We compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere‐ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression‐based ERF in small forcing simulations.

Abstract. Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic OscillationArctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901-2010, roughly one-third (31±17 %) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol-cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and the economy in the Mediterranean region.

Extreme El Niño events severely disrupt global climate, causing pronounced socio-economic losses. A prevailing view is that extreme El Niño events, defined by total precipitation or convection in Niño3 area, would increase twofold in future. However, this projected change was drawn without correcting potential impacts of CMIP5 models’ common biases. Here, we find that models’ systematic biases in simulating the tropical climate change over the past can reduce the reliability of the projected change in the Pacific sea surface temperature (SST) and its-related extreme El Niño frequency. The projected Pacific SST change, after correcting impacts of 13 common biases, displays a La Niña-like rather than El Niño-like change. Consequently, the extreme El Niño frequency, which is highly linked to the zonal distribution of the Pacific SST change, would remain mostly unchanged under CMIP5 warming scenarios. This finding increases the confidence in coping with climate risks associated with global warming.

As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts great socio-economic impacts globally, especially on Asia, Africa, and Australia. While enormous efforts have been made since its discovery to improve both climate models and statistical methods for better prediction, current skills in IOD predictions are mostly limited up to three months ahead. Here, we challenge this long-standing problem using a multi-task deep learning model that we name MTL-NET. Hindcasts of the IOD events during the past four decades indicate that the MTL-NET can predict the IOD well up to 7-month ahead, outperforming most of world-class dynamical models used for comparison in this study. Moreover, the MTL-NET can help assess the importance of different predictors and correctly capture the nonlinear relationships between the IOD and predictors. Given its merits, the MTL-NET is demonstrated to be an efficient model for improved IOD prediction.

The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.

Abstract. Shortwave cloud radiative effects (SWCREs), defined as the difference of the shortwave radiative flux between all-sky and clear-sky conditions at the surface, have been reported to play an important role in influencing the Earth's energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols, and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe, and eastern China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from changes in relative humidity (RH) and, to a lesser extent, changes in cloud liquid water, circulation, dynamics, and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC because part of the radiation scattered by clouds under all-sky conditions will also be scattered by aerosols under clear-sky conditions. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 and 0.13 K per watt per square meter (W m−2) increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the contribution of SWCRE change to summer mean Tmax changes was 10 %–30 % under CO2 forcing and 30 %–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socioeconomic activities.

Abstract. The responses of river runoff to shifts of largescale climatic patterns are of increasing concerns to water resource planners and managers for long-term climate change adaptation. El Niño, as one of the most dominant modes of climate variability, is closely linked to hydrologic extremes such as floods and droughts that cause great loss of lives and properties. However, the different impacts of the two types of El Niño, i.e., central Pacific (CP-) and eastern Pacific (EP-)El Niño, on runoff across the conterminous US (CONUS) are not well understood. This study characterizes the impacts of the CP-and EP-El Niño on seasonal and annual runoff using observed streamflow data from 658 reference gaging stations and the NCAR-CCSM4 model. We found that surface runoff responds similarly to the two types of El Niño events in southeastern, central, southern, and western coastal regions, but differently in northeast (NE), Pacific northwest (PNW) and west north central (WNC) climatic zones. Specifically, EP-El Niño events tend to bring above-average runoff in NE, WNC, and PNW throughout the year while CP-El Niño events cause below-than normal runoff in the three regions. Similar findings were also found by analyzing NCAR-CCSM4 model outputs that captured both the CP-and EP-El Niño events, representing the best data set among CMIP5 models. The CCSM4 model simulates lower runoff values during CP-El Niño years than those in EP-El Niño over all of the three climatic regions (NE, PNW, and WNC) during 1950-1999. In the future (2050-2099), for both types of El Niño years, runoff is projected to increase over the NE and PNW regions, mainly due to increased precipitation (P ). In contrast, the increase of future evapotranspiration (ET) exceeds that of future P , leading to a projected decrease in runoff over the WNC region. In addition, model analysis indicates that all of the three regions (NE, PNW, and WNC) are projected to have lower runoff in CP-El Niño years than in EP-El Niño years. Our study suggests that the US water resources may be distributed more unevenly in space and time with more frequent and intense flood and drought events. The findings from this study have important implications to water resource management at regional scales. Information generated from this study may help water resource planners to anticipate the influence of two different types of El Niño events on droughts and floods across the CONUS.

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth’s radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project.