scholarly journals A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality Application to the United States Historical Record

2021 ◽  
Author(s):  
Mark Risser ◽  
William Collins ◽  
Michael Wehner ◽  
Travis O'Brien ◽  
Christopher Paciorek ◽  
...  
Keyword(s):  
United States ◽  
Climate Models ◽  
Water Cycle ◽  
Global Climate ◽  
Internal Variability ◽  
The United States ◽  
Historical Record ◽  
Global Climate Models ◽  
Detection And Attribution ◽  
Observational Uncertainty

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. We use output from global climate models in a perfect-data sense to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day). In doing so, we turn an apparent limitation into an opportunity by using the diversity of responses to short-lived climate forcers across the CMIP6 multi-model ensemble to ensure our D&A is insensitive to structural uncertainty. Our framework is developed using a Pearl-causal perspective, but forthcoming research now underway will apply the framework to in situ measurements using a Granger-causal perspective. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

scholarly journals A prognostic pollen emissions model for climate models (PECM1.0)

2017 ◽  
Author(s):  
Matthew C. Wozniak ◽  
Allison Steiner
Keyword(s):  
United States ◽  
Land Cover ◽  
Climate Models ◽  
Climate Model ◽  
Vegetation Type ◽  
Global Climate ◽  
The United States ◽  
Global Climate Models ◽  
Pollen Counts ◽  
Surface Pollen

Abstract. We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

scholarly journals Simulating Hydrological Drought Properties at Different Spatial Units in the United States Based on Wavelet–Bayesian Regression Approach

2012 ◽  
Vol 16 (17) ◽  
pp. 1-23 ◽  
Author(s):  
Ashok K. Mishra ◽  
Vijay P. Singh
Keyword(s):  
United States ◽  
Regression Model ◽  
Climate Models ◽  
Global Climate ◽  
The United States ◽  
Hydrological Drought ◽  
Bayesian Regression ◽  
Global Climate Models ◽  
Meteorological Variables ◽  
The Relationship

Abstract Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals Climate Model Assessment of Changes in Winter–Spring Streamflow Timing over North America

2018 ◽  
Vol 31 (14) ◽  
pp. 5581-5593 ◽  
Author(s):  
Jonghun Kam ◽  
Thomas R. Knutson ◽  
P. C. D. Milly
Keyword(s):  
United States ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Influence ◽  
Model Assessment ◽  
The North ◽  
Streamflow Timing

Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Changes in Future Flash Flood–Producing Storms in the United States

2020 ◽  
Vol 21 (10) ◽  
pp. 2221-2236 ◽  
Author(s):  
Erin Dougherty ◽  
Kristen L. Rasmussen
Keyword(s):  
United States ◽  
Climate Models ◽  
Flash Flood ◽  
Global Climate ◽  
The United States ◽  
Flash Floods ◽  
Global Climate Models ◽  
Future Climate ◽  
South Central ◽  
Rain Rates

AbstractFlash floods are high-impact events that can result in massive destruction, such as the May 2010 flash floods in the south-central United States that resulted in over $2 billion of damage. While floods in the current climate are already destructive, future flood risk is projected to increase based on work using global climate models. However, global climate models struggle to resolve precipitation structure, intensity, and duration, which motivated the use of convection-permitting climate models that more accurately depict these precipitation processes on a regional scale due to explicit representation of convection. These high-resolution convection-permitting simulations have been used to examine future changes to rainfall, but not explicitly floods. This study aims to fill this gap by examining future changes to rainfall characteristics and runoff in flash flood–producing storms over the United States using convection-permitting models under a pseudo–global warming framework. Flash flood accumulated rainfall increases on average by 21% over the United States in a future climate. Storm-generated runoff increases by 50% on average, suggesting increased runoff efficiency in future flash flood–producing storms. In addition to changes in nonmeteorological factors, which were not explored in this study, increased future runoff is possible due to the 7.5% K−1 increase in future hourly maximum rain rates. Though this median change in rain rates is consistent with Clausius–Clapeyron theory, some storms exhibit increased future rain rates well above this, likely associated with storm dynamics. Overall, results suggest that U.S. cities might need to prepare for more intense flash flood–producing storms in a future climate.

scholarly journals Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 136
Author(s):  
Susan M. Kotikot ◽  
Olufemi A. Omitaomu
Keyword(s):  
United States ◽  
Spatial Scale ◽  
Climate Models ◽  
Global Climate ◽  
Severity Index ◽  
The United States ◽  
Global Climate Models ◽  
Drought Severity ◽  
Near Term ◽  
Local Variability

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

scholarly journals Climate change will increase potential hydropower production in six Arctic Council member countries based on probabilistic hydrological projections

2018 ◽  
Author(s):  
Elena Shevnina ◽  
Karoliina Pilli-Sihvola ◽  
Riina Haavisto ◽  
Timo Vihma ◽  
Andrey Silaev
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
The United States ◽  
Annual Runoff ◽  
Global Climate Models ◽  
Exceedance Probability ◽  
Arctic Council ◽  
Pearson Type ◽  
Hydropower Production ◽  
Runoff Rate

Abstract. Potential hydropower production for 2020–2050 is calculated for 173 catchments located over the territories of Finland, Sweden, Norway, the Russian Federation, Canada and the United States. The results are based on hydrological river runoff projections assessed together with their exceedance probabilities. The annual runoff rate of particular exceedance probability was modelled with the Pearson type 3 distribution from three parameters (mean values, coefficient of variation and coefficient of skewness) simulated by the probabilistic hydrological MARcov Chain System (MARCS) model. The probabilistic projections of annual runoff were simulated from outputs of four global climate models under three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). The future potential hydropower production was evaluated based on annual runoff of low and high exceedance probabilities, and then aggregated at a country level. Under forcing from climate models that project a large increase in precipitation (CaEMS2 and MPI-EMS-LM), the expected potential hydropower production in the six countries increased by 14.0 to 18.0 % according to the projected values of annual runoff rate on exceedance probabilities of 10 and 90 %. This increase in water resources allows for 10–15 % more hydropower energy generation by rivers located in Russia, Finland, Norway, and Sweden. For the USA and Canada, the potential hydropower production is projected to increases by 4.0–9.0 %. Under forcing from climate models that project a smaller increase in precipitation (HadGEM2-ES and INMCM4), the increase of potential hydropower production by 2050 was predicted to be 2.1–8.4 % over the six countries considered.

Drivers of Twenty-First Century U.S. Winter Precipitation Trends in CMIP6 Models: A Storyline-Based Approach

2021 ◽  
pp. 1-48
Author(s):  
Daniel F. Schmidt ◽  
Kevin M. Grise
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
The United States ◽  
Winter Precipitation ◽  
Global Climate Models ◽  
First Century ◽  
Precipitation Trend ◽  
Twenty First Century ◽  
Precipitation Trends

AbstractClimate change during the twenty-first century has the potential to substantially alter geographic patterns of precipitation. However, regional precipitation changes can be very difficult to project, and in some regions, global climate models do not even agree on the sign of the precipitation trend. Since some of this uncertainty is due to internal variability rather than model bias, models cannot be used to narrow the possibilities to a single outcome, but they can usefully quantify the range of plausible outcomes and identify the combination of dynamical drivers that would be likely to produce each.This study uses a storylines approach—a type of regression-based analysis—to identify some of the key dynamical drivers that explain the variance in 21st century U.S. winter precipitation trends across CMIP6 models under the SSP3-7.0 emissions scenario. This analysis shows that the spread in precipitation trends is not primarily driven by differences in modeled climate sensitivity. Key drivers include global-mean surface temperature, but also tropical upper-troposphere temperature, the El Niño-Southern Oscillation (ENSO), the Pacific-North America (PNA) pattern, and the East Pacific (EP) dipole (a dipole pattern in geopotential heights over North America’s Pacific coast). Combinations of these drivers can reinforce or cancel to produce various high- or low-impact scenarios for winter precipitation trends in various regions of the United States. For example, the most extreme winter precipitation trends in the southwestern U.S. result from opposite trends in ENSO and EP, whereas the wettest winter precipitation trends in the midwestern U.S. result from a combination of strong global warming and a negative PNA trend.

scholarly journals A prognostic pollen emissions model for climate models (PECM1.0)

2017 ◽  
Vol 10 (11) ◽  
pp. 4105-4127 ◽  
Author(s):  
Matthew C. Wozniak ◽  
Allison L. Steiner
Keyword(s):  
United States ◽  
Land Cover ◽  
Climate Models ◽  
Climate Model ◽  
Vegetation Type ◽  
The United States ◽  
Global Climate Models ◽  
Pollen Counts ◽  
Surface Pollen ◽  
Pollen Concentrations

Abstract. We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

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