Abstract
Abstract. Understanding and quantifying land management impacts on local climate is important for distinguishing between the effects of land management and large-scale climate forcings. This study for the first time explicitly considers the radiative forcing resulting from realistic land management and offers new insights into the local land surface response to land management. Regression-based trend analysis is applied to observations and present-day ensemble simulations with the Community Earth System Model (CESM) version 1.2.2 to assess the impact of irrigation and conservation agriculture (CA) on warming trends using an approach that is less sensitive to temperature extremes. At the regional scale, an irrigation- and CA-induced acceleration of the annual mean near-surface air temperature (T2m) warming trends and the annual maximum daytime temperature (TXx) warming trends were evident. Estimation of the impact of irrigation and CA on the spatial average of the warming trends indicated that irrigation and CA have a pulse cooling effect on T2m and TXx, after which the warming trends increase at a greater rate than the control simulations. This differed at the local (subgrid) scale under irrigation where surface temperature cooling and the dampening of warming trends were both evident. As the local surface warming trends, in contrast to regional trends, do not account for atmospheric (water vapour) feedbacks, their dampening confirms the importance of atmospheric feedbacks (water vapour forcing) in explaining the enhanced regional trends. At the land surface, the positive radiative forcing signal arising from enhanced atmospheric water vapour is too weak to offset the local cooling from the irrigation-induced increase in the evaporative fraction. Our results underline that agricultural management has complex and non-negligible impacts on the local climate and highlight the need to evaluate the representation of land management in global climate models using climate models of higher resolution.
Highlights
According to observational and global climate model (GCM) data, temperatures associated with hot extremes have increased, consistent with global anthropogenic climate change (Sillmann and Croci-Maspoli, 2009; Donat et al, 2013a, b; Hartmann et al, 2013; Pendergrass and Hartmann, 2014; Fischer and Knutti, 2015)
For the IRR ensemble, T2m warming trends are overestimated by ∼ 0.001 K yr−1 across irrigated pixels, whereas over conservation agriculture (CA) pixels T2m warming trends are overestimated by ∼ 0.002–0.004 K yr−1 in both the CA and CTL ensemble
In this study the impact of a theoretical constant level of irrigation and CA on warming trends in global climate and climate extremes was assessed for the period of 1981–2010 using the Community Earth System Model
Summary
According to observational and global climate model (GCM) data, temperatures associated with hot extremes have increased, consistent with global anthropogenic climate change (Sillmann and Croci-Maspoli, 2009; Donat et al, 2013a, b; Hartmann et al, 2013; Pendergrass and Hartmann, 2014; Fischer and Knutti, 2015). Hot spots of accelerated warming in annual maximum daytime temperature (TXx) relative to local mean temperature (T2m) simulated by climate models from phase 5 of the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project (CMIP5) are spatially inconsistent with observations (Donat et al, 2017) This is the case over southeast China, South America, North America, and parts of Australia and Europe. Further analysis of the CMIP5 ensemble over central Europe by Vogel et al (2018) highlighted that several GCMs overestimate the observed negative correlation between summer precipitation and TXx, resulting in too strong future drying and associated TXx increases under RCP8.5 This underlines the importance of a correct representation of land–atmosphere coupling for simulating changes in temperature extremes at regional scales. These discrepancies between multiple GCMs and observations raise the questions as to whether (1) these model results can be used to reliably project changes in local temperature extremes; (2) the discrepancies remain if the rates are examined at which warming occurs over a time period which is less sensitive to outliers common in extreme temperature data than the absolute temperature difference between two time periods, as used in Donat et al (2017); and (3) the inclusion of more processes that represent land–atmosphere coupling would enhance model skill
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