Regional Climate Change Assessment Research Articles

Assessment of future rainfall for the Brahmani-Baitarani river basin – practical implications of limited data availability

AbstractSevere floods are common in the Brahmani-Baitarani river basin in India. Insights into the implications of climate change on rainfall extremes and resulting floods are of major importance to improve flood risk analysis and water system design. A wide range of statistical and dynamical downscaling and bias-correction methods for the generation of local climate projections exists. Yet, the applicability of these methods highly depends on availability of meteorological data. In developing countries, data availability is often limited, either because data do not exist or because of restrictions on use. We here present a climate change analysis for the Brahmani-Baitarani river basin focusing on changes in rainfall using data from three GCMs from the Fifth Coupled Model Intercomparison Project (CMIP5) that were selected based on their performance. We apply and compare two widely used and easy to implement bias-correction methods. These were selected because reliable open historical meteorological datasets required for advanced methods were not available. The results indicate likely increases in monsoon rainfall especially in the mountainous regions and likely increases in the number of heavy rain days. We conclude with a discussion on the gap between state-of-the-art downscaling techniques and the actual options in regional climate change assessments.

Projecting Canadian Prairie Runoff for 2041–2070 with North American Regional Climate Change Assessment Program (NARCCAP) Data

AbstractThe South Saskatchewan River Basin (SSRB) of Alberta, Canada, is semiarid and under severe water stress due to increasing human demands. We present the first examination of projected changes in SSRB runoff from a large set of North American Regional Climate Change Assessment Program regional climate models (RCMs) plus one Coordinated Regional Climate Downscaling Experiment RCM. We used six different runoff estimation methods: total surface and subsurface runoff (total runoff), surface runoff, and four estimations based on Budyko functions. Most RCM estimations showed substantial biases and distribution differences when compared to observed data; thus bias correction was necessary. Total runoff was the best of the six variables in modeling observed runoff for each of the four SSRB subbasins. Projected total runoff for 2041–2070 shows a geographic gradient in the SSRB, with possible drying in the southern Oldman River subbasin and possible increased runoff in the northernmost Red Deer River subbasin. A shift to an earlier spring peak in runoff and drier late summer, with a need for increased irrigation, should be expected. In a first examination of the important question of projected changes in interannual variability, we show increasing magnitude. This result further adds to adaptation challenges over the course of this century in this basin, which is already largely closed to further allocation.

Evaluation of regional very heavy precipitation events during the summer season using NARCCAP contemporary simulations

ABSTRACTRegional climate models (RCMs) from the North American Regional Climate Change Assessment Program (NARCCAP) are compared with the two gridded precipitation data sets [Climate Prediction Center (CPC) and the University of Washington (UW)] and the North American regional reanalysis (NARR) to examine if RCMs are able to reproduce very heavy precipitation under similar physical conditions seen in observations. The analysis focuses on contemporary climate (1982–1999) in an upper Mississippi region during the summer (June–July–August) months and utilizes output from NARCCAP RCMs forced with a reanalysis and atmosphere–ocean global climate models (AOGCMs).The NARCCAP models generally reproduce the precipitation frequency versus intensity spectrum seen in observations up to around 25 mm day−1, before producing overly strong precipitation at high intensities. CRCM simulations produce lower precipitation amounts than the rest of the models and observations past the 25 mm day−1 threshold. Further analysis focuses on precipitation events exceeding the 99.5th percentile that occur simultaneously at several points in the region, yielding ‘widespread events’. Apart from the CRCM and EPC2 simulations, models and observations produce peaks in widespread events during 0300–0900 UTC, although the models typically produce slightly weaker intensities compared to observations. Widespread precipitation falls too frequently throughout the day, especially between 1500 and 2100 UTC, compared to observations. Composite precipitation shows inter‐model differences in magnitude and location of widespread events. Examination of additional fields shows that NARCCAP models produce credible representations of very heavy precipitation and their supporting environments when compared to the NARR.

Shifting the urban heat island clock in a megacity: a case study of Hong Kong

With increasing levels of urbanization in the near future, understanding the impact of urbanization on urban heat islands (UHIs) is critical to adapting to regional climate and environmental changes. However, our understanding of the UHI effect relies mainly on its intensity or magnitude. The present study evaluates the impact of urbanization on UHI duration changes by comparing three stations with different rates of urbanization, including highly developed and developing urban areas throughout Hong Kong, from 1990–2015. Results show that the 26 year average UHI intensity in highly urbanized regions is much higher than that in developing areas, and the 26 year average of UHI duration is similar. Over the past 25 years, however, UHI duration has increased only in developing urban areas, from 13.59–17.47 hours. Both earlier UHI starting and later UHI ending times concurrently contribute to the UHI effect being experienced for a longer duration. The differences in UHI duration change between the two areas are supported by population and by night light changes from space. Increasing night light, which suggests enhancements in the economic infrastructure, occurred only in the developing urban areas. Our results suggest that changes in UHI duration should be included in an assessment of regional climate change as well as in urban planning in a megacity.

Climate change impact analysis on watershed using QSWAT

This study focuses on analysis of future climate change impacts on West Nishnabotna Watershed, using climate model data and an open source hydrological model—Soil and Water Assessment Tool (SWAT), which is run in Quantum Geographical Information System(QGIS) environment. The data required to simulate hydrological processes using SWAT plugin in QGIS (QSWAT) are Digital Elevation Model, land use/landcover map, soil map, slope map and climate data of the study area. The required climate parameters like maximum temperature, minimum temperature and precipitation are obtained from North American Regional Climate Change Assessment Program climate model database which are bias corrected for the study area using linear scaling method. SWAT model is setup using observed climate and the model calibration and validation is performed using SWAT Calibration and Uncertainty Program (SWAT-CUP). Simulations are run for historic (1971–1999) and future (2041–2069) climate conditions for the stream flow prediction. The climate change impact analysis like anomalies and trends in the hydrological regime of the watershed are carried out based on QSWAT simulation results.

A Mechanistically Credible, Poleward Shift in Warm-Season Precipitation Projected for the U.S. Southern Great Plains?

Abstract Global and regional climate model ensembles project that the annual cycle of rainfall over the southern Great Plains (SGP) will amplify by midcentury. Models indicate that warm-season precipitation will increase during the early spring wet season but shift north earlier in the season, intensifying late summer drying. Regional climate models (RCMs) project larger precipitation changes than their global climate model (GCM) counterparts. This is particularly true during the dry season. The credibility of the RCM projections is established by exploring the larger-scale dynamical and local land–atmosphere feedback processes that drive future changes in the simulations, that is, the responsible mechanisms or processes. In this case, it is found that out of 12 RCM simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP), the majority are mechanistically credible and consistent in the mean changes they are producing in the SGP. Both larger-scale dynamical processes and local land–atmosphere feedbacks drive an earlier end to the spring wet period and deepening of the summer dry season in the SGP. The midlatitude upper-level jet shifts northward, the monsoon anticyclone expands, and the Great Plains low-level jet increases in strength, all supporting a poleward shift in precipitation in the future. This dynamically forced shift causes land–atmosphere coupling to strengthen earlier in the summer, which in turn leads to earlier evaporation of soil moisture in the summer, resulting in extreme drying later in the summer.

Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data.

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.

Assessing NARCCAP climate model effects using spatial confidence regions.

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.

Assessing the Effects of Climate Change on Water Quantity and Quality in an Urban Watershed Using a Calibrated Stormwater Model

Assessing climate change (CC) impacts on urban watersheds is difficult due to differences in model spatial and temporal scales, making prediction of hydrologic restoration a challenge. A methodology was developed using an autocalibration tool to calibrate a previously developed Storm Water Management Model (SWMM) of Difficult Run in Fairfax, Virginia. Calibration was assisted by use of multi-objective optimization. Results showed a good agreement between simulated and observed data. Simulations of CC for the 2041–2068 period were developed using dynamically downscaled North American Regional CC Assessment Program models. Washoff loads were used to simulate water quality, and a method was developed to estimate treatment performed in stormwater control measures (SCMs) to assess water quality impacts from CC. CC simulations indicated that annual runoff volume would increase by 6.5%, while total suspended solids, total nitrogen, and total phosphorus would increase by 7.6%, 7.1%, and 8.1%, respectively. The simulations also indicated that within season variability would increase by a larger percentage. Treatment practices (e.g., bioswale) that were intended to mitigate the negative effects of urban development will need to deal with additional runoff volumes and nutrient loads from CC to achieve the required water quality goals.

Projected hydrological changes in the North Carolina piedmont using bias-corrected North American Regional Climate Change Assessment Program (NARCCAP) data

Study regionThe Haw River basin in the North Carolina Piedmont. Study focusSimulation of hydrologic change by projected CO2 and climate using the Soil and Water Assessment Tool (SWAT) hydrologic model. Biases of climate output from the North American Regional Climate Change Assessment Program (NARCCAP) were corrected using the LOCal Intensity (LOCI) scaling method for precipitation and Fourier functions for temperature. New hydrological insights for the regionEvapotranspiration (ET) and water yield (WY) with projected CO2, precipitation, and temperature during 2044–2070 were affected by each climate factor separately and synergistically. Increasing CO2 to 600ppm only scenario resulted in an ET decrease (5–17%) which led to WY increase (17–36%). With projected temperature increases (1–5°C) only scenarios, ET was projected to increase noticeably (12–112%) especially in winter and spring. The amount of projected ET increase was reduced by a CO2 increase to 600ppm due to decreased stomatal conductance. Projected WY varied due to the high variability of future precipitation patterns (−54% to 33%) but generally increased when solely precipitation projections were applied. However, WY with combined effects of CO2, precipitation, and temperature did not show significant changes compared with the historical WY. Therefore, it is necessary to incorporate interactions of precipitation, temperature, and CO2 to simulate future water availability in the North Carolina Piedmont.

Projected precipitation changes within the Great Lakes and Western Lake Erie Basin: a multi‐model analysis of intensity and seasonality

ABSTRACTThe Great Lakes region encompasses the largest freshwater lake network in the world and supports a diverse network of agriculture, transportation, and tourism. Recently, Lake Erie has experienced increased hypoxia events, which have been attributed to agricultural practices and changes in run‐off. Here we examine the projected changes in extreme precipitation events to address concerns regarding regional agriculture, surface run‐off, and subsequent water quality. Precipitation projections within the overall Great Lakes Basin and the Western Lake Erie Basin subregion are examined using climate model simulations of varying spatial resolutions to understand historical precipitation and projected future precipitation. We develop three model ensembles for the historical period (1980–1999) and the mid‐century (2041–2060) that cover a range of spatial resolutions and future emissions scenarios, including: (1) 12 global model members from the fifth Climate Model Intercomparison Project (CMIP5) using Representative Concentration Pathway (RCP) 8.5, (2) ten regional climate model (RCM) members from the North American Regional Climate Change Assessment Program driven by CMIP3 global models using the A2 emissions scenario, and (3) two high resolution RCM simulations (RCM4) driven by CMIP5 global models using the RCP 8.5 scenario. For the historical period, all model ensembles overestimate winter and spring precipitation, and many of the models simulate a summer drying that is not observed. At mid‐century, most of the models predict a 10–20% increase in precipitation depending on the time of year. Daily probability distribution functions from three model ensembles reveal spring seasonal increases in high precipitation event probabilities when compared to the historical period, suggesting an increase in the frequency of high intensity precipitation at mid‐century. Overall, the presence of lakes or higher spatial resolution does not ensure improved representation of historical processes, and more complex interactions between large‐scale dynamics, local feedbacks, and physical parameterizations drive the model spread.

The impact of climate change on the characteristics of the frost-free season over the contiguous USA as projected by the NARCCAP model ensembles

Understanding the impacts of climate change on frost-free seasons is key to designing effective adaptation strategies for ecosystem management and agricultural production. This study examines the potential changes in the frost-free season length between historical (1971−2000) and future (2041−2070) periods over the contiguous USA with a focus on spatial variability and the uncertainties surrounding the projections, using daily minimum temperature outputs from a 6-member ensemble composed of 3 regional-climate models nested within 3 general-circulation models provided by the North American Regional Climate Change Assessment Program (NARCCAP). Despite broad agreement among ensemble members on future advance (delay) of last spring (first autumn) frost and thus an increase in the frost-free season length across the USA, large inter-model spread, an indication of high uncertainties, exists especially over the mountainous West. The uncertainty surrounding the first autumn frost is the major contributor to the high uncertainty in the projected frost-free season length for the West, in contrast to other regions, especially the Great Plains, where the ensemble spread in the last spring frost contributes more to the season-length uncertainty. The lengthening is not symmetric in spring and autumn, and the asymmetry, which varies by region and model, is relatively small in the eastern and central USA, and large in the western USA. Across California and portions of the Southwest, the advance in the last spring frost will be more than the delay in the first autumn frost. Elsewhere in the western USA, especially over high terrain, the frost-free season will lengthen more in autumn than in spring.

Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease −19% (NARCCAP) to −38% (VR-CESM), with an ensemble median change of −27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075–2100 that average western USA mountain snowfall decreases by −30%, snow cover by −44%, SWE by −69%, and average surface temperature increase of +5.0 $$^\circ$$ C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.

Assessing crop yield simulations driven by the NARCCAP regional climate models in the southeast United States

AbstractA set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/cotton yield amounts for the period of 1981–2003 are obtained in a regularly gridded (~20 km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regional‐climate model‐driven crop yield amounts are better simulated than the reanalysis‐driven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The bias‐corrected or weighted composite methods combine the crop yield ensemble members better than the simple composite method. In general, the weighted ensemble crop yield simulations match marginally better with the observed‐weather‐driven yields compared to those of the other ensemble methods.

Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data

The assessment of regional climate change impacts on agriculture would benefit from a climate data processing tool that aids preparation of input data to agricultural models. A gridded data tool was developed to process the outputs of regional climate models including the COordinated Regional climate Downscaling EXperiment (CORDEX) data. The CORDEX Data Support Library (CDSL) was designed to provide functionalities associated with high performance computing and the preparation of input data without additional storage requirement. A set of functions was implemented in the CDSL to facilitate the parallel processing of CORDEX data. The CDSL had functionalities to unify the spatial extent and resolution, projection and calendar system of gridded data for creating ensemble data sets that could be imported into a model of interest using a function call. As a case study, reference evapotranspiration (ET0) in East Asia was calculated using the CDSL to process the outputs of regional climate models (RCMs) available from the website of the CORDEX East Asia. Six sets of ET0 (ETCORDEX) were calculated using CORDEX data as inputs to the FAO 56 formula. Those sets were compared with ET0 calculated using AgMERRA data as inputs (ETAgMERRA). The processing time for climate data decreased with the increasing number of processor cores when the features of parallel processing were used for the CDSL. For example, the running time for data loading reduced by 88% using the CDSL with 16 processor cores. These results demonstrated that the CDSL would facilitate regional climate change impact assessment using a considerably large amount of climate data, e.g., >200GB, as inputs to agricultural models.

Potential Value of Expert Elicitation for Determining Differential Credibility of Regional Climate Change Simulations: An Exercise with the NARCCAP co-PIs for the Southwest Monsoon Region of North America

Abstract In this brief article, we report the initial results of an expert elicitation with the co-PIs (regional climate modelers) of the North American Regional Climate Change Assessment Program regarding their evaluation of the relative quality of regional climate model simulations focusing on the subregion dominated by the North American monsoon (NAM). We assumed that an expert elicitation framework might reveal interesting beliefs and understanding that would be different from what would be obtained from calculating quantitative metrics associated with model quality. The simulations considered were of six regional climate models (RCMs) that used NCEP Reanalysis 2 as boundary conditions for the years 1980–2004. The domain covers most of North America and adjacent oceans. The seven participating regional modelers were asked to complete surveys on their background beliefs about model credibility and their judgments regarding the quality of the six models based on a series of plots of variables related to the NAM (e.g., temperature, winds, humidity, moisture flux, precipitation). The specific RCMs were not identified. We also compared the results of the expert elicitation with those obtained from using a series of metrics developed to evaluate a European collection of climate model simulations. The results proved to be quite different in the two cases. The results of this exercise proved very enlightening regarding regional modelers’ perceptions of model quality and their beliefs about how this information should or should not be used. Based on these pilot study results, we believe a more complete study is warranted.

Human-induced modifications to land surface fluxes and their implications on water management under past and future climate change conditions

We used a coupled land-atmospheric simulation framework to quantify the feedback impacts of water resources in a changing climate. The simulations dynamically downscaled data from the North American Regional Reanalysis (NARR) data, Climate Forecast System (CFS) Version 2 and the North American Regional Climate Change Assessment Program (NARCCAP), to investigate irrigation effects on temperatures and surface fluxes under current and future scenarios in Southern Idaho. This study implements irrigation schemes within the Noah Land Surface Model (LSM) coupled with the Weather Research and Forecast (WRF) 3.4 at a 4-km resolution to diagnose irrigation-induced changes to surface energy fluxes and boundary layer properties. Our results suggest that induced cooling via evaporation causes a decrease of 0.8°C to the air temperature over April − October, a decrease of up to 90m of the planetary boundary layer depth and an increase of 2.0°C in the dew point over the irrigated areas. On average, the growing season start date trends 0.46days/year earlier and the growing season termination date trends 0.60days/year later. A decrease in precipitation with time is also seen at all elevation ranges from the year 2040–2070, with the lowest elevation levels seeing a bigger decrease than the higher levels. Thus, irrigation-induced increases in growing degree days and modifications to surface fluxes are shown in the basin and it is critical to consider when planning both crop and water management.

On bias correction in drought frequency analysis based on climate models

Assessment of future drought characteristics based on climate models is difficult as climate models usually have bias in simulating precipitation frequency and intensity. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we use three bias correction techniques with different emphases and complexities to investigate how they affect the results of drought frequency and severity based on climate models. The characteristics of drought are investigated using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data and the bias-corrected RCM historical simulations. Preserving mean and standard deviation of NARR precipitation is essential in drought frequency analysis. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Different timescales of input precipitation in the bias corrections show similar results. The relative changes in drought frequency in future scenario compared to historical scenario are similar whether both scenarios are bias corrected or both are not bias corrected.

Evaluating NARCCAP model performance for frequencies of severe-storm environments

Abstract. An important topic for climate change investigation is the behavior of severe weather under future scenarios. Given the fine-scale nature of the phenomena, such changes can only be analyzed indirectly, for example, through large-scale indicators of environments conducive to severe weather. Climate models can account for changing physics over time, but if they cannot capture the relevant distributional properties of the current climate, then their use for inferring future regimes is limited. In this study, high-resolution climate models from the North American Regional Climate Change Assessment Program (NARCCAP) are evaluated for the present climate state using cutting-edge spatial verification techniques recently popularized in the meteorology literature. While climate models are not intended to predict variables on a day-by-day basis, like weather models, they should be expected to mimic distributional properties of these processes, which is how they are increasingly used and therefore this study assesses the degree to which the models are actually suitable for this purpose. Of particular value for social applications would be to better simulate extremes, rather than inferring means of variables, which may only change by small increments thereby making it difficult to interpret in terms of the impact on society. In this study, it is found that the relatively high-resolution NARCCAP climate model runs capture areas, spatial patterns, and placement of the most common severe-storm environments reasonably well, but all of them underpredict the spatial extent of these high-frequency zones. Some of the models generally perform better than others, but some models capture spatial patterns of the highest frequency severe-storm environment areas better than they do more moderate frequency regions.

Understanding the Effects of Climate Change on Urban Stormwater Infrastructures in the Las Vegas Valley

The intensification of the hydrological cycle due to climate change entails more frequent and intense rainfall. As a result, urban water systems will be disproportionately affected by the climate change, especially in such urban areas as Las Vegas, which concentrates its population, infrastructure, and economic activity. Proper design and management of stormwater facilities are needed to attenuate the severe effects of extreme rainfall events. The North American Regional Climate Change Assessment Program is developing multiple high-resolution projected-climate data from different combinations of regional climate models and global climate models. The objective of this study was to evaluate existing stormwater facilities of a watershed within the Las Vegas Valley in southern Nevada by using a robust design method for the projected climate. The projected climate change was incorporated into the model at the 100 year return period with 6 h duration depths, using a statistical regionalization analysis method. Projection from different sets of climate model combinations varied substantially. Gridded reanalysis data were used to assess the performance of the climate models. An existing Hydrologic Engineering Center’s Hydrological Modeling System (HEC-HMS) model was implemented using the projected change in standard design storm. Hydrological simulation using HEC-HMS showed exceedances of existing stormwater facilities that were designed under the assumption of stationarity design depth. Recognizing climate change and taking an immediate approach in assessing the city’s vulnerability by using proper strategic planning would benefit the urban sector and improve the quality of life.