American Regional Climate Change Assessment Research Articles

Evaluating the Effectiveness of Best Management Practices in Adapting the Impacts of Climate Change-Induced Urban Flooding

Floods are amongst the most destructive and costly natural disasters impacting communities around the globe. The severity and reoccurrence of flooding events have been more common in recent years as a result of the changing climate and urbanization. Best Management Practices (BMPs) are commonly used flood management techniques that aim to alleviate flooding and its impacts by capturing surface runoff and promoting infiltration. Recent studies have examined the effectiveness of BMPs in countering the effects of flooding; however, the performance of such strategies still needs to be analyzed for possible future climate change. In this context, this research employs climate model-driven datasets from the North American Regional Climate Change Assessment Program to evaluate the effects of climate change on urban hydrology within a study region by calculating historical and projected 6 h 100-year storm depths. Finally, the climate-induced design storms are simulated in the PCSWMM model, and the three BMP options (i.e., porous pavement, infiltration trench, and green roof) are evaluated to alleviate the impact of flooding events. This study quantifies the impact of changing climate on flood severity based on future climate models. The results indicate that peak discharge and peak volume are projected to increase by a range of 5% to 43% and 8% to 94%, respectively. In addition, the results demonstrated that green roofs, Permeable Pavement, and infiltration trenches help to reduce peak discharge by up to 7%, 14%, and 15% and reduce flood volume by up to 19%, 24%, and 29%, respectively, thereby presenting a promising solution to address the challenges posed by climate change-induced flooding events.

Evaluating the Effectiveness of Low Impact Development Practices against Climate Induced Extreme Floods

Short but extreme flooding events have been frequent and severe globally due to climate change and urbanization in recent years. Similarly, researchers, scientists, and water managers are suggesting the application of sustainable flood management strategies such as Low Impact Development (LID) to mitigate the impacts of such extreme flooding events. However, most of these strategies have primarily been evaluated using historical precipitation events, which may not accurately represent the impact of climate-induced flooding events, which are projected to become more extreme. In this context, this study assesses the effectiveness of LIDs in combating climate change-induced flooding events. The North American Regional Climate Change Assessment Program (NARCCAP) climate model was applied in this study to quantify the magnitude of future projected storm depths, which are expected to increase due to climate change. Similarly, Personal Computer Storm Water Management Model (PCSWMM) was used to develop a rainfall-runoff simulation model and to assess the effectiveness of three LID techniques (Permeable Pavement, Green Roof, and Bio-Retention Cell) in reducing surface runoff under various climate scenarios. The results revealed that under the climate change scenarios the future projected design depths are expected to increase by up to 104%. Similarly, peak discharge, and total flooding volume were found to increase by 37.72% and 88.73%, respectively under the most extreme climate change scenario. Furthermore, the study demonstrated that applying LID strategies decreases peak discharge, offering a viable solution to tackle flooding events induced by climate change. The results illustrated the performance of permeable pavement was superior in reducing the peak discharge by up to 28.57%. Similarly, applying green roofs and bioretention cells reduced the peak discharge by up to 19.93% and 14.25%, respectively.

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Evaluation of land-use, climate change, and low-impact development practices on urban flooding

ABSTRACT We investigated the potential influence of land-use and climate change on urban hydrology for an urbanized watershed located in Columbia, South Carolina (USA). The Personal Computer Storm Water Management Model (PCSWMM) was used as an urban hydrology model to formulate the low-impact development (LID) controls on runoff for flood mitigation. We used ensemble projections provided by the North American Regional Climate Change Assessment Program (NARCCAP) for the climate change assessment. The results for future periods (2038–2069) show an increase in mean annual runoff from 40% to 70%. The LID mitigation strategies were compared based on the rain barrel, the rain garden, and a combination of the two approaches to evaluate the potential reduction in urban flooding. It is recommended that LID practices should be used synergistically with improved drainage facilities for successful stormwater management. This analysis will help stakeholders to develop strategies to minimize the socio-economic impacts due to urban flooding.

Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support.

Modeling climate change impact on streamflow as affected by snowmelt in Nicolet River Watershed, Quebec

Frequent spring flooding in Southern Quebec’s Nicolet River watershed has a history of causing severe damage, which is likely to worsen as climate change progresses. Employing the ArcSWAT model, an attempt was made to assess the potential impacts of climate change on the Nicolet River watershed’s seasonal and annual streamflow, particularly that portion affected by snowmelt. Calibrated and validated against observed streamflow data for the periods of 1986–1990 and 1991–2000, respectively, the model reliably predicted daily streamflow (e.g., percent bias within ±15%, Nash-Sutcliffe model efficiency >0.50, and the ratio of root mean square error to the standard deviation ≤0.70). In an effort to investigate the impacts of climate change on streamflow, future climate datasets were generated for 2053–2067 by implementing the eleven sets of existing Regional Climate Model (RCM) simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP) in the ArcSWAT model. The ArcSWAT model’s hydrological responses were closely tied to changes of climate variables: a strong correlation existed between simulated runoff and precipitation, and between temperature and predicted evapotranspiration, snowfall, and winter snowmelt. Projected future climate data showed increases in both average temperature (+2.5 °C) and precipitation (+21%). Significant greater total precipitation was forecasted for the winter season, while total snowfall was projected to decrease by 6%. However, the snowmelt showed an increasing trend for the late winter and earlier spring period. Streamflow was expected to increase annually and in most seasons except spring. Annual peak flows volumes would increase by 13% in the future and the occurrence of peak flows would shift to the winter (vs. the spring), indicating a greater risk of winter flooding in the future. The individual impact of temperature and precipitation on peak flows showed that increases in peak flows were mainly tied to increased precipitation, while the shift in their timing was mostly tied to warming temperatures.

Mid-21st century anthropogenic changes in extreme precipitation and snowpack projections over Newfoundland

Extreme precipitation events, including probable maximum precipitation (PMP) and probable maximum snow accumulation (PMSA) and 1/100 annual exceedance probability (AEP) values for precipitation (P100) and snow accumulation (expressed in snow water equivalent; SWE100) were analyzed over Newfoundland to compute the projected changes from 1971–2000 to 2041–2070. PMP and PMSA of various storm durations were simulated based on the moisture maximization of high efficiency storms. Also, P100 and SWE100 data were calculated based on the frequency analysis of liquid precipitation and snowpack data during each 30-year period. The required meteorological variables, including liquid and solid precipitation, precipitable water content, and snow accumulation, defined over a 50 × 50 km grid, were extracted from an ensemble of six regional climate model simulations provided by the North American Regional Climate Change Assessment Program (NARCCAP). Projections indicated that while PMP and P100 are intensifying in the future period, PMSA and SWE100 are declining. This is the first study which quantifies the impact of climate change on extreme-value characteristics of precipitation in Newfoundland. The results of the study can help stakeholders throughout the province to gain a better understanding of the impact of global warming on extreme meteorological events. Such an understanding is prerequisite to build resiliency and understand the uncertainty related to standard probable maximum flood analysis in the region.

The impacts of a warming climate on winter mid-latitude cyclones in the NARCCAP model suite

The North American Regional Climate Change Assessment Program (NARCCAP) represents the next step in investigating the behavior of weather phenomena in current and future climate. Specifically, variations in mid-latitude cyclone tracks attributable to a warming climate have potential socio-economic consequences via the redistribution of precipitation on regional spatial scales. This manuscript assesses the impacts of a warming climate on cyclone tracks in the NARCCAP model suite. Specifically, cyclones are generated from eight 33-year simulations for the twentieth Century (1968–2000) and eight 33-year simulations for the A2 greenhouse scenario (2068–2100). To provide comparison, cyclone tracks are also generated from the Climate Forecast System Reanalysis (CFSR) and ERA Interim (ERA_INT) reanalysis datasets from 1979 to 2016. The results support that the NARCCAP model suite is capable of producing a reasonable cyclone frequency and intensity climatology when compared with the reanalysis datasets. Comparison of the ensemble twentieth Century cyclone (20C) tracks with the ensemble A2 cyclone tracks demonstrate a zonally-oriented poleward shift in cyclone track frequency in response to a warming climate. Intensity differences were regionally oriented, with cyclones being more intense in the A2 relative to the twentieth Century scenarios west of the Appalachians, suggesting cyclones acquire greater latent heat from warmer western Atlantic/Gulf of Mexico moisture sources in A2. A sector analysis revealed a higher total frequency of cyclones in both reanalysis datasets relative to either NARCCAP scenario. For intense cyclones, the NARCCAP model simulations produced more frequent cyclones in the Great Lakes and East Coast sectors. In contrast, reanalysis produced a higher frequency of weak cyclones for all sectors except Atlantic Canada. In addition, NARCCAP simulations were found to be capable of reproducing the broadness of intensity distributions in the reanalysis datasets, likely due to the fine spatial gridding in the NARCCAP models. Sector analysis for frequency and intensity affirmed the ensemble results, with individual model simulations showed a reduction in frequency for all sectors except the Canadian Maritimes for A2 relative to 20C. For intensity, cyclones were once again overall more intense for the upper Midwest/Great Lakes sector for A2 relative to 20C. The results of this research demonstrates the ability for regional climate models to be used to assess changes in synoptic-scale phenomena in a warming climate. Future work will focus on assessing cyclone structure including changes in moisture transport, precipitation, and the low-level jet.

AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).

Incorporating Climate Change Projections into Risk Measures of Index-Based Insurance

We use regional climate model projections to quantify long-term projected changes to risk measures for an example set of temperature index–based insurance products in California. This region is a major agricultural producer for the United States and the world. The climate model projections are an ensemble of six regional climate model runs obtained from the North American Regional Climate Change Assessment Program. Hindcasts for the period of 1971–2000 are compared to historical observed temperature data for bias and variance corrections. Adjusted future model projections are used to estimate distributions of cooling degree days for 2041–2070, which are then used to estimate risk measures for index-based insurance products to demonstrate the scale of changes that climate models project through mid-century in actuarial terms. More broadly, this article provides an illustration of the use of climate data products to explore actuarial risks.

Evaluation of the Climate Extremes Index over the United States using 20th and mid‐21st century North American Regional Climate Change Assessment Program data

AbstractMuch of our current risk assessment, especially for extreme events and natural disasters, comes from the assumption that the likelihood of future extreme events can be predicted based on the past. However, as global temperatures rise, established climate ranges may no longer be applicable, as historic records for extremes such as heat waves and floods may no longer accurately predict the changing future climate. To assess extremes (present‐day and future) over the contiguous United States, we used NOAA's Climate Extremes Index (CEI), which evaluates extremes in maximum and minimum temperature, extreme one‐day precipitation, days without precipitation, and the Palmer Drought Severity Index (PDSI). The CEI is a spatially sensitive index that uses percentile‐based thresholds rather than absolute values to determine climate “extremeness” and is thus well‐suited to compare extreme climate across regions. We used regional climate model data from the North American Regional Climate Change Assessment Program (NARCCAP) to compare a late 20th century reference period to a mid‐21st century “business as usual” (SRES A2) greenhouse gas‐forcing scenario. Results show a universal increase in extreme hot temperatures across all models, with annual average maximum and minimum temperatures exceeding 90th percentile thresholds consistently across the continental United States. Results for precipitation indicators have greater spatial variability from model to model, but indicate an overall movement towards less frequent but more extreme precipitation days in the future. Due to this difference in response between temperature and precipitation, the mid‐21st century CEI is primarily an index of temperature extremes, with 90th percentile temperatures contributing disproportionately to the overall increase in climate extremeness. We also examine the efficacy of the PDSI in this context in comparison to other drought indices.

Extreme Precipitation Spatial Analog: In Search of an Alternative Approach for Future Extreme Precipitation in Urban Hydrological Studies

In this paper, extreme precipitation spatial analog is examined as an alternative method to adapt extreme precipitation projections for use in urban hydrological studies. The idea for this method is that real climate records from some cities can serve as “analogs” that behave like potential future precipitation for other locations at small spatio-temporal scales. Extreme precipitation frequency quantiles of a 3.16 km 2 catchment in the Chicago area, computed using simulations from North American Regional Climate Change Assessment Program (NARCCAP) Regional Climate Models (RCMs) with L-moment method, were compared to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 (NA14) quantiles at other cities. Variances in raw NARCCAP historical quantiles from different combinations of RCMs, General Circulation Models (GCMs), and remapping methods are much larger than those in NA14. The performance for NARCCAP quantiles tend to depend more on the RCMs than the GCMs, especially at durations less than 24-h. The uncertainties in bias-corrected future quantiles of NARCCAP are still large compared to those of NA14, and increase with rainfall duration. Results show that future 3-h and 30-day rainfall in Chicago will be similar to historical rainfall from Memphis, TN and Springfield, IL, respectively. This indicates that the spatial analog is potentially useful, but highlights the fact that the analogs may depend on the duration of the rainfall of interest.

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.

Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data

Heat waves and other extreme weather events have attracted a great deal of attention due to their socioeconomic impacts and relation to climate change. A heat wave is defined through a general loss function that captures its amplitude, temporal persistence, and spatial extent. The proposed statistical framework is at the nexus of extreme value theory (EVT) and functional data analysis (FDA) and enables computation of probabilities of yet unobserved rare events that are not seen in historical records. Data from the North American Regional Climate Change Assessment Program, which has produced computer model predictions of current and future temperatures across much of North America, are used. The approach allows for the computation of probabilities for heat waves of any pre-specified temporal duration, spatial extent, and overall magnitude. It can be applied to the computation of probabilities of other extreme weather events, including cold spells and droughts.

Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models

Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts 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.

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.