Regional Climate Model Results Research Articles

Converging Findings of Climate Models and Satellite Observations on the Positive Impact of European Forests on Cloud Cover

AbstractAlthough afforestation is a potential strategy to mitigate climate change by sequestering carbon, its potential biophysical effects on climate, such as regulating surface albedo, evapotranspiration, and energy balance, have not been fully incorporated into climate change mitigation strategies. This is partly due to the challenges associated with modeling the complex bidirectional interactions between vegetation and climate. In this study, we assess the impact of afforestation on low cloud cover using a regional climate model (RCM) and Earth observation data, applying a space‐for‐time approach to overcome limitations that may arise from comparing satellite and RCM results, such as different background climate conditions or different extents of land cover change. Our results show a consistent increase in low cloud cover in Europe due to afforestation in both datasets (3.71% and 3.56% on average, respectively), but the magnitude and direction of this effect depend on various factors, including location, seasonality, and forest type. These results suggest that afforestation can have important feedbacks on the climate system, and that its biophysical effects must be considered in climate change mitigation strategies. Furthermore, we emphasize the role of the modeling community in developing accurate and reliable approaches to assess the biophysical effects of land cover change on climate.

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.

Sustainability of Agricultural Productivity of Potato Crop in Desert Soils and Evaluation of Water Productivity Under Drip Irrigation System with Future Climate Changes

A field experiment was conducted during the spring season 2020 in Karbala proving/ Al-Sharia Distrit, located at latitude N 32° 42' 13.8" and longitude E 43° 54' 36.6" and at an altitude of 27 m above sea level. The experiment included a study of two factors: the first, Irrigation Interval, three treatments were used: irrigation treatment every 2 days, Irrigation treatment every 4 days, and Irrigation treatment every 6 days. The second factor is the addition of soil conditioners, in which four treatments were used: the control treatment without any addition, the treatment of adding bio-organic fertilizers, the treatment of adding water-conserving technology (polymer), and the treatment of adding water-conserving technology + fertilizers Organic vitality. The experiment was designed according to the randomized complete block design (RCBD) with three replicates and under the split block design (split block design).It use the results of regional climate models to highlight how climate change affects the region through the AquaCrop model and repeat the same process for another regional climate model and another scenario to assess the impact using climate projections and analyzing the impact of climate change on water resources, the results were as follows: An increase in the amount of annual rainfall and monsoon rain during the two periods (2016-2035) and (2046-2065) under the RCP4.5 scenario, amounting to 17.00, 9.42, 11.91, and 9.06 mm. respectively compared to the base period (1985-2005) and its increase during the period (2016-2035) according to the RCP8.5 scenario amounted to 3.97 and 4.51 mm, with a slight decrease during the period (2046-2065) amounting to -3.78 and -2.57 mm, respectively, compared to base period. An increase in the maximum and minimum temperatures, according to the climate change scenario RCP4.5, amounted to 0.81, 0.75, 1.65, and 1.55°C, and the RCP8.5 scenario amounted to 1.1, 0.98, 2.33, and 2.14°C, as an average of the values of climate models. (EC-Earth, CNRM-CM5, GFDL-ESM2M) for the maximum and minimum temperatures, respectively, during the period (2016-2035) and (2046-2065), compared to the base periodd. There were no significant differences between the current productivity of the potato crop and the expected productivity using the AquaCrop model, so the value of R2 was 0.85 between the expected and measured data for productivity for twelve years under the surface drip irrigation system.The correlation coefficient (r) was 0.951, the root mean square error (RMSE) was 2.426, and the efficiency coefficient was 0.659 for the surface drip irrigation system.

Stepwise cluster ensemble downscaling for drought projection under climate change

AbstractDrought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously.

Evaluation of extreme precipitation over Southeast Asia in the Coupled Model Intercomparison Project Phase 5 regional climate model results andHighResMIPglobal climate models

AbstractModelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model.

Rainstorms impacts on water, sediment, and trace elements loads in an urbanized catchment within Moscow city: case study of summer 2020 and 2021

In 2020 and 2021, the city of Moscow, Russia, has experienced two historical rainfall events that had caused major flooding of small rivers. Based on long-term observation datasets from the surrounding weather stations, regional mesoscale COSMO-CLM climate model results, and a detailed hydrological and water quality monitoring data, we performed a pioneer assessment of climate change and urbanization impact on flooding hazard and water quality of the urban Setun River as a case study. Statistically significant rise of some moderate ETCCDI climate change indices (R20mm and R95pTOT) was revealed for the 1966–2020 period, while no significant trends were observed for more extreme indices. The combined impact of climate change and increased urbanization is highly non-linear and results in as much as a fourfold increase in frequency of extreme floods and shift of water regime features which lead to formation of specific seasonal flow patterns. The rainstorm flood wave response time, involving infiltrated and hillslope-routed fraction of rainfall, is accounted as 6 to 11 h, which is more than twice as rapid as compared to the non-urbanized nearby catchments. Based on temporal trends before and after rainfall flood peak, four groups of dissolved chemicals were identified: soluble elements whose concentrations decrease with an increase in water discharge; mostly insoluble and well-sorted elements whose concentrations increase with discharge (Mn, Cs, Cd, Al); elements negatively related to water discharge during flood events (Li, B, Cr, As, Br and Sr); and a wide range of dissolved elements (Cu, Zn, Mo, Sn, Pb, Ba, La, Cs, U) which concentrations remain stable during rainfall floods. Our study identifies that lack of research focused on the combined impacts of climate change and urbanization on flooding and water quality in the Moscow urban area is a key problem in water management advances.

Evaluation of Surface Water Resource Availability under the Impact of Climate Change in the Dhidhessa Sub-Basin, Ethiopia

Climate change, with its reaching implications, has become a popular topic in recent years. Among the many aspects of climate change, one of the most pressing concerns has been identified as the impact on the terrestrial water cycle, which has a direct impact on human settlement and ecosystems. The paper begins by reviewing previous studies, and then identifies their flaws and future research directions. The effects of climate change on surface water resources in the Dhidhessa Sub-basin, Abbay Basin, Ethiopia, were studied as practices. For future potential climate change, the results of global climate models (GCMs) and high-resolution regional climate models (RCMs) from multiple climate models were combined with data from Representative Concentration Pathways (RCPs) prepared by the Intergovernmental Panel on Climate Change from the CCAFS Data Distribution Center web page. To evaluate the impacts on water resources, various distributed hydrologic models based on local underlying surfaces were developed. The future potential climate change of the Dhidhessa Sub-basin Province was evaluated by integrating RCP outputs, whereas the climate change of the Dhidhessa River was directly derived from the results of different RCP. Dhidhessa stream flow will decrease in the future compared to the baseline era. The predictions of future discharge (stream flow) were based on climate scenarios data from 1991 to 2020 and for the future with two time windows, 2044 (2030–2059) and 2084 (2070–2099), on a monthly time-step after bias correction to both precipitation and temperature in the future climate described in the under each RCP. According to model results, the quantity of surface water resources in the Dhidhessa river region will decrease over the next 100 years, the percent decrease in mean annual stream flow by 10%, in 2044, and 6.3% in 2084, respectively, making the impact of temperature increase on runoff greater than that of precipitation. The distribution of runoff would be more even across years but more uneven across years in the long-term window, implying a higher possibility of drought and flooding. In general, this study discovered that any effect on this river that results in a decrease in flow will have a direct impact on the area’s ongoing water resource development and socioeconomic development.

Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium

Abstract. Climate change is one of the biggest challenges currently faced by society, with an impact on many systems, such as the hydrological cycle. To assess this impact in a local context, regional climate model (RCM) simulations are often used as input for rainfall-runoff models. However, RCM results are still biased with respect to the observations. Many methods have been developed to adjust these biases, but only during the last few years, methods to adjust biases that account for the correlation between the variables have been proposed. This correlation adjustment is especially important for compound event impact analysis. As an illustration, a hydrological impact assessment exercise is used here, as hydrological models often need multiple locally unbiased input variables to ensure an unbiased output. However, it has been suggested that multivariate bias-adjusting methods may perform poorly under climate change conditions because of bias nonstationarity. In this study, two univariate and four multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To this end, a case study is performed using data from the Royal Meteorological Institute of Belgium, located in Uccle. The methods are calibrated in the late 20th century (1970–1989) and validated in the early 21st century (1998–2017), in which the effect of climate change is already visible. The variables adjusted are precipitation, evaporation and temperature, of which the former two are used as input for a rainfall-runoff model, to allow for the validation of the methods on discharge. Although not used for discharge modeling, temperature is a commonly adjusted variable in both uni- and multivariate settings and we therefore also included this variable. The methods are evaluated using indices based on the adjusted variables, the temporal structure, and the multivariate correlation. The Perkins skill score is used to evaluate the full probability density function (PDF). The results show a clear impact of nonstationarity on the bias adjustment. However, the impact varies depending on season and variable: the impact is most visible for precipitation in winter and summer. All methods respond similarly to the bias nonstationarity, with increased biases after adjustment in the validation period in comparison with the calibration period. This should be accounted for in impact models: incorrectly adjusted inputs or forcings will lead to predicted discharges that are biased as well.

Quantile mapping for improving precipitation extremes from regional climate models


 The potential of quantile mapping (QM) as a tool for bias correction of precipitation extremes simulated by regional climate models (RCMs) is investigated in this study. We developed an extended version of QM to improve the quality of bias-corrected extreme precipitation events. The extended version aims to exploit the advantages of both non-parametric methods and extreme value theory. We evaluated QM by applying it to a small ensemble of hindcast simulations, performed with RCMs at six different locations in Europe. We examined the quality of both raw and bias-corrected simulations of precipitation extremes using the split sample and cross-validation approaches. The split-sample approach mimics the application to future climate scenarios, while the cross-validation framework is designed to analyse “new extremes”, that is, events beyond the range of calibration of QM. We demonstrate that QM generally improves the simulation of precipitation extremes, compared to raw RCM results, but still tends to present unstable behaviour at higher quantiles. This instability can be avoided by carefully imposing constraints on the estimation of the distribution of extremes. The extended version of the bias-correction method greatly improves the simulation of precipitation extremes in all cases evaluated here. In particular, extremes in the classical sense and new extremes are both improved. The proposed approach is shown to provide a better representation of the climate change signal and can thus be expected to improve extreme event response for cases such as floods in bias-corrected simulations, a development of importance in various climate change impact assessments. Our results are encouraging for the use of QM for RCM precipitation post-processing in impact studies where extremes are of relevance.

Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets

AbstractThe physical interactions between ice sheets and the atmosphere and ocean around them are major factors in determining the state of the climate system, yet many current Earth System models omit them entirely or treat them very simply. In this work we describe how models of the Greenland and Antarctic ice sheets have been incorporated into the global U.K. Earth System model (UKESM1) via substantial technical developments with a two‐way coupling that passes fluxes of energy and water, and the topography of the ice sheet surface and ice shelf base, between the component models. File‐based coupling outside the running model executables is used throughout to pass information between the components, which we show is both physically appropriate and convenient within the UKESM1 structure. Ice sheet surface mass balance is computed in the land surface model using multi‐layer snowpacks in subgrid‐scale elevation ranges and compares well to the results of regional climate models. Ice shelf front discharge forms icebergs, which drift and melt in the ocean. Ice shelf basal mass balance is simulated using the full three‐dimensional ocean model representation of the circulation in ice‐shelf cavities. We show a range of example results, including from simulations with changes in ice sheet height and thickness of hundreds of meters, and changes in ice sheet grounding line and land‐terminating margin of many tens of kilometres, demonstrating that the coupled model is computationally stable when subject to significant changes in ice sheet geometry.

Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

Based on the outputs of the global climate models (GCMs) HadGEM2-ES, NorESM1-M and MPI-ESM-LR from Coupled Model Intercomparison Project Phase 5 (CMIP5) and the downscaling results with the regional climate model (RCM) REMO, the ability of the climate models to reproduce the extreme precipitation in China during the current period (1986–2005) is evaluated. Then, the future extreme precipitation in the mid (2036–2065) and the late 21st century (2066–2095) is projected under the RCP8.5 scenario. The results show that the RCM simulations have great improvements compared with the GCMs, and the ensemble mean of the RCM results (ensR) outperforms each single RCM simulation. The annual precipitation of the RCM simulations is more consistent with the observation than that of the GCMs, with the overestimation of the peak precipitation reduced, and the ensR further reduces the bias. For the extreme precipitation, the RCM simulations significantly decrease the underestimation of intensity in the GCMs. The RCM simulations and the ensR can greatly improve the simulations of Rx5day and CWD compared with the GCMs, decreasing the wet bias in North China and Northwest China. In the future, the consecutive dry days (CDD) will decrease in the northern arid regions, especially in North China and Northeast China. However, the southern regions will experience longer dry period. Both the amount and the intensity of precipitation will increase in various regions of China. The number of wet days will decrease in the south and increase in the north area. The significantly greater Rx5day and R95t indicate more intensive extreme precipitation in the future, and the intensity in the late 21st century will be stronger than that in the middle. Attribution analysis indicates that the extreme precipitation indices especially the R95t have significant positive temporal and spatial correlations with the water vapor flux.

Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting

Abstract. Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation set-up in which each model simulation, in turn, performs pseudo-observations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11∘ resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981–2005) and end-21st-century (2075–2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantile-mapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

Supporting climate proof planning with CLARITY's climate service and modelling of climate adaptation strategies – the Linz use-case

In recent years, the representation of climate information in a way to support decision making has been gaining momentum. Worldwide, these so-called climate services are emerging as an essential tool to connect the advances in climate science with the domains of climate change adaptation. The methodology developed within the CLARITY project (funded through European Union funding program Horizon 2020) is aimed at implementing a new generation of climate services specifically designed to assess adaptation measures at the city level under the effects of extreme weather events in the context of climate change. These effects are assessed based on observations as well as climate projections, and the subsequent derivation of climate indices to address changes in climate extremes. The dynamical-statistical downscaling of regional climate model results is used to obtain this information on fine spatial scales (100 m), hence providing urban scale projections and enabling climate sensitivity simulations of adaptation measures on the urban scale. The climate adaptation strategies encompass, among others, green roofs, increasing roof albedo, as well as changes in soil sealing. Here, the climate assessment methodology developed within CLARITY will be discussed in detail, and results for the city of Linz (Austria) presented. In addition, the usage of these methods and results within the CLARITY climate service as well as the connection to urban climate change resilience will be highlighted.

Changes in temperature and precipitation in the instrumental period (1951–2018) and projections up to 2100 in Podgorica (Montenegro)

AbstractThe article presents the results of the analysis of several temperature and precipitation parameters in the instrumental period in the capital of Montenegro—Podgorica, for the period 1951–2018. In order to use the latest results of several Regional Climate Models (RCMs) developed for the Western Balkans, the results of temperature and precipitation modelling for the period 2011–2100, ALADIN, HIRHAM i RACMO models for RCP4.5 and RCP8.5 scenarios, are also presented. For the observed 68‐year period (1951–2018), the trend calculations clearly show that the temperature is rising. This is indicated by all the analysed temperature parameters (TY, TW, TSp, TSu, TA, TYx, TYn, FD, ID, SU, TD, TR, T35+ and T40+). Although seasonal and annual precipitation totals do not show significant changes in the instrumental period, Podgorica's climate has become more arid and extreme, as the number of days with precipitation ≥1 mm (R1) has significantly decreased. Conversly, the number of days with precipitation has increased ≥40 and 50 mm (R40 and R50). The projected changes in the considered precipitation parameters are in most cases insignificant. The obtained results indicate that there is a high agreement between the temperatures projections of the three models used. Concerning precipitation, it appears that their modelling is more complex, because there are visible qualitative and quantitative differences in the projections of models of future changes in seasonal and annual sums (RY, RW, RSp, RSu and RA), that is, the number of precipitation days (R0.1, R1, R10, R20, R30, R40 and R50). The projections of the used models show that in the future we can expect a warmer climate with more extremes of temperature and precipitation in the grid area to which Podgorica belongs.

Modeling the urban climate of Budapest using the SURFEX land surface model driven by the ALADIN-Climate regional climate model results

Modeling the urban climate of Budapest using the SURFEX land surface model driven by the ALADIN-Climate regional climate model results

Future changes in five extreme precipitation indices in the lowlands of Romania

AbstractThis paper presents the results of a study focused on the projected changes in extreme precipitation indices in Romania during three future periods: 2021–2040, 2041–2070, and 2071–2100. We investigated changes in future climate based on historical and modelled data sets of daily precipitation. We compared the values calculated for the three future periods with those obtained for the historical reference period 1961–1990. The historical observation data recorded at 30 weather stations and data extracted from six regional climate model outputs (ALADIN53, CCLM4‐8‐17, RACMO22E, RCA4, REMO2009, and WRF331F) under moderate (RCP4.5) and pessimistic (RCP8.5) scenarios were employed. Five indices established by the Expert Team for Climate Change Detection Monitoring and Indices were analysed: consecutive dry days, consecutive wet days, heavy precipitation days, very heavy precipitation days, and annual total wet‐day precipitation. The series of indices were generated using ClimPACT2 software. Based on bias correction methods, the quality of the modelled data considerably increased, and the errors obtained for the daily precipitation varied in the range of ±0.05 to ±1.22 mm/day. The main findings revealed an increase in three of the extreme precipitation indices based on all models considered compared to the historical period. The most important increase (more than 50%) compared to the historical period was detected in the frequency of very heavy precipitation days and the annual total wet‐day precipitation. They were followed by the heavy precipitation days index. The consecutive dry days index indicated mainly no change or a slow decrease, while inconsistencies among the results of regional climate models were detected for the consecutive wet days index. For all indices except the consecutive dry days index, the ALADIN53 and WRF331F models provided the highest projected values for both scenarios. The dispersion of values obtained as model output for each location was higher for RCP8.5 than for RCP4.5.

Regional grid refinement in an Earth system model: impacts on the simulated Greenland surface mass balance

Abstract. In this study, the resolution dependence of the simulated Greenland ice sheet surface mass balance (GrIS SMB) in the variable-resolution Community Earth System Model (VR-CESM) is investigated. Coupled atmosphere–land simulations are performed on two regionally refined grids over Greenland at 0.5∘ (∼55 km) and 0.25∘ (∼28 km), maintaining a quasi-uniform resolution of 1∘ (∼111 km) over the rest of the globe. On the refined grids, the SMB in the accumulation zone is significantly improved compared to airborne radar and in situ observations, with a general wetting (more snowfall) at the margins and a drying (less snowfall) in the interior GrIS. Total GrIS precipitation decreases with resolution, which is in line with best-available regional climate model results. In the ablation zone, CESM starts developing a positive SMB bias with increased resolution in some basins, notably in the east and the north. The mismatch in ablation is linked to changes in cloud cover in VR-CESM, and a reduced effectiveness of the elevation classes subgrid parametrization in CESM. Overall, our pilot study introduces VR-CESM as a new tool in the cryospheric sciences, which could be used to dynamically downscale SMB in scenario simulations and to force dynamical ice sheet models through the CESM coupling framework.

UNCERTAINTY OF CLIMATE PROJECTIONS AND AN APPROACH UTILIZING CLIMATE MODEL OUTPUTS FOR HYDROLOGIC COMPUTATION IN THE BA RIVER BASIN

A top-down approach begins with Global Climate Models (GCMs) is a common method for assessing climate change impacts on water resources in river basins. To overcome the coarse resolution of GCMs, dynamic downscaling by regional climate models (RCMs) with bias-correction procedures is utilized with the aim to reflect the meteorological features at the river basin scale. However, the results still entail large uncertainties. This paper examines the ability to capture the observed baseline temperature and precipitation (1986-2005) in the Ba River Basin from GCM outputs, RCM outputs, bias-corrected GCM outputs and bias-corrected RCM outputs by analyzing statistical indicators between historical simulations and observed data in 4 temperature and 6 rainfall stations. Bias-corrected results of both GCM and RCM have significantly smaller errors compared to the unbias-corrected ones. The uncertainty of future climate projection for the mid and late 21th century of the bias-corrected GCMs and RCMs are evaluated. It is found that there is still uncertainty in projected results. A concept of “Decision-Scaling” which combines top-down and bottom-up approaches is proposed to assess the climate change impacts on hydrological system to take into account uncertainties of climate projections by models.

Temperature seasonality in the North American continental interior during the Early Eocene Climatic Optimum

Abstract. Paleogene greenhouse climate equability has long been a paradox in paleoclimate research. However, recent developments in proxy and modeling methods have suggested that strong seasonality may be a feature of at least some greenhouse Earth periods. Here we present the first multi-proxy record of seasonal temperatures during the Paleogene from paleofloras, paleosol geochemistry, and carbonate clumped isotope thermometry in the Green River Basin (Wyoming, USA). These combined temperature records allow for the reconstruction of past seasonality in the continental interior, which shows that temperatures were warmer in all seasons during the peak Early Eocene Climatic Optimum and that the mean annual range of temperatures was high, similar to the modern value ( ∼ 26 °C). Proxy data and downscaled Eocene regional climate model results suggest amplified seasonality during greenhouse events. Increased seasonality reconstructed for the early Eocene is similar in scope to the higher seasonal range predicted by downscaled climate model ensembles for future high-CO2 emissions scenarios. Overall, these data and model comparisons have substantial implications for understanding greenhouse climates in general, and may be important for predicting future seasonal climate regimes and their impacts in continental regions.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.