Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Abstract

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

Numbers of Terns Breeding Inland in California: Trends or Tribulations?

“…To date, evidence for anthropogenic influence on rainfall and atmospheric circulation patterns during the current California drought is equivocal (Funk et al 2014, Seager et al 2014, Swain et al 2014. Likewise, expectations of future precipitation patterns in California remain unclear given differences in climate models and high natural variability (e.g., Deser et al 2012, Pierce et al 2012). Such natural fluctuations will intensify or mute the magnitude of effects caused by human actions.…”

Vulnerability of California specialty crops to projected mid-century temperature changes

“…Modeling studies have produced mixed results (Lobell et al 2007;Deschenes and Kolstad 2011;Lee et al 2011;Lee and Sumner 2015), with some crops such as melons and tomatoes projected to expand their area of cultivation. Predicting how changes in precipitation and/or water availability may affect specialty crop production in California is even more complex than predicting temperature effects, not only due to the inherent difficulty in modeling precipitation, snowpack, and runoff at a regional scale (Pierce et al 2013) but because available water for California crops depends greatly on infrastructure and policy. The large majority of specialty crop area in California is irrigated, and approximately half of this irrigation is supplied by groundwater (Cooley et al 2015).…”

Interrogating empirical-statistical downscaling

“…Mahmood and Babel 2012;Stoner et al 2012), or on applications using one or more ESD methods to providing information to end users (e.g. Pierce et al 2012;Jeong et al 2012). In terms of the value of downscaling, some are pessimistic (e.g.…”