Divergent responses of maize yield to precipitation in the United States

How maize yield responds to precipitation variability in space and time over broader scales is largely unknown compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. Here, we develop a method to quantify the spatially explicit precipitation response of maize yield using statistical data and crop models in the contiguous United States. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39 %), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ~90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change.

Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

Radiative cooling by clouds affects the precipitation - temperature scaling derived from observations

<p>One direct effect of climate warming on hydrology is the increase in moisture holding capacity of atmosphere at the rate of 7%/°C as suggested by the Clausius Clapeyron equation. Extreme precipitation largely depends on the amount of precipitable water in the atmospheric column and is thus expected to scale with temperature at the same rate. Observations, however, show significant variability in precipitation - temperature scaling rates, with negative scaling dominating in the tropical regions. These scaling relationships assume a one way causality, i.e. temperature is independent of precipitation. However, we show here that temperatures strongly co-vary with precipitation through the effect that clouds have on surface radiation. The presence of clouds associated with precipitation events result in lower solar isolation at the surface, further leading to reduced temperatures. This induces a two-way causality and thus temperature is no longer independent of precipitation. To remove this cooling effect of clouds, we used a surface energy balance model with a thermodynamic constraint to derive clear sky temperatures during precipitation events. We then show using observations from India, that extreme precipitation scaled with clear sky temperatures shows an increase consistent with the CC rate. On contrary, the negative scaling obtained using observed temperatures misrepresent the precipitation response to warming as a result of the co-variation with the cloud radiative effect. Our findings reveal that scaling relationships not only show how precipitation changes with temperature but also how atmospheric conditions associated with precipitation affect temperature. Thus, this covariation needs to be taken into account when using observations to derive scaling relationships that are then used to infer the extreme precipitation response to climate change.</p>

Explicit versus Parameterized Convection in Response to the Atlantic Meridional Mode

This study investigates whether the representation of explicit and parameterized convection influences the response to the Atlantic meridional mode (AMM). The main focus is on the precipitation response to the AMM-SST pattern, but possible implications for the atmospheric feedback on SST are also examined by considering differences in the circulation response between explicit and parameterized convection. On the basis of analysis from observations, SST composites are built to represent the positive and negative AMM. These SST patterns, in addition to the March–May climatology, are prescribed to the atmospheric ICON model. High-resolution simulations with explicit convection (E-CON) and coarse-resolution simulations with parameterized convection (P-CON) are used over a nested tropical Atlantic Ocean domain and a global domain, respectively. Our results show that a meridional shift of about 1° in the precipitation climatology explains most of the response to the AMM-SST pattern in simulations both with explicit convection and with parameterized convection. Our results also indicate a linearity in the precipitation response to the positive and negative AMM in E-CON, in contrast to P-CON. Further analysis of the atmospheric response to the AMM reveals that anomalies in the wind-driven enthalpy fluxes are generally stronger in E-CON than in P-CON. This result suggests that SST anomalies would be amplified more strongly in coupled simulations using an explicit representation of convection.

Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

Sensitivity of modeled Indian monsoon to Chinese and Indian aerosol emissions

The South Asian summer monsoon supplies over 80 % of India's precipitation. Industrialization over the past few decades has resulted in severe aerosol pollution in India. Understanding monsoonal sensitivity to aerosol emissions in general circulation models (GCMs) could improve predictability of observed future precipitation changes. The aims here are (1) to assess the role of aerosols in India's monsoon precipitation and (2) to determine the roles of local and regional emissions. For (1), we study the Precipitation Driver Response Model Intercomparison Project experiments. We find that the precipitation response to changes in black carbon is highly uncertain with a large intermodel spread due in part to model differences in simulating changes in cloud vertical profiles. Effects from sulfate are clearer; increased sulfate reduces Indian precipitation, a consistency through all of the models studied here. For (2), we study bespoke simulations, with reduced Chinese and/or Indian emissions in three GCMs. A significant increase in precipitation (up to ∼20 %) is found only when both countries' sulfur emissions are regulated, which has been driven in large part by dynamic shifts in the location of convective regions in India. These changes have the potential to restore a portion of the precipitation losses induced by sulfate forcing over the last few decades.