deep Learning Emulators for Regional Climate Model Projections: Perspectives for Future Applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global climate models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here, we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as perfect prognosis (PP) and model output statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of explainable artificial intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differs between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications. Significance Statement Regional climate model (RCM) emulators are a cost-effective emerging approach for generating comprehensive ensembles of regional climate projections. Promising results have been recently obtained using deep learning models. However, their potential to capture the regional climate dynamics and to emulate other periods, emission scenarios, or driving global climate models (GCMs) remains an open issue that affects their practical use. This study explores the potential of current emulation approaches incorporating new explainable artificial intelligence (XAI) evaluation techniques to assess the reliability and transferability of the emulators. Our findings show that the different global and regional model biases involved in the different approaches play a key role in transferability. Based on the results obtained, we provide some prospects for potential applications of these models in challenging problems.

Impact of climate change on major floods flowing into the Georges River estuary, Australia

Coastal flooding induced by storm surges and heavy rainfall is one of the most frequent climate-related natural hazards along the southeast Australian coast, home to more than 55% of the Australian population. Flooding in this densely populated region is a threat to public safety, coastal infrastructure, ecological systems and the economy. Although climate change is expected to cause an increase in major floods, few studies have quantified the potential changes in flood severity. This study quantifies the changes in flood peak discharge flowing to the Georges River estuary in Australia due to climate-change. An event-based hydrological model, Watershed Bounded Network Model (WBNM), was used to predict flood discharge. This hydrological model was forced by rainfall data obtained from the New South Wales and Australian Capital Territory Regional Climate Modelling Project version 1.5 (NARCliM1.5) for both historical and the Representative Concentration Pathway 8.5 (RCP8.5) conditions. Model calibration for the floods of March 1978 and March 2022 achieved a general agreement between the predicted and observed hydrographs, with an overall average 14% error in the peak values, further demonstrating that the modelling approach is generally reliable in projections of flood severity. Using high resolution climate model projections, the present study observed an increase of 22% in the model ensemble average from historical conditions to the RCP8.5 scenario for the 20-year average recurrence interval (ARI) 24 h extreme rainfall. This heightened extreme rainfall consequently resulted in the changes in flood discharge with an average rise of 55%. This study provides specific assessment of climate-generated risks for densely-populated regions, especially those on Australian east coast. Global studies have suggested that extreme precipitation events will increase under climate change. This study supports and enhances these assertions by using high resolution downscaling to quantify the specific changes within a large catchment.

Land use modulates resistance of grasslands against future climate and inter-annual climate variability in a large field experiment.

Climate and land-use change are key drivers of global change. Full-factorial field experiments in which both drivers are manipulated are essential to understand and predict their potentially interactive effects on the structure and functioning of grassland ecosystems. Here, we present 8 years of data on grassland dynamics from the Global Change Experimental Facility in Central Germany. On large experimental plots, temperature and seasonal patterns of precipitation are manipulated by superimposing regional climate model projections onto background climate variability. Climate manipulation is factorially crossed with agricultural land-use scenarios, including intensively used meadows and extensively used (i.e., low-intensity) meadows and pastures. Inter-annual variation of background climate during our study years was high, including three of the driest years on record for our region. The effects of this temporal variability far exceeded the effects of the experimentally imposed climate change on plant species diversity and productivity, especially in the intensively used grasslands sown with only a few grass cultivars. These changes in productivity and diversity in response to alterations in climate were due to immigrant species replacing the target forage cultivars. This shift from forage cultivars to immigrant species may impose additional economic costs in terms of a decreasing forage value and the need for more frequent management measures. In contrast, the extensively used grasslands showed weaker responses to both experimentally manipulated future climate and inter-annual climate variability, suggesting that these diverse grasslands are more resistant to climate change than intensively used, species-poor grasslands. We therefore conclude that a lower management intensity of agricultural grasslands, associated with a higher plant diversity, can stabilize primary productivity under climate change.

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

AbstractGlobal climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

Multiple regional climate model projections to assess building thermal performance in Brazil: Understanding the uncertainty

Understanding the trends and uncertainties in Building Energy Simulation (BES) performance indicators under future climate conditions is crucial for mitigating issues such as overheating and power outages. To address this, we generated a set of weather files for all 27 state capitals in Brazil, considering six climate model projections (three General Circulation Models as driving models and two nested Regional Climate Models) and two distinct emission scenarios from the CORDEX project. We analyzed the variability in climatic variables and subsequently performed BES on a representative Brazilian social housing unit to evaluate its impact on the performance indicators outcomes. Consistent with previous studies, a substantial increase in cooling-related demands was observed in the more pessimistic scenario (RCP8.5) and mild increases in the more optimistic scenario (RCP2.6), with a trend toward stabilization after 2050. Regarding uncertainties, we found higher Relative Standard Deviation (RSD) values for the cooling degree hours indicator. The capitals in the Central-West, Southeast, and South regions exhibited greater uncertainty regarding temperature indicators, whereas the irradiation parameters displayed higher uncertainties in the Northeast region. For the BES outcomes, RSD values as high as 19.9% were found for cooling load values. It was also demonstrated that locations, periods, and scenarios exhibit different extreme climate model projections. Ideally, employing an ensemble of weather files developed from other models would help assess associated uncertainties in the building performance indicators.

Projected changes in Köppen‒Trewartha climate zones under 1.5–4 °C global warming targets over mid-high latitudes of Northern Asia using an ensemble of RegCM4 simulations

Mid-high latitude Northern Asia is one of the most vulnerable and sensitive areas to global warming, but relatively less studied previously. We used an ensemble of a regional climate model (RegCM4) projections to assess future changes in surface air temperature, precipitation and Köppen‒Trewartha (K‒T) climate types in Northern Asia under the 1.5–4 °C global warming targets. RegCM4 is driven by five CMIP5 global models over an East Asia domain at a grid spacing of 25 km. Validation of the present day (1986–2005) simulations shows that the ensembles of RegCM4 (ensR) and driving GCMs (ensG) reproduce the major characters of the observed temperature, precipitation and K‒T climate zones reasonably well. Greater and more realistic spatial detail is found in RegCM4 compared to the driving GCMs. A general warming and overall increases in precipitation are projected over the region, with these changes being more pronounced at higher warming levels. The projected warming by ensR shows different spatial patterns, and is in general lower, compared to ensG in most months of the year, while the percentage increases of precipitation are maximum during the cold months. The future changes in K‒T climate zones are characterized by a substantial expansion of Dc (temperature oceanic) and retreat of Ec (sub-arctic continental) over the region, reaching ∼20% under the 4 °C warming level. The most notable change in climate types in ensR is found over Japan (∼60%), followed by Southern Siberia, Mongolia, and the Korean Peninsula (∼40%). The largest change in the K‒T climate types is found when increasing from 2 to 3 °C. The results will help to better assess the impacts of climate change and in implementation of appropriate adaptation measures over the region.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory.

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across "Balochistan, Pakistan". Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historicalperiod (1991-2020) from the observational data and the two projected periods as near-future (2041-2070) and far-future (2071-2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9°C and + 3.6°C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5°C and + 4.8°C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

Changes in universal thermal climate index from regional climate model projections over European beaches

Millions of people travel to European beaches making beach tourism the largest tourism sector. Understanding how person’s thermal comfort changes with projected climate change, which is paramount in deciding where and when to visit, can help the tourism sector mitigate risk and identify opportunities. Sustainable adaptation strategies for coastal tourism can be developed for different regional changes. This work calculates the Universal Thermal Comfort Index (UTCI), a comprehensive assessment of the human physiological response to the thermal environment, from an ensemble of regional climate models participating in EURO-CORDEX. Under Representative Concentration Pathways (RCP) 8.5, the ensemble’s projections of the UTCI in the near- and far-future, compared to a historical reference period, show a robust and statistically significant increase across the entire EURO-CORDEX domain. Four popular beach regions in Germany, Italy, France, and Spain show the beach season can be extended - starting earlier and ending later each year. Across the Mediterranean, July and August exhibit, on average, 4 more days of ‘very strong’ heat stress and 1 day of ‘extreme’ heat stress at the end of the century. In accordance with FAIR data science principles, the UTCI calculation is available in the xclim open-source python library.

Divergent future drought projections in UK river flows and groundwater levels

Abstract. Hydrological drought is a serious issue globally, which is likely to be amplified by 21st century climate change. In the UK, the impacts of changes in river flow and groundwater drought severity in a future of climate change and higher water demand are potentially severe. Recent publication of a new nationally consistent set of river flow and groundwater level projections (the eFLaG dataset), based on state-of-the-art UKCP18 climate projections, offers a unique opportunity to quantitatively assess future UK hydrological drought susceptibility. The dataset includes a transient, multi-model ensemble of hydrological projections driven by a single regional climate model (RCM), with a 12-member perturbed-parameter ensemble, for 200 catchments and 54 boreholes spanning a period from 1961 to 2080. Assessment of a baseline period (1989–2018) shows that the RCM-driven projections adequately reproduce observed river flow and groundwater level regimes, improving our confidence in using these models for assessment of future drought. Across all hydrological models and most catchments, future low river flows are projected to decline consistently out to 2080. Drought durations, intensities and severities are all projected to increase in most (over 90 %, pooling across different drought characteristics) UK catchments. However, the trajectory of low groundwater levels and groundwater drought characteristics diverges from that of river flows. Whilst groundwater levels at most (> 85 %) boreholes are projected to decline (consistent with river flows), these declines are relatively modest (< 10 % reduction) in transient low groundwater levels by 2080, and, in fact, six show moderate increases. Groundwater drought characteristics in the far future (2050–2079) are often similar to those of the baseline (1989–2018), with only 33 % of boreholes showing an increase (towards worsening drought) of more than 10 % for drought severity (48 % of boreholes for drought intensity). Interestingly, for some boreholes, droughts are projected to be more prolonged and severe in the near future (2020–2049) before returning to shorter durations and lower severity in the far future. A number of explanatory factors for this divergence between river flow and groundwater are discussed. The sensitivity to seasonal changes in precipitation and potential evapotranspiration is proposed as a principal driver of divergence because low river flows are more influenced by shorter-term rainfall deficits in the summer half-year, whilst groundwater drought appears to be offset somewhat by the wetter winter signal in the RCM projections. Our results have important implications for water management, demonstrating a widespread increase in river flow drought severity and diminishing low flows that could have profound societal and environmental impacts unless mitigated. Furthermore, the divergence in projections of drought in river flows and groundwater levels brings into question the balance between surface and subsurface water resources. The projected contrast in fortunes of surface and subsurface water resources identified for the UK may be replicated in other parts of the world where climate projections suggest a shift towards drier summers and wetter winters.

Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.

Climate change information tailored to the agricultural sector in Central Europe, exemplified on the region of Lower Franconia

There is a growing societal, economic, and political demand to translate available data on regional climate change into sector-specific, practice-oriented, and user-friendly information. The study presents a demand-driven approach to specify the impacts of regional climate change on agriculture, viticulture, and fruit and vegetable growing in Lower Franconia, southern Germany, a region with heterogeneous topography, diversified land use patterns, and intense activities in the sectors specified above. The approach is based on an ensemble of high-resolution regional climate model projections, a bias correction tool, and a large spectrum of meteorological (extreme) indicators that are crucial to the agricultural sector in Central Europe, as inferred from a stakeholder survey.For several decades, Lower Franconia represents a hotspot region of climate change with enhanced heat waves, prolonged droughts, and intermittent local flooding by heavy rainfall events. Results of the high-resolution regional climate model projections indicate an increase of hot days and tropical nights by a factor of 5 and 12, respectively, if greenhouse gas emissions continue to grow until 2100 according to the RCP8.5 emission scenario. At the same time, droughts will occur more frequently and last longer while rainfall intensity enhances. A longer growing period starting more than 40 days earlier (compared to the reference period 1970 to 1999) implies a higher risk of late frost damage for crops, fruits, grapes, and even some tree species. In contrast, the thermal prerequisites for viticulture will be satisfied across the entire region, even at higher-elevation sites. These facets of regional climate change are made accessible to users and the public via an interactive field-resolving web portal. Altogether, they gravely challenge the historically developed land use systems in Lower Franconia and require timely adaptation and mitigation strategies.

Uncertainty analysis of future summer monsoon duration and area over East Asia using a multi-GCM/multi-RCM ensemble

This study examines the spatiotemporal characteristics of the summer monsoon rainy season over East Asia using six regional climate models (RCMs) participating in the Coordinated Regional Domain Experiment (CORDEX) East Asia Phase II project. The framework combining multiple global climate models (GCMs) with multiple RCMs produces a larger spread in summer monsoon characteristics than driving GCMs only, enabling a better quantification of uncertainty factors. On average, the RCM simulations reproduce the observed summer monsoon duration and area better than the corresponding boundary GCMs, implying the added values of downscaling. Both the area and duration of the East Asian summer monsoon are projected to increase by the late 21st century, more strongly in high emission scenarios than in low emission scenarios, particularly in China. Different responses between scenarios, which indicate warming mitigation benefits, only become significant in the late 21st century due to large intersimulation uncertainties. Analysis of variance results show that uncertainty in future monsoon area and duration is larger between boundary GCMs than between RCMs over East Asia and its coastal subregions. A strong intersimulation relationship between RCMs and GCMs supports that boundary GCMs substantially diversify downscaled RCM projections through different climate sensitivities. Furthermore, the distinct subregional responses in future monsoon area and duration emphasize the importance of fine-resolution projections with appropriate uncertainty measures for better preparing region-specific adaptation plans.

Assessing the land-use harmonization (LUH) 2 dataset in Central Asia for regional climate model projection

Although the land-use harmonization (LUH) datasets have been widely applied in regional climate model (RCM) projections for investigating the role of the land-use forcing in future climate changes, few studies have thoroughly assessed them on local scale, which may bring large uncertainties in the resultant climate information for designing adaption and mitigation measures of climate change. The authors use a local land-use dataset (referred to as Li-LU) as the benchmark to assess the latest version of the LUH datasets, LUH2, in Central Asia (CA) which has undergone extensive land-use changes (LUCs) and might undergo extensive LUCs in the future. The results show that LUH2 has large biases in depicting the historical land-use states in CA for 1995–2015. For instance, the area of grassland (cropland) in LUH2 is about 1.4–1.5 (0.4–0.5) times of that of Li-LU. Moreover, the future LUCs predicted by LUH2 for 2045 (relative to 2005) are much smaller than those of Li-LU and these two datasets generally have opposite signals in changes. In addition, the predicted LUCs of LUH2 do not follow the causal mechanisms [the causal connections between the key drivers (e.g. population, economy, and environment) and land use] behind the LUCs in the past. If the future scenario of LUH2 is used for RCM projection in CA with the historical land-use information from Li-LU, the simulation results could be misleading for understanding the impacts of LUCs on future climate changes there. This study suggests that the LUH datasets should be carefully assessed before using them for regional studies and provides practical notes for selecting the appropriate land-use dataset for RCM projections in other areas around the world.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Sustainability of cover cropping practice with changing climate in Illinois

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3–N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021–2040) and the far-term future (2041–2060) were compared with the baseline scenario (2001–2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (−6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Evaluation of Convective Environments in the NARCliM Regional Climate Modeling System for Australia

Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate models can resolve the convective environments better than global models. Australia, especially the east and southeast parts of the continent, is a global hot spot for severe thunderstorms. This study evaluates the simulated convective environments from the New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) project based on the parameters of CAPE, CIN, 0–6-km vertical wind shear and storm severity. The ensemble regional downscaling is compared against the fifth-generation European Centre for Medium-range Weather Forecast Reanalysis (ERA5). The results show that although there are apparent biases (generally positive for CAPE and negative for CIN, and slightly overestimated vertical wind shear) in the downscaled storm parameters, the climatology of measures of storm severity over land, including their spatial patterns and seasonality, agree well with ERA5. These results have strong implication on the application of the climate projection to assess future convective environments in the region.

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI < 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

Bias‐Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations

AbstractModel output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations.