Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming.

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming.

Quantification of human contribution to soil moisture-based terrestrial aridity.

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N-65°N, during 1971-2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0-10 cm SSI during August-November, and 0-100 cm during September-April.

Quantifying stochastic uncertainty in detection time of human-caused climate signals.

Large initial condition ensembles of a climate model simulation provide many different realizations of internal variability noise superimposed on an externally forced signal. They have been used to estimate signal emergence time at individual grid points, but are rarely employed to identify global fingerprints of human influence. Here we analyze 50- and 40-member ensembles performed with 2 climate models; each was run with combined human and natural forcings.

Twentieth-century hydroclimate changes consistent with human influence.

Although anthropogenic climate change is expected to have caused large shifts in temperature and rainfall, the detection of human influence on global drought has been complicated by large internal variability and the brevity of observational records. Here we address these challenges using reconstructions of the Palmer drought severity index obtained with data from tree rings that span the past millennium. We show that three distinct periods are identifiable in climate models, observations and reconstructions during the twentieth century.

Human influence on the seasonal cycle of tropospheric temperature.

We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic "fingerprint" predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics.

Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity.

The 2011-2016 Californian drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño-Southern Oscillation (ENSO) returns. In the 21 century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the aridity changes driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming-induced increased evaporative demand.

Future loss of Arctic sea-ice cover could drive a substantial decrease in California's rainfall.

From 2012 to 2016, California experienced one of the worst droughts since the start of observational records. As in previous dry periods, precipitation-inducing winter storms were steered away from California by a persistent atmospheric ridging system in the North Pacific. Here we identify a new link between Arctic sea-ice loss and the North Pacific geopotential ridge development.

Tropospheric Warming Over The Past Two Decades.

Satellite temperature measurements do not support the recent claim of a "leveling off of warming" over the past two decades. Tropospheric warming trends over recent 20-year periods are always significantly larger (at the 10% level or better) than model estimates of 20-year trends arising from natural internal variability. Over the full 38-year period of the satellite record, the separation between observed warming and internal variability estimates is even clearer.

Identifying external influences on global precipitation.

Changes in global (ocean and land) precipitation are among the most important and least well-understood consequences of climate change. Increasing greenhouse gas concentrations are thought to affect the zonal-mean distribution of precipitation through two basic mechanisms. First, increasing temperatures will lead to an intensification of the hydrological cycle ("thermodynamic" changes).

Human and natural influences on the changing thermal structure of the atmosphere.

Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols.

Human-induced changes in the hydrology of the western United States.

Observations have shown that the hydrological cycle of the western United States changed significantly over the last half of the 20th century. We present a regional, multivariable climate change detection and attribution study, using a high-resolution hydrologic model forced by global climate models, focusing on the changes that have already affected this primarily arid region with a large and growing population. The results show that up to 60% of the climate-related trends of river flow, winter air temperature, and snow pack between 1950 and 1999 are human-induced.

Empirical evidence for a recent slowdown in irrigation-induced cooling.

Understanding the influence of past land use changes on climate is needed to improve regional projections of future climate change and inform debates about the tradeoffs associated with land use decisions. The effects of rapid expansion of irrigated area in the 20th century has remained unclear relative to other land use changes, such as urbanization, that affected a similar total land area. Using spatial and temporal variations in temperature and irrigation extent observed in California, we show that irrigation expansion has had a large cooling effect on summertime average daily daytime temperatures (-0.