Origin, importance, and predictive limits of internal climate variability

Abstract

Adaptation to climate change has now become a necessity for many regions. Yet, adaptation planning at regional scales over the next few decades is challenging given the contingencies originating from a combination of different sources of climate projection uncertainty, chief among them internal variability. Here, we review the causes and consequences of internal climate variability, how it can be quantified and accounted for in uncertainty assessments, and what research questions remain most pertinent to better understand its predictive limits and consequences for science and society. This perspective argues for putting internal variability into the spotlight of climate adaptation science and intensifying collaborations between the climate modeling and application communities.

The Choice of Baseline Period Influences the Assessments of the Outcomes of Stratospheric Aerosol Injection

“…In terms of the specific regional changes in precipitation patterns, we note that they are often challenging to diagnose with confidence, considering the importance of internal climate variability (Deser et al., 2012; Lehner & Deser, 2023); so while here we have explored their link with large scale changes in circulation, some caution is warranted in the attribution considering the limited size of the ensembles under analyses.…”

Observed Changes in Interannual Precipitation Variability in the United States

“…Indeed, attribution with observations alone ranges from challenging to impossible (NASEM, 2016). Observed records undersample the full distribution of potential underlying climatic states and may contain statistically significant but anthropogenically unforced trends (Lehner & Deser, 2023). To avoid such pitfalls and increase confidence, attribution analyses using single or even multi‐model initial condition perturbation ensembles are recommended (Deser et al., 2020; Diffenbaugh et al., 2017).…”

Understanding the Contributions of Paleo‐Informed Natural Variability and Climate Changes to Hydroclimate Extremes in the San Joaquin Valley of California

“…Lehner et al (2020) shows that scenario and model uncertainty are the dominant drivers of global decadal mean annual temperature and precipitation, but that natural variability dominates projections of 10.1029/2023EF003909 3 of 29 regional temperatures (in Southern Europe) and precipitation (in the U.S. Pacific Northwest and Sahel region), particularly at shorter time scales (i.e., those critical to informing near-term management and infrastructure investment). Lehner and Deser (2023) similarly demonstrates how natural variability becomes the increasingly dominant driver of variability in winter mean air temperature projections at smaller spatial scales. Fewer studies have explicitly considered the role of natural climate variability when partitioning variance in projections of hydrologic and water systems variables (Cai et al, 2021;I.-W. Jung et al, 2011;Kay et al, 2009;Schlef et al, 2018;Vidal et al, 2015;Whateley & Brown, 2016).…”

“…A key contribution of this study is a method to utilize 600 years of paleo-informed reconstructions of annual weather regimes (WRs; Gupta et al, 2023a) to force a weather-regime based daily-scale stochastic weather generator (Najibi et al, 2021;Steinschneider et al, 2019), which we utilize to force hydrologic models of five watersheds in the San Joaquin River basin. This novel component of the framework allows for the generation of regional precipitation and temperature that represents the substantial natural atmospheric variability that characterizes the San Joaquin region, addressing the need to integrate such variability into data sets used to inform robust water resources planning and management (Lehner & Deser, 2023). To assess the compound effect of climate change, we create temperature series that reflect projected scenarios of warming and precipitation series that have been scaled to reflect thermodynamically driven shifts in the distribution of daily precipitation.…”

“…A growing body of work has sought to partition the relative effects of climate change and natural variability, with a focus on climate variables and in the context of multi-model ensemble simulations (Hawkins & Sutton, 2009;Knutti et al, 2017;Lehner & Deser, 2023;Lehner et al, 2020;Rowell, 2012;Yip et al, 2011). These studies primarily attribute variability in projected global and regional temperature and precipitation to climate change scenario uncertainty, global climate change model (GCM) uncertainty, and natural variability.…”