Probabilistic projections of agro-climate indices in North America

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961-2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.

Original languageEnglish (US)
Article numberD08115
JournalJournal of Geophysical Research Atmospheres
Volume117
Issue number8
DOIs
StatePublished - Jan 1 2012

Fingerprint

climate
projection
twenty first century
climate change
heat stress
Climate change
calibration
uncertainty
crop damage
General Circulation Models
bootstrapping
Calibration
climate models
Climate models
heat
frost
agricultural production
crops
general circulation model
climate modeling

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

@article{0ebf9f81fd5a4d22b908e3ea96c1e355,
title = "Probabilistic projections of agro-climate indices in North America",
abstract = "We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961-2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.",
author = "Adam Terando and Klaus Keller and Easterling, {William E.}",
year = "2012",
month = "1",
day = "1",
doi = "10.1029/2012JD017436",
language = "English (US)",
volume = "117",
journal = "Journal of Geophysical Research: Atmospheres",
issn = "2169-897X",
number = "8",

}

Probabilistic projections of agro-climate indices in North America. / Terando, Adam; Keller, Klaus; Easterling, William E.

In: Journal of Geophysical Research Atmospheres, Vol. 117, No. 8, D08115, 01.01.2012.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Probabilistic projections of agro-climate indices in North America

AU - Terando, Adam

AU - Keller, Klaus

AU - Easterling, William E.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961-2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.

AB - We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961-2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.

UR - http://www.scopus.com/inward/record.url?scp=84861403618&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861403618&partnerID=8YFLogxK

U2 - 10.1029/2012JD017436

DO - 10.1029/2012JD017436

M3 - Article

AN - SCOPUS:84861403618

VL - 117

JO - Journal of Geophysical Research: Atmospheres

JF - Journal of Geophysical Research: Atmospheres

SN - 2169-897X

IS - 8

M1 - D08115

ER -