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

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.

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The Influence of Climate Model Biases on Projections of Aridity and Drought

Journal of Climate ◽

10.1175/jcli-d-15-0439.1 ◽

2016 ◽

Vol 29 (4) ◽

pp. 1269-1285 ◽

Cited By ~ 23

Author(s):

Darren L. Ficklin ◽

John T. Abatzoglou ◽

Scott M. Robeson ◽

Anna Dufficy

Keyword(s):

United States ◽

Climate Models ◽

Climate Model ◽

Potential Evapotranspiration ◽

Global Climate ◽

Precipitation Amount ◽

Severity Index ◽

Global Climate Models ◽

Drought Severity ◽

Climate Conditions

Abstract Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

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

10.5194/gmd-2017-105 ◽

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.

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

Earth Interactions ◽

10.1175/2012ei000453.1 ◽

2012 ◽

Vol 16 (17) ◽

pp. 1-23 ◽

Cited By ~ 7

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.

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

10.21203/rs.3.rs-785460/v1 ◽

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0014.1 ◽

2020 ◽

Vol 21 (10) ◽

pp. 2221-2236 ◽

Cited By ~ 1

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.

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

10.5194/hess-2018-473 ◽

2018 ◽

Cited By ~ 1

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.

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

Journal of Climate ◽

10.1175/jcli-d-21-0080.1 ◽

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.

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The Contemporary US Energy System Overview Including A Comparison With China

Energy and Climate ◽

10.1093/oso/9780190490331.003.0007 ◽

2016 ◽

Author(s):

Michael B. McElroy

Keyword(s):

Climate Change ◽

United States ◽

Global Climate ◽

Energy System ◽

The United States ◽

Economic Benefits ◽

Personal Consumption ◽

Energy Economy ◽

Near Term ◽

Reduce Emissions

The discussion in chapter 2 addressed what might be described as a microview of the US energy economy— how we use energy as individuals, how we measure our personal consumption, and how we pay for it. We turn attention now to a more expansive perspective— the use of energy on a national scale, including a discussion of associated economic benefits and costs. We focus specifically on implications for emissions of the greenhouse gas CO2. If we are to take the issue of human- induced climate change seriously— and I do— we will be obliged to adjust our energy system markedly to reduce emissions of this gas, the most important agent for human- induced climate change. And we will need to do it sooner rather than later. This chapter will underscore the magnitude of the challenge we face if we are to successfully chart the course to a more sustainable climate- energy future. We turn later to strategies that might accelerate our progress toward this objective.We elected in this volume to focus on the present and potential future of the energy economy of the United States. It is important to recognize that the fate of the global climate system will depend not just on what happens in the United States but also to an increasing extent on what comes to pass in other large industrial economies. China surpassed the United States as the largest national emitter of CO2 in 2006. The United States and China together were responsible in 2012 for more than 42% of total global emissions. Add Russia, India, Japan, Germany, Canada, United Kingdom, South Korea, and Iran to the mix (the other members of the top 10 emitting countries ordered in terms of their relative contributions), and we can account for more than 60% of the global total. Given the importance of China to the global CO2 economy (more than 26% of the present global total and likely to increase significantly in the near term), I decided that it would be instructive to include here at least some discussion of the situation in China— to elaborate what the energy economies of China and the United States have in common, outlining at the same time the factors and challenges that set them apart.

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Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

Water ◽

10.3390/w11091771 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1771 ◽

Cited By ~ 7

Author(s):

Kun Jia ◽

Yunfeng Ruan ◽

Yanzhao Yang ◽

Chao Zhang

Keyword(s):

Tibetan Plateau ◽

Climate Models ◽

Global Climate ◽

Precipitation Change ◽

Multiple Criteria ◽

Global Climate Models ◽

The Tibetan Plateau ◽

Spatial Distributions ◽

Near Term ◽

Far Future

In this study, the performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) global climate models (GCMs) in simulating precipitation over the Tibetan Plateau (TP) was assessed using data from 1961 to 2005 by an improved score-based method, which adopts multiple criteria to achieve a comprehensive evaluation. The future precipitation change was also estimated based on the Delta method by selecting the submultiple model ensemble (SMME) in the near-term (2006–2050) and far future (2051–2095) periods under Representative Concentration Pathways (RCP) scenarios RCP4.5 and RCP8.5. The results showed that most GCMs can reasonably simulate the precipitation pattern of an annual cycle; however, all GCMs overestimated the precipitation over TP, especially in spring and summer. The GCMs generally provide good simulations of the temporal characteristics of precipitation, while they did not perform as well in reproducing its spatial distributions. Different assessment criteria lead to inconsistent results; however, the improved rank score method, which adopts multiple criteria, provided a robust assessment of GCMs performance. The future annual precipitation was projected to increase by ~6% in the near-term with respect to the period 1961–2005, whereas increases of 12.3% and 16.7% are expected in the far future under RCP4.5 and RCP8.5 scenarios, respectively. Similar spatial distributions of future precipitation changes can be seen in the near-term and far future periods under the two scenarios, and indicate that the most predominant increases occurred in the north of TP. The results of this study are expected to provide valuable information on climate change, and for water resources and agricultural management in TP.

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Projected increases in magnitude and socioeconomic exposure of global droughts in 1.5 and 2 °C warmer climates

Hydrology and Earth System Sciences ◽

10.5194/hess-24-451-2020 ◽

2020 ◽

Vol 24 (1) ◽

pp. 451-472 ◽

Cited By ~ 9

Author(s):

Lei Gu ◽

Jie Chen ◽

Jiabo Yin ◽

Sylvia C. Sullivan ◽

Hui-Min Wang ◽

...

Keyword(s):

Global Warming ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Drought Severity ◽

Copula Functions ◽

Infrastructure Failure ◽

Potential Risks ◽

Socioeconomic Consequences

Abstract. The Paris Agreement sets a long-term temperature goal to hold global warming to well below 2.0 ∘C and strives to limit it to 1.5 ∘C above preindustrial levels. Droughts with either intense severity or a long persistence could both lead to substantial impacts such as infrastructure failure and ecosystem vulnerability, and they are projected to occur more frequently and trigger intensified socioeconomic consequences with global warming. However, existing assessments targeting global droughts under 1.5 and 2.0 ∘C warming levels usually neglect the multifaceted nature of droughts and might underestimate potential risks. This study, within a bivariate framework, quantifies the change in global drought conditions and corresponding socioeconomic exposures for additional 1.5 and 2.0 ∘C warming trajectories. The drought characteristics are identified using the Standardized Precipitation Evapotranspiration Index (SPEI) combined with the run theory, with the climate scenarios projected by 13 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under three representative concentration pathways (RCP 2.6, RCP4.5 and RCP8.5). The copula functions and the most likely realization are incorporated to model the joint distribution of drought severity and duration, and changes in the bivariate return period with global warming are evaluated. Finally, the drought exposures of populations and regional gross domestic product (GDP) under different shared socioeconomic pathways (SSPs) are investigated globally. The results show that within the bivariate framework, the historical 50-year droughts may double across 58 % of global landmasses in a 1.5 ∘C warmer world, while when the warming climbs up to 2.0 ∘C, an additional 9 % of world landmasses would be exposed to such catastrophic drought deteriorations. More than 75 (73) countries' populations (GDP) will be completely affected by increasing drought risks under the 1.5 ∘C warming, while an extra 0.5 ∘C warming will further lead to an additional 17 countries suffering from a nearly unbearable situation. Our results demonstrate that limiting global warming to 1.5 ∘C, compared with 2 ∘C warming, can perceptibly mitigate the drought impacts over major regions of the world.

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