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Angeline G. Pendergrass and Clara Deser

Abstract

Precipitation is often quantified by the amount that falls over a given period of time but not the rate at which most of it falls or the rate associated with the most frequent events. Here, three metrics are introduced to distill salient characteristics of typical daily precipitation accumulation based on the full distribution of rainfall: rain amount peak (the rain rate at which the most rain falls), rain frequency peak (the most frequent nonzero rain rate), and rain amount width (a measure of the variability of typical precipitation accumulation). These metrics are applied to two observational datasets to describe the climatology of typical daily precipitation accumulation: GPCP 1° daily (October 1996–October 2015) and TMPA 3B42 (January 1998–October 2015). Results show that the rain frequency peak is similar to total rainfall in terms of geographical pattern and seasonal cycle and varies inversely with rain amount width. In contrast, the rain amount peak varies distinctly, reaching maxima on the outer edges of the regions of high total precipitation, and with less seasonal variation. Despite that GPCP and TMPA 3B42 are both merged satellite–gauge precipitation products, they show substantial differences. In particular, the rain amount peak and rain amount width are uniformly greater in TMPA 3B42 compared to GPCP, and there are large discrepancies in their rain frequency distributions (peak and width). Issues relating to model evaluation are highlighted using CESM1 as an illustrative example and underscore the need for observational datasets incorporating measurements of light rain.

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Angeline G. Pendergrass and Edwin P. Gerber

Abstract

As the planet warms, climate models predict that rain will become heavier but less frequent and that the circulation will weaken. Here, two heuristic models relating moisture, vertical velocity, and rainfall distributions are developed—one in which the distribution of vertical velocity is prescribed and another in which it is predicted. These models are used to explore the response to warming and moistening as well as changes in circulation, atmospheric energy budget, and stability. Some key assumptions of the models include that relative humidity is fixed within and between climate states and that stability is constant within each climate state. The first model shows that an increase in skewness of the vertical velocity distribution is crucial for capturing salient characteristics of the changing distribution of rain, including the muted rate of mean precipitation increase relative to extremes and the decrease in the total number or area of rain events. The second model suggests that this increase in the skewness of the vertical velocity arises from the asymmetric impact of latent heating on vertical motion.

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Angeline G. Pendergrass and Hugh E. Willoughby

Abstract

The Sawyer–Eliassen Equation (SEQ) is here rederived in height coordinates such that the sea surface is also a coordinate surface. Compared with the conventional derivation in mass field coordinates, this formulation adds some complexity, but arguably less than is inherent in terrain-following coordinates or interpolation to the lower physical boundary. Spatial variations of static stability change the vertical structure of the mass flow streamfunction. This effect leads to significant changes in both secondary-circulation structure and intensification of the primary circulation. The SEQ is solved on a piecewise continuous, balanced mean vortex where the shapes of the wind profiles inside and outside the eye and the tilt of the specified heat source can be adjusted independently. A series of sensitivity studies shows that the efficiency with which imposed heating intensifies the vortex is most sensitive to intensity itself as measured by maximum wind and to vortex size as measured by radius of maximum wind. Vortex shape and forcing tilt have impacts 20%–25% as great as intensity and size, suggesting that the aspects of tropical cyclones that predispose them to rapid intensification are environmental or thermodynamic rather than kinematic.

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Angeline G. Pendergrass and Dennis L. Hartmann

Abstract

Changes in the frequency and intensity of rainfall are an important potential impact of climate change. Two modes of change, a shift and an increase, are applied to simulations of global warming with models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The response to CO2 doubling in the multimodel mean of CMIP5 daily rainfall is characterized by an increase of 1% K−1 at all rain rates and a shift to higher rain rates of 3.3% K−1. In addition to these increase and shift modes of change, some models also show a substantial increase in rainfall at the highest rain rates called the extreme mode of response to warming. In some models, this extreme mode can be shown to be associated with increases in grid-scale condensation or gridpoint storms.

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Angeline G. Pendergrass and Dennis L. Hartmann

Abstract

Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) robustly predict that the rate of increase in global-mean precipitation with global-mean surface temperature increase is much less than the rate of increase of water vapor. The goal of this paper is to explain in detail the mechanisms by which precipitation increase is constrained by radiative cooling. Changes in clear-sky atmospheric radiative cooling resulting from changes in temperature and humidity in global warming simulations are in good agreement with the multimodel, global-mean precipitation increase projected by GCMs (~1.1 W m−2 K−1).

In an atmosphere with fixed specific humidity, radiative cooling from the top of the atmosphere (TOA) increases in response to a uniform temperature increase of the surface and atmosphere, while atmospheric cooling by exchange with the surface decreases because the upward emission of longwave radiation from the surface increases more than the downward longwave radiation from the atmosphere. When a fixed relative humidity (RH) assumption is made, however, uniform warming causes a much smaller increase of cooling at the TOA, and the surface contribution reverses to an increase in net cooling rate due to increased downward emission from water vapor. Sensitivity of precipitation changes to lapse rate changes is modest when RH is fixed. Carbon dioxide reduces TOA emission with only weak effects on surface fluxes, and thus suppresses precipitation. The net atmospheric cooling response and thereby the precipitation response to CO2-induced warming at fixed RH are mostly contributed by changes in surface fluxes. The role of clouds is discussed.

Intermodel spread in the rate of precipitation increase across the CMIP5 simulations is attributed to differences in the atmospheric cooling.

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Angeline G. Pendergrass and Dennis L. Hartmann

Abstract

The frequency and intensity of rainfall determine its character and may change with climate. A methodology for characterizing the frequency and amount of rainfall as functions of the rain rate is developed. Two modes of response are defined, one in which the distribution of rainfall increases in equal fraction at all rain rates and one in which the rainfall shifts to higher or lower rain rates without a change in mean rainfall.

This description of change is applied to the tropical distribution of daily rainfall over ENSO phases in models and observations. The description fits observations and most models well, although some models also have an extreme mode in which the frequency increases at extremely high rain rates. The multimodel mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) agrees with observations in showing a very large shift of 14%–15% K−1, indicating large increases in the heaviest rain rates associated with El Niño. Models with an extreme mode response to global warming do not agree as well with observations of the rainfall response to El Niño.

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Ryan J. Kramer, Brian J. Soden, and Angeline G. Pendergrass

Abstract

We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

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Angeline G. Pendergrass, Gregory J. Hakim, David S. Battisti, and Gerard Roe

Abstract

A central issue for understanding past climates involves the use of sparse time-integrated data to recover the physical properties of the coupled climate system. This issue is explored in a simple model of the midlatitude climate system that has attributes consistent with the observed climate. A quasigeostrophic (QG) model thermally coupled to a slab ocean is used to approximate midlatitude coupled variability, and a variant of the ensemble Kalman filter is used to assimilate time-averaged observations. The dependence of reconstruction skill on coupling and thermal inertia is explored. Results from this model are compared with those for an even simpler two-variable linear stochastic model of midlatitude air–sea interaction, for which the assimilation problem can be solved semianalytically.

Results for the QG model show that skill decreases as the length of time over which observations are averaged increases in both the atmosphere and ocean when normalized against the time-averaged climatological variance. Skill in the ocean increases with slab depth, as expected from thermal inertia arguments, but skill in the atmosphere decreases. An explanation of this counterintuitive result derives from an analytical expression for the forecast error covariance in the two-variable stochastic model, which shows that the ratio of noise to total error increases with slab ocean depth. Essentially, noise becomes trapped in the atmosphere by a thermally stiffer ocean, which dominates the decrease in initial condition error owing to improved skill in the ocean.

Increasing coupling strength in the QG model yields higher skill in the atmosphere and lower skill in the ocean, as the atmosphere accesses the longer ocean memory and the ocean accesses more atmospheric high-frequency “noise.” The two-variable stochastic model fails to capture this effect, showing decreasing skill in both the atmosphere and ocean for increased coupling strength, due to an increase in the ratio of noise to the forecast error variance. Implications for the potential for data assimilation to improve climate reconstructions are discussed.

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Sebastian Sippel, Nicolai Meinshausen, Anna Merrifield, Flavio Lehner, Angeline G. Pendergrass, Erich Fischer, and Reto Knutti

Abstract

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

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Vineel Yettella, Jeffrey B. Weiss, Jennifer E. Kay, and Angeline G. Pendergrass

Abstract

Climate variability and its response to increasing greenhouse gases are important considerations for impacts and adaptation. Modeling studies commonly assess projected changes in variability in terms of changes in the variance of climate variables. Despite the distant and impactful covariations that climate variables can exhibit, the covariance response has received much less attention. Here, a novel ensemble framework is developed that facilitates a unified assessment of the response of the regional variances and covariances of a climate variable to imposed external forcings and their time of emergence from an unforced climate state.

Illustrating the framework, the response of variability and covariability of land and ocean temperatures is assessed in the Community Earth System Model Large Ensemble under historical and RCP8.5 forcing. The results reveal that land temperature variance emerges from its preindustrial state in the 1950s and, by the end of the twenty-first century, grows to 1.5 times its preindustrial level. Demonstrating the importance of covariances for variability projections, the covariance between land and ocean temperature is considerably enhanced by 2100, reaching 1.4 times its preindustrial estimate. The framework is also applied to assess changes in monthly temperature variability associated with the Arctic region and the Northern Hemisphere midlatitudes. Consistent with previous studies and coinciding with sea ice loss, Arctic temperature variance decreases in most months, emerging from its preindustrial state in the late twentieth century. Overall, these results demonstrate the utility of the framework in enabling a comprehensive assessment of variability and its response to external climate forcings.

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